On Reliability of Smart Grid Neighborhood Area Networks

With the integration of the advanced computing and communication technologies, smart grid system is dedicated to enhance the efficiency and the reliability of future power systems greatly through renewable energy resources, as well as distributed communication intelligence and demand response. Along with advanced features of smart grid, the reliability of smart grid communication system emerges to be a critical issue, since millions of smart devices are interconnected through communication networks throughout critical power facilities, which has an immediate and direct impact on the reliability of the entire power infrastructure. In this paper, we present a comprehensive survey of reliability issues posted by the smart grid with a focus on communications in support of neighborhood area networks (NAN). Specifically, we focus on network architecture, reliability requirements and challenges of both communication networks and systems, secure countermeasures, and case studies in smart grid NAN. We aim to provide a deep understanding of reliability challenges and effective solutions toward reliability issues in smart grid NAN

An Empirical Analysis and Evaluation of Internet Robustness

The study of network robustness is a critical tool in the understanding of complex interconnected systems such as the Internet, which due to digitalization, gives rise to an increasing prevalence of cyberattacks. Robustness is when a network maintains its basic functionality even under failure of some of its components, in this instance being nodes or edges. Despite the importance of the Internet in the global economic system, it is rare to find empirical analyses of the global pattern of Internet traffic data established via backbone connections, which can be defined as an interconnected network of nodes and edges between which bandwidth flows. Hence in this thesis, I use metrics based on graph properties of network models to evaluate the robustness of the backbone network, which is further supported by international cybersecurity ratings. These cybersecurity ratings are adapted from the Global Cybersecurity Index which measures countries' commitments to cybersecurity and ranks countries based on their cybersecurity strategies. Ultimately this empirical analysis follows a three-step process of firstly mapping the Internet as a network of networks, followed by analysing the various networks and country profiles, and finally assessing each regional network's robustness. By using TeleGeography and ITU data, the results show that the regions with countries which have higher cybersecurity ratings in turn have more robust networks, when compared to regions with countries which have lower cybersecurity ratings

Criticality assessment of energy infrastructure

After the last major accidents in the energy sector of the last decade (USA and Canada (2003), India (2012), Russian-Ukrainian (2009)), energy infrastructure criticality assessment has become one of the most important issues. It has become the topical subject of the economy and national security in all countries. There is no single measure unit for the assessment of critical infrastructure with respect to “interdependency” among critical infrastructure sectors. This paper proposes to use criticality of infrastructure element as a measure to assess the importance of considered element to the normal activity of all sectors of infrastructure. The pilot numerical simulation of heat and electricity infrastructure was performed to demonstrate the implementation of the application of developed method for the assessment of infrastructure functionality and criticality

On Detection of Current and Next-Generation Botnets.

Botnets are one of the most serious security threats to the Internet and its end users. A botnet consists of compromised computers that are remotely coordinated by a botmaster under a Command and Control (C&C) infrastructure. Driven by financial incentives, botmasters leverage botnets to conduct various cybercrimes such as spamming, phishing, identity theft and Distributed-Denial-of-Service (DDoS) attacks. There are three main challenges facing botnet detection. First, code obfuscation is widely employed by current botnets, so signature-based detection is insufficient. Second, the C&C infrastructure of botnets has evolved rapidly. Any detection solution targeting one botnet instance can hardly keep up with this change. Third, the proliferation of powerful smartphones presents a new platform for future botnets. Defense techniques designed for existing botnets may be outsmarted when botnets invade smartphones. Recognizing these challenges, this dissertation proposes behavior-based botnet detection solutions at three different levels---the end host, the edge network and the Internet infrastructure---from a small scale to a large scale, and investigates the next-generation botnet targeting smartphones. It (1) addresses the problem of botnet seeding by devising a per-process containment scheme for end-host systems; (2) proposes a hybrid botnet detection framework for edge networks utilizing combined host- and network-level information; (3) explores the structural properties of botnet topologies and measures network components' capabilities of large-scale botnet detection at the Internet infrastructure level; and (4) presents a proof-of-concept mobile botnet employing SMS messages as the C&C and P2P as the topology to facilitate future research on countermeasures against next-generation botnets. The dissertation makes three primary contributions. First, the detection solutions proposed utilize intrinsic and fundamental behavior of botnets and are immune to malware obfuscation and traffic encryption. Second, the solutions are general enough to identify different types of botnets, not a specific botnet instance. They can also be extended to counter next-generation botnet threats. Third, the detection solutions function at multiple levels to meet various detection needs. They each take a different perspective but are highly complementary to each other, forming an integrated botnet detection framework.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91382/1/gracez_1.pd