Vulnerability analysis of satellite-based synchronized smart grids monitoring systems

The large-scale deployment of wide-area monitoring systems could play a strategic role in supporting the evolution of traditional power systems toward smarter and self-healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by a satellite-based global positioning system. Although these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radio frequency interference. Consequently, a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for correct and safe wide-area monitoring system operation. Armed with such a vision, this article presents and discusses the results of an experimental analysis aimed at characterizing the vulnerability of global positioning system based wide-area monitoring systems to external interferences. The article outlines the potential strategies that could be adopted to protect global positioning system receivers from external cyber-attacks and proposes decentralized defense strategies based on self-organizing sensor networks aimed at assuring correct time synchronization in the presence of external attacks

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.Comment: 8 pages, 4 figure

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

A New Tax Framework: A Blueprint for Averting a Fiscal Crisis

This report presents fiscally responsible recommendations for overhauling the federal tax system and raising the revenues needed to combat mounting federal budget deficits and the fiscal demands presented by an aging society. The proposal calls for a new hybrid federal tax system featuring a phased-in 10 percent Value-Added Tax (VAT) to supplement a reformed and streamlined federal income tax. The report also provides guidance for eliminating, reducing and consolidating special tax preferences. In addition, the report calls for the elimination of the Alternative Minimum Tax (AMT) and reform of the Estate Tax

Evaluating Multimodal Driver Displays of Varying Urgency

Previous studies have evaluated Audio, Visual and Tactile warnings for drivers, highlighting the importance of conveying the appropriate level of urgency through the signals. However, these modalities have never been combined exhaustively with different urgency levels and tested while using a driving simulator. This paper describes two experiments investigating all multimodal combinations of such warnings along three different levels of designed urgency. The warnings were first evaluated in terms of perceived urgency and perceived annoyance in the context of a driving simulator. The results showed that the perceived urgency matched the designed urgency of the warnings. More urgent warnings were also rated as more annoying but the effect of annoyance was lower compared to urgency. The warnings were then tested for recognition time when presented during a simulated driving task. It was found that warnings of high urgency induced quicker and more accurate responses than warnings of medium and of low urgency. In both studies, the number of modalities used in warnings (one, two or three) affected both subjective and objective responses. More modalities led to higher ratings of urgency and annoyance, with annoyance having a lower effect compared to urgency. More modalities also led to quicker responses. These results provide implications for multimodal warning design and reveal how modalities and modality combinations can influence participant responses during a simulated driving task

Coping with Layoffs: Current State Strategies for Better Rapid Response

This issue brief from the Heldrich Center for Workforce Development at Rutgers University examines the issue of state rapid response practices that are designed to coordinate the delivery of a wide array of services to displaced workers. It summarizes the efforts of several states, identified by national experts as having promising practices, in the service delivery,organization, and evaluation of rapid response activities. It particularly highlights those states that combine post-layoff activities with strong proactive layoff aversion and worker transition assistance