Biblio

Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.

Through analysis of sessions in which files were created and downloaded on three Cowrie SSH/Telnet honeypots, we find that IoT botnets are by far the most common source of malware on connected systems with weak credentials. We detail our honeypot configuration and describe a simple method for listing near-identical malicious login sessions using edit distance. A large number of IoT botnets attack our honeypots, but the malicious sessions which download botnet software to the honeypot are almost all nearly identical to one of two common attack patterns. It is apparent that the Mirai worm is still the dominant botnet software, but has been expanded and modified by other hackers. We also find that the same loader devices deploy several different botnet malware strains to the honeypot over the course of a 40 day period, suggesting multiple botnet deployments from the same source. We conclude that Mirai continues to be adapted but can be effectively tracked using medium interaction honeypots such as Cowrie.

We use potential fields tuned by genetic algorithms to dynamically reconFigure unmanned aerial vehicles networks to serve user bandwidth needs. Such flying network base stations have applications in the many domains needing quick temporary networked communications capabilities such as search and rescue in remote areas and security and defense in overwatch and scouting. Starting with an initial deployment that covers an area and discovers how users are distributed across this area of interest, tuned potential fields specify subsequent movement. A genetic algorithm tunes potential field parameters to reposition UAVs to create and maintain a mesh network that maximizes user bandwidth coverage and network lifetime. Results show that our evolutionary adaptive network deployment algorithm outperforms the current state of the art by better repositioning the unmanned aerial vehicles to provide longer coverage lifetimes while serving bandwidth requirements. The parameters found by the genetic algorithm on four training scenarios with different user distributions lead to better performance than achieved by the state of the art. Furthermore, these parameters also lead to superior performance in three never before seen scenarios indicating that our algorithm finds parameter values that generalize to new scenarios with different user distributions.

In recent years, integrated circuits (ICs) have become significant for various industries and their security has been given greater priority, specifically in the supply chain. Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multi-level game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zero-sum, repeated game using prospect theory (PT) that captures different players' rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender learns about the attacker's tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by "playing dumb" in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker's view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.

With the growing number of cyberattack incidents, organizations are required to have proactive knowledge on the cybersecurity landscape for efficiently defending their resources. To achieve this, organizations must develop the culture of sharing their threat information with others for effectively assessing the associated risks. However, sharing cybersecurity information is costly for the organizations due to the fact that the information conveys sensitive and private data. Hence, making the decision for sharing information is a challenging task and requires to resolve the trade-off between sharing advantages and privacy exposure. On the other hand, cybersecurity information exchange (CYBEX) management is crucial in stabilizing the system through setting the correct values for participation fees and sharing incentives. In this work, we model the interaction of organizations, CYBEX, and attackers involved in a sharing system using dynamic game. With devising appropriate payoff models for each player, we analyze the best strategies of the entities by incorporating the organizations' privacy component in the sharing model. Using the best response analysis, the simulation results demonstrate the efficiency of our proposed framework.

With the advancement of unmanned aerial vehicles (UAV), 3D wireless mesh networks will play a crucial role in next generation mission critical wireless networks. Along with providing coverage over difficult terrain, it provides better spectral utilization through 3D spatial reuse. However, being a wireless network, 3D meshes are vulnerable to jamming/disruptive attacks. A jammer can disrupt the communication, as well as control of the network by intelligently causing interference to a set of nodes. This paper presents a distributed mechanism of avoiding jamming attacks by means of 3D spatial filtering where adaptive beam nulling is used to keep the jammer in null region in order to bypass jamming. Kalman filter based tracking mechanism is used to estimate the most likely trajectory of the jammer from noisy observation of the jammer's position. A beam null border is determined by calculating confidence region of jammer's current and next position estimates. An optimization goal is presented to calculate optimal beam null that minimizes the number of deactivated links while maximizing the higher value of confidence for keeping the jammer inside the null. The survivability of a 3D mesh network with a mobile jammer is studied through simulation that validates an 96.65% reduction in the number of jammed nodes.

The initiative to protect against future cyber crimes requires a collaborative effort from all types of agencies spanning industry, academia, federal institutions, and military agencies. Therefore, a Cybersecurity Information Exchange (CYBEX) framework is required to facilitate breach/patch related information sharing among the participants (firms) to combat cyber attacks. In this paper, we formulate a non-cooperative cybersecurity information sharing game that can guide: (i) the firms (players)1 to independently decide whether to “participate in CYBEX and share” or not; (ii) the CYBEX framework to utilize the participation cost dynamically as incentive (to attract firms toward self-enforced sharing) and as a charge (to increase revenue). We analyze the game from an evolutionary game-theoretic strategy and determine the conditions under which the players' self-enforced evolutionary stability can be achieved. We present a distributed learning heuristic to attain the evolutionary stable strategy (ESS) under various conditions. We also show how CYBEX can wisely vary its pricing for participation to increase sharing as well as its own revenue, eventually evolving toward a win-win situation.