Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD

Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by ‘open networks’, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings

Security risk assessment and protection in the chemical and process industry

This article describes a security risk assessment and protection methodology that was developed for use in the chemical- and process industry in Belgium. The approach of the method follows a risk-based approach that follows desing principles for chemical safety. That approach is beneficial for workers in the chemical industry because they recognize the steps in this model from familiar safety models .The model combines the rings-of-protection approach with generic security practices including: management and procedures, security technology (e.g. CCTV, fences, and access control), and human interactions (pro-active as well as re-active). The method is illustrated in a case-study where a practical protection plan was developed for an existing chemical company. This chapter demonstrates that the method is useful for similar chemical- and process industrial activities far beyond the Belgian borders, as well as for cross-industrial security protection. This chapter offers an insight into how the chemical sector protects itself on the one hand, and an insight into how security risk management can be practiced on the other hand

Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience

Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks. This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint

The concepts of surveillance and sousveillance: A critical analysis

The concept of surveillance has recently been complemented by the concept of sousveillance. Neither term, however, has been rigorously defined, and it is particularly unclear how to understand and delimit sousveillance. This article sketches a generic definition of surveillance and proceeds to explore various ways in which we might define sousveillance, including power differentials, surreptitiousness, control, reciprocity, and moral valence. It argues that for each of these ways of defining it, sousveillance either fails to be distinct from surveillance or to provide a generally useful concept. As such, the article concludes that academics should avoid the neologism, and simply clarify what sense of surveillance is at stake when necessary

WIRELESS NETWORK LOAD CORROBORATION USING MACHINE LEARNING (ML) BASED VIDEO ANALYTICS

Presented herein are techniques for correlating the output of a crowd counting machine learning (ML) algorithm, which operates on surveillance video, with observed network load to determine if a load spike is due to a valid network usage or an attacker trying to sabotage the network. The techniques presented herein include vision field classification based on access point (AP) coverage, linking of vision fields to AP coverage in DNAC UI, and consensus-based threat assessment and alerts

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Psychopower and Ordinary Madness: Reticulated Dividuals in Cognitive Capitalism

Despite the seemingly neutral vantage of using nature for widely-distributed computational purposes, neither post-biological nor post-humanist teleology simply concludes with the real "end of nature" as entailed in the loss of the specific ontological status embedded in the identifier "natural." As evinced by the ecological crises of the Anthropocene—of which the 2019 Brazil Amazon rainforest fires are only the most recent—our epoch has transfixed the “natural order" and imposed entropic artificial integration, producing living species that become “anoetic,” made to serve as automated exosomatic residues, or digital flecks. I further develop Gilles Deleuze’s description of control societies to upturn Foucauldian biopower, replacing its spacio-temporal bounds with the exographic excesses in psycho-power; culling and further detailing Bernard Stiegler’s framework of transindividuation and hyper-control, I examine how becoming-subject is predictively facilitated within cognitive capitalism and what Alexander Galloway terms “deep digitality.” Despite the loss of material vestiges qua virtualization—which I seek to trace in an historical review of industrialization to postindustrialization—the drive-based and reticulated "internet of things" facilitates a closed loop from within the brain to the outside environment, such that the aperture of thought is mediated and compressed. The human brain, understood through its material constitution, is susceptible to total datafication’s laminated process of “becoming-mnemotechnical,” and, as neuroplasticity is now a valid description for deep-learning and neural nets, we are privy to the rebirth of the once-discounted metaphor of the “cybernetic brain.” Probing algorithmic governmentality while posing noetic dreaming as both technical and pharmacological, I seek to analyze how spirit is blithely confounded with machine-thinking’s gelatinous cognition, as prosthetic organ-adaptation becomes probabilistically molded, networked, and agentially inflected (rather than simply externalized)

Real-Time Building Management System Visual Anomaly Detection Using Heat Points Motion Analysis Machine Learning Algorithm

The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomaly detection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV-based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy