Game-Theoretic Frameworks and Strategies for Defense Against Network Jamming and Collocation Attacks

Modern networks are becoming increasingly more complex, heterogeneous, and densely connected. While more diverse services are enabled to an ever-increasing number of users through ubiquitous networking and pervasive computing, several important challenges have emerged. For example, densely connected networks are prone to higher levels of interference, which makes them more vulnerable to jamming attacks. Also, the utilization of software-based protocols to perform routing, load balancing and power management functions in Software-Defined Networks gives rise to more vulnerabilities that could be exploited by malicious users and adversaries. Moreover, the increased reliance on cloud computing services due to a growing demand for communication and computation resources poses formidable security challenges due to the shared nature and virtualization of cloud computing. In this thesis, we study two types of attacks: jamming attacks on wireless networks and side-channel attacks on cloud computing servers. The former attacks disrupt the natural network operation by exploiting the static topology and dynamic channel assignment in wireless networks, while the latter attacks seek to gain access to unauthorized data by co-residing with target virtual machines (VMs) on the same physical node in a cloud server. In both attacks, the adversary faces a static attack surface and achieves her illegitimate goal by exploiting a stationary aspect of the network functionality. Hence, this dissertation proposes and develops counter approaches to both attacks using moving target defense strategies. We study the strategic interactions between the adversary and the network administrator within a game-theoretic framework. First, in the context of jamming attacks, we present and analyze a game-theoretic formulation between the adversary and the network defender. In this problem, the attack surface is the network connectivity (the static topology) as the adversary jams a subset of nodes to increase the level of interference in the network. On the other side, the defender makes judicious adjustments of the transmission footprint of the various nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender\u27s strategy is based on playing Nash equilibrium strategies securing a worst-case network utility. Moreover, scalable decomposition-based approaches are developed yielding a scalable defense strategy whose performance closely approaches that of the non-decomposed game for large-scale and dense networks. We study a class of games considering discrete as well as continuous power levels. In the second problem, we consider multi-tenant clouds, where a number of VMs are typically collocated on the same physical machine to optimize performance and power consumption and maximize profit. This increases the risk of a malicious virtual machine performing side-channel attacks and leaking sensitive information from neighboring VMs. The attack surface, in this case, is the static residency of VMs on a set of physical nodes, hence we develop a timed migration defense approach. Specifically, we analyze a timing game in which the cloud provider decides when to migrate a VM to a different physical machine to mitigate the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker\u27s payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings as well as a proof-of-concept implementation in a realistic cloud setting

Tematski zbornik radova međunarodnog značaja. Tom 3 / Međunarodni naučni skup “Dani Arčibalda Rajsa”, Beograd, 10-11. mart 2016.

In front of you is the Thematic Collection of Papers presented at the International Scientific Conference “Archibald Reiss Days”, which was organized by the Academy of Criminalistic and Police Studies in Belgrade, in co-operation with the Ministry of Interior and the Ministry of Education, Science and Technological Development of the Republic of Serbia, National Police University of China, Lviv State University of Internal Affairs, Volgograd Academy of the Russian Internal Affairs Ministry, Faculty of Security in Skopje, Faculty of Criminal Justice and Security in Ljubljana, Police Academy “Alexandru Ioan Cuza“ in Bucharest, Academy of Police Force in Bratislava and Police College in Banjaluka, and held at the Academy of Criminalistic and Police Studies, on 10 and 11 March 2016. The International Scientific Conference “Archibald Reiss Days” is organized for the sixth time in a row, in memory of the founder and director of the first modern higher police school in Serbia, Rodolphe Archibald Reiss, PhD, after whom the Conference was named. The Thematic Collection of Papers contains 165 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, informatics, as well as by members of national security system participating in education of the police, army and other security services from Belarus, Bosnia and Herzegovina, Bulgaria, China, Croatia, Greece, Hungary, Macedonia, Montenegro, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Spain, Switzerland, Turkey, Ukraine and United Kingdom. Each paper has been double-blind peer reviewed by two reviewers, international experts competent for the field to which the paper is related, and the Thematic Conference Proceedings in whole has been reviewed by five competent international reviewers. The papers published in the Thematic Collection of Papers contain the overview of contemporary trends in the development of police education system, development of the police and contemporary security, criminalistic and forensic concepts. Furthermore, they provide us with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, as well as suggestions on how to systematically deal with these issues. The Collection of Papers represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Collection contributes to improving of mutual cooperation betw

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available