Detecting Intrusions and anomalies is becoming much more challenging with new attacks popping out over a period of time. Achieving better accuracies by applying benchmark classifier algorithms used for identifying intrusions and anomalies have several hidden data mining challenges. Although neglected by many research findings, one of the most important and biggest challenges is the similarity or membership computation. Another challenge that cannot be simply neglected is the number of features that attributes to dimensionality. This research aims to come up (...) with a new membership function to carry similarity computation that can be helpful for addressing feature dimensionality issues. In principle, this work is aimed at introducing a novel membership function that can help to achieve better classification accuracies and eventually lead to better intrusion and anomaly detection. Experiments are performed on KDD dataset with 41 attributes and also KDD dataset with 19 attributes. Recent approaches CANN and CLAPP have showed new approaches for intrusion detection. The proposed classifier is named as UTTAMA. UTTAMA performed better to both CANN and CLAPP approaches w.r.t overall classifier accuracy. Another promising outcome achieved using UTTAMA is the U2R and R2L attack accuracies. The importance of proposed approach is that the accuracy achieved using proposed approach outperforms CLAPP, CANN, SVM, KNN and other existing classifiers. (shrink)

Cloud computing paradigm is growing rapidly, and it allows users to get services via the Internet as pay-per-use and it is convenient for developing, deploying, and accessing mobile applications. Currently, security is a requisite concern owning to the open and distributed nature of the cloud. Copious amounts of data are responsible for alluring hackers. Thus, developing efficacious IDS is an imperative task. This article analyzed four intrusion detection systems for the detection of attacks. Two standard benchmark datasets, (...) namely, NSL-KDD and UNSW-NB15, were used for the simulations. Additionally, this study highlights the proliferating challenges for the security of sensitive user data and gives useful recommendations to address the identified issues. Finally, the projected results show that the hybridization method with support vector machine classifier outperforms the existing techniques in the case of the datasets investigated. (shrink)

Policy Interaction Graph Analysis is a Host-based Intrusion Detection tool that uses Linux MAC Mandatory access control policy to build the licit information flow graph and uses a detection policy defined by the administrator to extract illicit behaviour from the graph. The main limitation of this tool is the generation of a huge signature base of illicit behaviours; hence, this leads to the use of huge memory space to store it. Our primary goal in this article is (...) to reduce this memory space while keeping the tool’s efficiency in terms of intrusion detection rate and false generated alarms. First, the interactions between the two nodes of the graph were grouped into a single interaction. The notion of equivalence class was used to classify the paths in the graph and was compressed by using a genetic algorithm. Such an approach showed its efficiency compared to the approach proposed by Pierre Clairet, by which the detection rate obtained was 99.9%, and no false-positive with a compression rate of illicit behaviour signature database reached 99.44%. Having these results is one of the critical aspects of realizing successful host-based intrusion detection systems. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K nearest (...) neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

In cloud security, intrusion detection system (IDS) is one of the challenging research areas. In a cloud environment, security incidents such as denial of service, scanning, malware code injection, virus, worm, and password cracking are getting usual. These attacks surely affect the company and may develop a financial loss if not distinguished in time. Therefore, securing the cloud from these types of attack is very much needed. To discover the problem, this paper suggests a novel IDS established on (...) a combination of a leader-based k-means clustering (LKM), optimal fuzzy logic system. Here, at first, the input dataset is grouped into clusters with the use of LKM. Then, cluster data are afforded to the fuzzy logic system (FLS). Here, normal and abnormal data are inquired by the FLS, while FLS training is done by the grey wolf optimization algorithm through maximizing the rules. The clouds simulator and NSL-Knowledge Discovery and DataBase (KDD) Cup 99 dataset are applied to inquire about the suggested method. Precision, recall, and F-measure are conceived as evaluation criteria. The obtained results have denoted the superiority of the suggested method in comparison with other methods. (shrink)

This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set (...) of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily. (shrink)

Specifically tailored industrial control systems attacks are becoming increasingly sophisticated, accentuating the need of ICS cyber security. The nature of these systems makes traditional IT security measures not suitable, requiring expressly developed security countermeasures. Within the past decades, research has been focused in network-based intrusion detection systems. With the appearance of software-defined networks, new opportunities and challenges have shown up in the research community. This paper describes the potential benefits of using SDNs in industrial networks with security purposes (...) and presents the set up and results of a pilot experiment carried out in a scaled physical implementation. The experimental set up consists in the detection of ICMP flood and packet payload alteration based on signature comparison. Results point to the potential viability of the technology for intrusion detection and the need of researching in architectural scalability. (shrink)

Despite the growing popularity of machine learning models in the cyber-security applications ), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous (...) studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms. (shrink)

Over the years, artificial neural networks have been applied successfully in many areas including IT security. Yet, neural networks can only process continuous input data. This is particularly challenging for security-related, non-continuous data like system calls of an operating system. This work focuses on five different options to preprocess sequences of system calls so that they can be processed by neural networks. These input options are based on one-hot encodings and learning word2vec, GloVe or fastText representations of system calls. As (...) an additional option, we analyse if mapping system calls to their respective kernel modules is an adequate generalization step for replacing system calls or enhancing system call data with additional information regarding their context. When performing such preprocessing steps it is important to ensure that no relevant information is lost during the process. The overall objective of system call analysis in the context of IT security is to categorize a sequence of them as benign or malicious behavior. Therefore, this scenario is used to evaluate different system call representations in a classification task. Results indicate that a broader range of attacks can be detected when enriching system call representations with corresponding kernel module information. Prior learning of embeddings does not achieve significant improvements. This work is an extension of the work by Wunderlich et al. [1] published in Advances in Intelligent Systems and Computing. (shrink)

In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the (...) experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. (...) The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

Detecting intrusions in large networks is a highly demanding task. In order to reduce the computation demand of analysing every single packet travelling along one of such networks, some years ago flows were proposed as a way of summarizing traffic information. Very few research works have addressed intrusion detection in flows from a visualizations perspective. In order to bridge this gap, the present paper proposes the application of a novel projection method (Beta Hebbian Learning) under this framework. With (...) the aim to validate this method, 8 traffic segments, containing many flows, have been analysed by means of this projection method. The promising results obtained for these segments, extracted from the University of Twente dataset, validate the proposed application. (shrink)

Contemporary Artificial Intelligence methods, especially their subset-deep learning, are finding their way to successful implementations in the detection and classification of intrusions at the network level. This paper presents an intrusion detection mechanism that leverages Deep AutoEncoder and several Deep Decoders for unsupervised classification. This work incorporates multiple network topology setups for comparative studies. The efficiency of the proposed topologies is validated on two established benchmark datasets: UNSW-NB15 and NetML-2020. The results of their analysis are discussed in (...) terms of classification accuracy, detection rate, false-positive rate, negative predictive value, Matthews correlation coefficient and F1-score. Furthermore, comparing against the state-of-the-art methods used for network intrusion detection is also disclosed. (shrink)

In this article I propose to discuss some passages in the Oresteia in order to illustrate the method devised by Heimsoeth and Headlam for the detection of intrusive glosses. Headlam's theory of glosses, which I outlined in a recent article, was based on a systematic study of the ancient lexica and scholia. Further work on the scholia has raised some problems affecting their authenticity, which need to be settled, if they are to be used effectively for the elucidation of (...) the text. (shrink)

Role-based database management systems are most widely used for information storage and analysis but are known as vulnerable to insider attacks. The core of intrusion detection lies in an adaptive system, where an insider attack can be judged if it is different from the predicted role by performing classification on the user’s queries accessing the database and comparing it with the authorized role. In order to handle the high similarity of user queries for misclassified roles, this paper proposes (...) a deep metric neural network with strategic sampling algorithm that properly extracts salient features and directly learns a quantitative measure of similarity. A strategic sampling method of heuristically generating and learning training pairs through Monte Carlo search is proposed to select a training pair that can represent the entire dataset. With the TPC-E–based benchmark data trained with 11,000 queries for 11 roles, the proposed model produces the classification accuracy of 95.41%, which is the highest compared with the previous models. The results are verified through comparison of quantitative and qualitative evaluations, and the feature space modelled in the neural network is analysed by t-SNE algorithm. (shrink)

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion (...) Detection Systems can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results. (shrink)

For a better detection in Network information security monitoring system, the author proposes a method based on adaptive depth detection. A deep belief network was designed and implemented, and the intrusion detection system model was combined with a support vector machine. The data set adopts the NSL-KDD network communication data set, and this data set is authoritative in the security field. Redundant cleaning, data type conversion, normalization, and other processing operations are performed on the data set. (...) Using the data conversion method based on the probability mass function probability mass function coding, a standard data set with low redundancy and low dimensionality can be obtained. Research indicates that when the batch size reaches 64, the accuracy of the test set reaches its maximum value. As the batch size increases, the accuracy first increases and then decreases. When the batch size continues to increase, the model will inevitably fall into the local optimal state, resulting in the degradation of the detection performance of the system. In terms of the false alarm rate, the DBN-SVM model is also the highest; however, it is only 10.73%. Under the premise of increasing the detection rate, the false alarm rate is improved; for the overall detection performance of the model, it is within an acceptable range. In terms of accuracy, the DBN-SVM model also scored the highest. The accuracy rate is the ratio of normal and correct classification for intrusion detection. It can explain the detection ability of the model. In summary, the overall detection ability of the DBN-SVM model is the best. The good classification ability to use SVM is proved, and the classification of low-dimensional features is expected to increase the detection rate of the system. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

In programs of acoustic survey, the amount of data collected and the lack of automatic routines for their classification and interpretation can represent a serious obstacle to achieving quick results. To overcome these obstacles, we are proposing an ecosemiotic model of data mining, ecoacoustic event detection and identification, that uses a combination of the acoustic complexity indices and automatically extracts the ecoacoustic events of interest from the sound files. These events may be indicators of environmental functioning at the scale (...) of individual vocal species, the acoustic community, the sound marks, or the soundscape. The EEDI model is represented by three procedural steps: 1) selecting acoustic data according to environmental variables, 2) detecting the events by creating an ecoacoustic event space produced by plotting ACIft and its evenness, 3) identifying events according to the level of correlation between the acoustic signature of the detected events and an ad hoc library of previously identified events. The EEDI procedure can be extensively used in basic and applied research. In particular, EEDI may be used in long-term monitoring programs to assess the effect of climate change on individual vocal species behavior, population, and acoustic community dynamics. The EEDI model can be also used to investigate acoustic human intrusion in natural systems and the effect in urban areas. (shrink)

This paper proposes a roadmap for how privacy leakages from outsourced managed security services using intrusion detection systems can be controlled. The paper first analyses the risk of leaking private or confidential information from signature-based intrusion detection systems. It then discusses how the situation can be improved by developing adequate privacy enforcement methods and privacy leakage metrics in order to control and reduce the leakage of private and confidential information over time. Such metrics should allow for (...) quantifying how much information that is leaking, where these information leakages are, as well as showing what these leakages mean. This includes adding enforcement mechanisms ensuring that operation on sensitive information is transparent and auditable. The data controller or external quality assurance organisations can then verify or certify that the security operation operates in a privacy friendly manner. The roadmap furthermore outlines how privacy-enhanced intrusion detection systems should be implemented by initially providing privacy-enhanced alarm handling and then gradually extending support for privacy enhancing operation to other areas like digital forensics, exchange of threat information and big data analytics based attack detection. (shrink)

There is much effort nowadays to protect communication networks against different cybersecurity attacks (which are more and more sophisticated) that look for systems’ vulnerabilities they could exploit for malicious purposes. Network Intrusion Detection Systems (NIDSs) are popular tools to detect and classify such attacks, most of them based on ML models. However, ML-based NIDSs cannot be trained by feeding them with network traffic data as it is. Thus, a Feature Engineering (FE) process plays a crucial role transforming network (...) traffic raw data onto derived one suitable for ML models. In this work, we study the effects of applying one such FE technique in different ways on the performance of two ML models (linear and non-linear) and their selected features. This the Feature as a Counter approach. The derived observations are computed from either with the same number of raw samples, (batch-based approaches) or by aggregating them by time intervals (timestamp-based approach). Results show that there is no significant differences between the proposed approaches neither in the performance of the models nor in the selected features that validate our proposal making it feasible to be widely used as a standard FE method. (shrink)

The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder’s latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing (...) on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity with more than a 99% of precision using only 2 learned features. (shrink)

In cybersecurity, there is a call for adaptive, accurate and efficient procedures to identifying performance shortcomings and security breaches. The increasing complexity of both Internet services and traffic determines a scenario that in many cases impedes the proper deployment of intrusion detection and prevention systems. Although it is a common practice to monitor network and applications activity, there is not a general methodology to codify and interpret the recorded events. Moreover, this lack of methodology somehow erodes the possibility (...) of diagnosing whether event detection and recording is adequately performed. As a result, there is an urge to construct general codification and classification procedures to be applied on any type of security event in any activity log. This work is focused on defining such a method using the so-called normalized compression distance (NCD). NCD is parameter-free and can be applied to determine the distance between events expressed using strings. As a first step in the concretion of a methodology for the integral interpretation of security events, this work is devoted to the characterization of web logs. On the grounds of the NCD, we propose an anomaly-based procedure for identifying web attacks from web logs. Given a web query as stored in a security log, a NCD-based feature vector is created and classified using a support vector machine. The method is tested using the CSIC-2010 data set, and the results are analyzed with respect to similar proposals. (shrink)

One of the major problems of intrusion detection concerns the large amount of alerts that intrusion detection systems (IDS) produce. Security operator who analyzes alerts and takes decisions, is often submerged by the high number of alerts to analyze. In this paper, we present a new alert correlation approach based on knowledge and preferences of security operators. This approach, which is complementary to existing ones, allows to rank-order produced alerts on the basis of a security operator (...) knowledge about the system, used IDS and his preferences about alerts that he wants to analyze or to ignore. Our approach is based on the development of a new non-classical logic for representing preferences, called FO-MQCL (First Order - Minimal Qualitative Choice Logic). Our logic extends a fragment of the first order logic by adding a new logical connective. The general idea is to present only alerts that fully fit security operator's preferences and knowledge. And if needed, less preferred alerts can also be presented. (shrink)

Connected devices, such as smartphones and tablets, are exposed to a large variety of attacks. Their protection is often challenged by their resource constraints in terms of CPU, memory and energy. Security chains, composed of security functions such as firewalls, intrusion detection systems and data leakage prevention mechanisms, offer new perspectives to protect these devices using software-defined networking and network function virtualization. However, the complexity and dynamics of these chains require new automation techniques to orchestrate them. This chapter (...) describes an automated orchestration methodology for security chains in order to secure connected devices and their applications. This methodology exploits process learning to establish behavioral models and infer security constraints represented as logical predicates. It then generates and merges a set of chains of security functions on the basis of these predicates. These chains are finally compiled into low-level configuration rules and deployed into the network, optimizing for the underlying topology. The benefits and limits of such a methodology combining machine learning and verification techniques are evaluated by a set of experimental results. (shrink)

Ritualized behavior, intuitively recognizable by its stereotypy, rigidity, repetition, and apparent lack of rational motivation, is found in a variety of life conditions, customs, and everyday practices: in cultural rituals, whether religious or non-religious; in many children's complicated routines; in the pathology of obsessive-compulsive disorders (OCD); in normal adults around certain stages of the life-cycle, birthing in particular. Combining evidence from evolutionary anthropology, neuropsychology and neuroimaging, we propose an explanation of ritualized behavior in terms of an evolved Precaution System geared (...) to the detection of and reaction to inferred threats to fitness. This system, distinct from fear-systems geared to respond to manifest danger, includes a repertoire of clues for potential danger as well as a repertoire of species-typical precautions. In OCD pathology, this system does not supply a negative feedback to the appraisal of potential threats, resulting in doubts about the proper performance of precautions, and repetition of action. Also, anxiety levels focus the attention on low-level gestural units of behavior rather than on the goal-related higher-level units normally used in parsing the action-flow. Normally automatized actions are submitted to cognitive control. This “swamps” working memory, an effect of which is a temporary relief from intrusions but also their long-term strengthening. Normal activation of this Precaution System explains intrusions and ritual behaviors in normal adults. Gradual calibration of the system occurs through childhood rituals. Cultural mimicry of this system's normal input makes cultural rituals attention-grabbing and compelling. A number of empirical predictions follow from this synthetic model. (Published Online February 8 2007) Key Words: childhood ritual; compulsion; event boundaries; evolutionary psychology; obsessive-compulsive disorder; ritual; thought intrusion. (shrink)

J.J. Gibson claims that one who is looking at Niagara Falls is seeing it directly, whereas one who is looking at a picture of Niagara Falls is seeing it indirectly or mediately. Gibson's cognitivist critics claim that all perception is mediated and that "external objects" are never seen directly. Each side takes the debate to be a scientific issue. But following Wittgenstein's "nose" for detecting philosophical intrusions into what do not appear to be philosophical debates, the author shows how such (...) elements play a decisive role in influencing the character of the argument. When the issue is seen from this perspective it can also be seen why both sides are mistaken in their claims. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Listening Eye: Postmodernism, Paranoia, and the HypervisibleJerry Aline Flieger (bio)Jean Baudrillard. The Transparency of Evil: Essays on Extreme Phenomena. Trans. James Benedict. London: Verso, 1993. Trans. of La transparence du mal: Essai sur les phénomènes extrêmes. Paris: Galilée, 1990.Jean-François Lyotard. The Inhuman: Reflections on Time. Trans. Geoff Bennington and Rachel Bowlby. Stanford: Stanford UP, 1991. Trans. of L’inhumain. Paris: Galilée, 1988.Slavoj Zizek. Looking Awry: An Introduction to Jacques (...) Lacan Through Popular Culture. Cambridge: MIT P, 1992.“To the insides of the ear belong those who have a sight of internal hearing.” That’s the nocturnal look, the listening eye.—Emmanuel Swedenborg, cited in Lyotard, The InhumanPostmodernism, Yet AgainIn this essay, I shall take a cue from Slavoj Zizek’s discussion of Lacanian anamorphosis, or skewed perspective (Looking Awry), in order to read three dissimilar works alongside one another, and from a “paranoid” slant. For in spite of sometimes radically incommensurate subject matter, the three works I discuss here—The Transparency of Evil (Jean Baudrillard), Looking Awry (Slavoj Zizek) and The Inhuman (Jean-François Lyotard)—all share a focus on the dehumanizing effects of technology as determinants of what Lyotard has called the postmodern condition. 1 Moreover, all three works themselves adopt something like a paranoid perspective, suggesting that under the sway of an ethic of sheer performativity, human contact—face to face—has been rendered obsolete by interface.In the bleak vision of The Transparency of Evil (1993), for instance, Baudrillard describes an isolated disembodied satellite man, cut loose from his moorings: “The centrifugal force of our proliferating technologies has stripped us of all weight.... Freed of all density, all gravity, we are being dragged into an orbital motion which threatens to become perpetual” [30–31]. Similarly, in “The Ecstasy of Communication” (1983), Baudrillard describes a robotic subject mesmerized by pornographic hypervisibility, [End Page 90] “more visible than the visible... the obscenity of what no longer has any secret, or what dissolves completely in information and communication” [130–31]. 2Like Baudrillard, Lyotard (The Inhuman) describes the loss of dimensional intersubjectivity, suggesting that space is no longer an enabling interval between subjects, the scene of the exchanged gaze: contemporary cyberspace is the scene of communication without community, saturated with hypervisible information. But while for Baudrillard, we are already in weightless orbit, in a postcatastrophic, fractal aftermath, for Lyotard our fate is not yet cast, although adumbrated disaster—the end of the species or of the universe—provides a telos for our existence.Slavoj Zizek is not nearly so metaphysical, nor so glum, as his fellow post-philosophers, but he is every bit as concerned with the optical, Looking Awry. Zizek is a patient and even cheery exegete of Lacan, attempting to “introduce” Lacanian thought by looking at film, detective fiction, and other user-friendly instances of modern culture as templates of theory. Indeed, compared to his fellow travelers, Zizek is resolutely quotidian, even earthbound, focusing on the contemporary human subject as consumer/spectator, seated not in a satellite capsule or at a computer terminal but at the movies, popcorn in hand.Yet in spite of his goodwill, Zizek’s take on postmodernism is anything but sanguine: he too depicts a subject who is prey to the fascination of overexposed “obscene” virtual reality (X-rated Hitchcock, if you will), and always threatened as well by the eruption of the menacing Lacanian real into the web of symbolic reality—an intrusion that awakens the paranoid “post-man” from an absorption in the image on the screen, with a reminder of the precariousness of all that appears evident or meaningful. Thus in “The Obscene Object of Postmodernity” [Looking Awry, chap. 8], Zizek describes a blot or stain that sticks out or refuses assimilation to the totalizing gaze, eclipsing the heretofore meaningful and legible symbolic universe with an ineffable real that “produces a radical opacity and blocks every essay of interpretation” [151]. I suggest that Zizek’s notion of the obscene object as blot—like the hair on the camera lens that “disturbs” our absorption in the screen—as well as Lyotard’s notion of obdurate matter, may... (shrink)

Exploitation is a fundamental element of the parental strategies of many species of birds. Cuckoos, for example, lay their eggs in the nest of other birds, who often unwittingly rear the alien nestlings as their own. Nest parasitism is an efficient reproductive strategy for cuckoos, who do not have to worry about building a nest, incubating their eggs, or feeding their nestlings. But not all hosts respond passively to such intrusions. In response to parasitic cowbirds, for example, robins have evolved (...) the ability to detect and selectively eject alien young from their nests. Human parenting strategies differ sharply from the strategies of cuckoos and robins. Unlike cuckoos, we are reluctant to allow our children to be raised by others. Unlike robins, we knowingly rear strange young. What makes human behavior toward children so different from that of cuckoos and robins? Humans seem to share a number of predispositions that facilitate successful adoptive relationships, and the desire to raise children seems to be pervasive among modern humans. Despite these commonalities, patterns of adoption transactions vary greatly among contemporary human societies. This paper considers the origins and causes of cross-cultural variation in human adoptive behavior from an evolutionary perspective. (shrink)

This article addresses the question of whether an expectation of privacy is reasonable in the face of new technologies of surveillance, by developing a principle that best fits our intuitions. A "no sense enhancement" principle which would rule out searches using technologically sophisticated devices is rejected. The paper instead argues for the "mischance principle," which proscribes uses of technology that reveal what could not plausibly be discovered accidentally without the technology, subject to the proviso that searches that serve a great (...) public good that clearly outweighs minimal intrusions upon privacy are permissible. Justifications of the principle are discussed, including reasons why we should use the principle and not rely solely on a utilitarian balancing test. The principle is applied to uses of aerial photography and heat-detection devices. (shrink)

Background Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method Four community juries were convened in two demographically different Sydney municipalities and two regional cities in (...) New South Wales, Australia to elicit the views of well-informed community members on the acceptability and legitimacy of: making pathogen WGS and linked administrative data available for public health researchusing this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. Results Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. Conclusions Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, even when the potential public health benefits are clear. Any suggestion of private sector involvement or commercialisation of WGS or surveillance data was unanimously rejected. (shrink)

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred (...) to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters. Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals. Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely lifelogging and self-monitoring, remote monitoring of vital signs, emotional state recognition, food intake monitoring, activity and behaviour recognition, activity and personal assistance, gesture recognition, fall detection and prevention, mobility assessment and frailty recognition, and cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed. (shrink)

Security has become one of the important factors for any network communication and transmission of data packets. An organization with an optimal security system can lead to a successful business and can earn huge profit on the business they are doing. Different network devices are linked to route, compute, monitor, and communicate various real-time developments. The hackers are trying to attack the network and want to draw the organization’s significant information for its own profits. During the communication, if an (...) class='Hi'>intrusion or eavesdropping occurs, it will lead to a severe disfigurement of the whole communication network, and the data will be controlled by wrong malicious users. Identification of attack is a way to identify the security violations and analyze the measures in a computer network. An identification system, which is effective and accurate, can add security to the existing system for secure and smooth communication among end to end nodes and can work efficiently in the identification of attack on data packets. The role of information security is to design and protect the entire data of networks and maintain its confidentiality, integrity, and availability for their right users. Therefore, there is a need for end to end security management, which will ensure the security and privacy of the network and will save the data inside networks from malicious users. As the network devices are growing, so the level of threats is also increasing for these devices. The proposed research is an endeavor toward the detection of data packets attack by using the rough set theory for a secure end to end communication. The experimental work was performed by the RSES tool. The accuracy of the K-NN was 88% for the total objects of 8459. For cross validation purposes, the decision rules and decomposition tree algorithms were used. The DR algorithm showed accuracy of 59.1%, while the DT showed accuracy of 61.5%. The experimental results of the proposed study show that the research is capable of detecting data packets attack. (shrink)

Llewellyn's proposal that rapid eye movement dreaming reflects elaborative encoding mediated by the hippocampus offers an interesting perspective for understanding hallucinations accompanying sleep paralysis. SP arises from anomalous intrusion of REM processes into waking consciousness, including threat-detection systems mediated by the amygdala. Unique aspects of SP hallucinations offer additional prospects for investigation of Llewellyn's theory of elaborative encoding.

Recent developments in studies of sperm‐mediated gene transfer (SMGT) now provide solid ground for the notion that sperm cells can act as vectors for exogenous genetic sequences. A substantive body of evidence indicates that SMGT is potentially useable in animal transgenesis, but also suggests that the final fate of the exogenous sequences transferred by sperm is not always predictable. The analysis of SMGT‐derived offspring has shown the existence of integrated foreign sequences in some cases, while in others stable modifications of (...) the genome are difficult to detect. The appearance of SMGT‐derived modified offspring on the one hand and, on the other hand, the rarity of actual modification of the genome, suggest inheritance as extrachromosomal structures. Several specific factors have been identified that mediate distinct steps in SMGT. Among those, a prominent role is played by an endogenous reverse transcriptase of retrotransposon origin. Mature spermatozoa are naturally protected against the intrusion of foreign nucleic acid molecules; however, particular environmental conditions, such as those occurring during human assisted reproduction, can abolish this protection. The possibility that sperm cells under these conditions carry genetic sequences affecting the integrity or identity of the host genome should be critically considered. These considerations further suggest the possibility that SMGT events may occasionally take place in nature, with profound implications for evolutionary processes. BioEssays 27: 551–562, 2005. © 2005 Wiley periodicals, Inc. (shrink)

This paper offers a general theory of privacy, a theory that takes privacy to consist in being free from certain kinds of intrusions. On this understanding, privacy interests are distinct and distinguishable from those in solitude, anonymity, and property, for example, or from the fact that others possess, with neither consent nor permission, personal information about oneself. Privacy intrusions have both epistemic and psychological components, and can range in value from relatively trivial considerations to those of profound consequence for an (...) individual’s dignity, integrity, and autonomy. Thus while the focus of this theory is privacy per se— privacy as being free from certain kinds of intrusions—it has significant implications, discussed briefly, for what properly counts as the content of moral or legal rights to privacy. (shrink)

How might advanced neuroscience—in which perfect neuro-predictions are possible—interact with ordinary judgments of free will? We propose that peoples' intuitive ideas about indeterminist free will are both imported into and intrude into their representation of neuroscientific scenarios and present six experiments demonstrating intrusion and importing effects in the context of scenarios depicting perfect neuro-prediction. In light of our findings, we suggest that the intuitive commitment to indeterminist free will may be resilient in the face of scientific evidence against such (...) free will. (shrink)

On the basis of a communicative theory of criminal punishment, I show how mercy has a significant but limited role to play in the criminal law—in particular (although not only) in criminal sentencing. Mercy involves an intrusion into the realm of criminal law of values and concerns that are not themselves part of the perspective of criminal law: a merciful sentencer acts beyond the limits of her legal role, on the basis of moral considerations that conflict with the demands (...) of penal justice. Sometimes, however (but in a decent system of law in a decent society, rarely), that is how citizens should act. Finally, I discuss, and criticise, two attempts to find a place for mercy within a communicative conception of punishment, and argue that repentance is not an appropriate ground for leniency or mercy in sentencing. (shrink)

Can managers detect honest people’s lies in a handwritten message? In this article, I will briefly discuss graphology and a basic model of interpersonal communication. I will then develop a fundamental theoretical framework of eight principles for detecting lies based on the basic communication model, handwriting analyses, and the following assumptions: For most people, it is easier to tell the truth than to tell lies. This applies to handwritings also. When most honest people lie, they try to hide their stressful (...) emotions in the encoding process. As a consequence, they deviate from their own normal writing and violate their own personal moral standards. Interestingly enough, the art or science of detecting a lie in a handwritten sample is to focus not on what they write, but on how they write it. These 24 exhibits (cases) written in 11 languages—used in different parts of the world—help managers apply this important theoretical framework of interpersonal communication, understand the encoding process, pinpoint these sudden emotional changes, decode handwritten messages, unlock the secrets, reveal the message’s true meanings, and detect people’s lies. (shrink)

Defect detection is a major concern in quality control of various products in industries. This paper presents two different machine-vision based methods for detecting defects on periodically patterned textures. In the first method, input defective image is split into several blocks of size same as the size of the periodic unit of the image and chi-square histogram distances of each periodic block with respect to itself and all other periodic blocks are calculated to get a dissimilarity matrix. This dissimilarity (...) matrix is subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. The second method of defect detection is based on Universal Quality Index which is a measure of loss of correlation, luminance distortion and contrast distortion between any two signals. Quality indices of a periodic block with respect to itself and all other periodic blocks are calculated to get a similarity matrix containing quality indices. Specific variances of the periodic blocks are derived from the quality index matrix through orthogonal factor model based on eigen decomposition. These variances are subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results of experiments on real fabric images with defects show that the defect detection methods based on chi-square histogram distance and universal quality index yield a success rate of 98.6% and 97.8% respectively. (shrink)

Presented research focuses on detecting of interrelationships between the overall rate of self-harm and the symptomatology of eating disorders. The research group consisted of 60 adolescent girls with eating disorders. We used the SHI questionnaire to determine self-harm and EDI-2 to determine the symptoms of individual eating disorders. We detected that there was a moderate positive relationship between self-harm and the overall score in EDI-2 symptoms. We also found moderate and weak positive relationships between eating disorders symptoms and self-harm. We (...) did not observe a statistically significant relationship between bulimia and self-harm. (shrink)