123,425 research outputs found
Power system security enhancement by HVDC links using a closed-loop emergency control
In recent years, guaranteeing that large-scale interconnected systems operate safely, stably and economically has become a major and emergency issue. A number of high profile blackouts caused by cascading outages have focused attention on this issue. Embedded HVDC (High Voltage Direct Current) links within a larger AC power system are known to act as a “firewall” against cascading disturbances and therefore, can effectively contribute in preventing blackouts. A good example is the 2003 blackout in USA and Canada, where the Québec grid was not affected due to its HVDC interconnection. In the literature, many works have studied the impact of HVDC on the power system stability, but very few examples exist in the area of its impact on the system security. This paper presents a control strategy for HVDC systems to increase their contribution to system security. A real-time closed-loop control scheme is used to modulate the DC power of HVDC links to alleviate AC system overloads and improve system security. Simulations carried out on a simplified model of the Hydro-Québec network show that the proposed method works well and can greatly improve system security during emergency situations.Peer reviewedFinal Accepted Versio
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
Department of Homeland Security Science and Technology Directorate: Developing Technology to Protect America
In response to a congressional mandate and in consultation with Department of Homeland Security's (DHS) Science and Technology Directorate (S&T), the National Academy conducted a review of S&T's effectiveness and efficiency in addressing homeland security needs. This review included a particular focus that identified any unnecessary duplication of effort, and opportunity costs arising from an emphasis on homeland security-related research. Under the direction of the National Academy Panel, the study team reviewed a wide variety of documents related to S&T and homeland security-related research in general. The team also conducted interviews with more than 200 individuals, including S&T officials and staff, officials from other DHS component agencies, other federal agencies engaged in homeland security-related research, and experts from outside government in science policy, homeland security-related research and other scientific fields.Key FindingsThe results of this effort indicated that S&T faces a significant challenge in marshaling the resources of multiple federal agencies to work together to develop a homeland security-related strategic plan for all agencies. Yet the importance of this role should not be underestimated. The very process of working across agencies to develop and align the federal homeland security research enterprise around a forward-focused plan is critical to ensuring that future efforts support a common vision and goals, and that the metrics by which to measure national progress, and make changes as needed, are in place
A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead
Physical layer security which safeguards data confidentiality based on the
information-theoretic approaches has received significant research interest
recently. The key idea behind physical layer security is to utilize the
intrinsic randomness of the transmission channel to guarantee the security in
physical layer. The evolution towards 5G wireless communications poses new
challenges for physical layer security research. This paper provides a latest
survey of the physical layer security research on various promising 5G
technologies, including physical layer security coding, massive multiple-input
multiple-output, millimeter wave communications, heterogeneous networks,
non-orthogonal multiple access, full duplex technology, etc. Technical
challenges which remain unresolved at the time of writing are summarized and
the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication
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