The role of information systems in the prevention and detection of transnational and international crime

© Cambridge University Press 2014. All around the world criminal activity remains at the forefront of governmental concerns, not only as a problem that distorts the very fabric of society within the confines of national jurisdictions, but also as a problem that cuts across national borders to exhibit a global dimension. The international dimension of criminal activity remains critical and is generally characterized by a complexity that is unique and requires action on many different levels. Criminals set out to mask their illegal activities and deliberately generate complexity as a means of concealment. In doing so, they exploit new developments in technology that assist them in achieving their ends. This criminality exhibits forms of innovation that stretch far beyond traditional criminal activity (e.g., drug and human trafficking) and manages to attach itself within the broader fabric of society by exploiting the very latest developments. This evolution is necessary as criminals seek not only to escape arrest, prosecution and conviction, but also to enjoy the fruits of their criminality (mostly financial gains). Thus, they seek to develop ways of exploiting the various diffuse norms of social interaction (e.g., trust), financial modes of conduct (e.g., cash-based economies), technological and communication developments (e.g., Internet), and thereby minimize the possibility for detection. By limiting the resources that can be made available for prevention (or making them obsolete when developing new criminal behaviour), they participate in this co-evolution actively; and this they achieve by generating complexity

NegDL: Privacy-Preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy

Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR'16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further

JPEG: the quadruple object

The thesis, together with its practice-research works, presents an object-oriented perspective on the JPEG standard. Using the object-oriented philosophy of Graham Harman as a theoretical and also practical starting point, the thesis looks to provide an account of the JPEG digital object and its enfolding within the governmental scopic regime. The thesis looks to move beyond accounts of digital objects and protocols within software studies that position the object in terms of issues of relationality, processuality and potentiality. From an object-oriented point of view, the digital object must be seen as exceeding its relations, as actual, present and holding nothing in reserve. The thesis presents an account of JPEG starting from that position as well as an object-oriented account of JPEG’s position within the distributed, governmental scopic regime via an analysis of Facebook’s Timeline, tagging and Haystack systems. As part of a practice-research project, the author looked to use that perspective within photographic and broader imaging practices as a spur to new work and also as a “laboratory” to explore Harman’s framework. The thesis presents the findings of those “experiments” in the form of a report alongside practice-research eBooks. These works were not designed to be illustrations of the theory, nor works to be “analysed”. Rather, following the lead of Ian Bogost and Mark Amerika, they were designed to be “philosophical works” in the sense of works that “did” philosophy

The State of Surveillance Law (with transcript)

Jeffrey Vagle and Marcy Wheeler examine the the intricacies of U.S. surveillance law and the implications for civil liberties and national security

The State of Surveillance Law (with transcript)

Jeffrey Vagle and Marcy Wheeler examine the the intricacies of U.S. surveillance law and the implications for civil liberties and national security

JPEG: the quadruple object

The thesis, together with its practice-research works, presents an object-oriented perspective on the JPEG standard. Using the object-oriented philosophy of Graham Harman as a theoretical and also practical starting point, the thesis looks to provide an account of the JPEG digital object and its enfolding within the governmental scopic regime. The thesis looks to move beyond accounts of digital objects and protocols within software studies that position the object in terms of issues of relationality, processuality and potentiality. From an object-oriented point of view, the digital object must be seen as exceeding its relations, as actual, present and holding nothing in reserve. The thesis presents an account of JPEG starting from that position as well as an object-oriented account of JPEG’s position within the distributed, governmental scopic regime via an analysis of Facebook’s Timeline, tagging and Haystack systems. As part of a practice-research project, the author looked to use that perspective within photographic and broader imaging practices as a spur to new work and also as a “laboratory” to explore Harman’s framework. The thesis presents the findings of those “experiments” in the form of a report alongside practice-research eBooks. These works were not designed to be illustrations of the theory, nor works to be “analysed”. Rather, following the lead of Ian Bogost and Mark Amerika, they were designed to be “philosophical works” in the sense of works that “did” philosophy

From banal surveillance to function creep: automated license plate recognition (ALPR) in Denmark

This article discusses how Automated License Plate Recognition (ALPR) has been implemented in Denmark across three different sectors: parking, environmental zoning, and policing. ALPR systems are deployed as a configuration of cameras, servers, and algorithms of computer vision that automatically reads and records license plates of passing cars. Through digital ethnography and interviews with key stakeholders in Denmark, we contribute to the fields of critical algorithm and surveillance studies with a concrete empirical study on how ALPR systems are configured according to user-specific demands. Each case gives nuance to how ALPR systems are implemented: (1) how the seamless charging for a “barrier-free” parking experience poses particular challenges for customers and companies; (2) how environmental zoning enforcement through automated fines avoids dragnet data collection through customized design and regulation; and (3) how the Danish Police has widened its dragnet data collection with little public oversight and questionable efficacy. We argue that ALPR enacts a form of “banal surveillance” because such systems operate inconspicuously under the radar of public attention. As the central analytic perspective, banality highlights how the demand for increasing efficiency in different domains makes surveillance socially and politically acceptable in the long run. Although we find that legal and civic modes of regulation are important for shaping the deployment of ALPR, the potential for function creep is embedded into the very process of infrastructuring due to a lack of public understanding of these technologies. We discuss wider consequences of ALPR as a specific and overlooked instance of algorithmic surveillance, contributing to academic and public debates around the embedding of algorithmic governance and computer vision into everyday life