19,763 research outputs found

    An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

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    Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

    APPLICATION OF GAME THEORY FOR ACTIVE CYBER DEFENSE AGAINST ADVANCED PERSISTENT THREATS

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    Advanced persistent threats (APTs) are determined, adaptive, and stealthy threat actors in cyber space. They are often hosted in, or sponsored by, adversary nation-states. As such, they are challenging opponents for both the U.S. military and the cyber-defense industry. Current defenses against APTs are largely reactive. This thesis used machine learning and game theory to test simulations of proactive defenses against APTs. We first applied machine learning to two benchmark APT datasets to classify APT network traffic by attack phase. This data was then used in a game model with reinforcement learning to learn the best tactics for both the APT attacker and the defender. The game model included security and resource levels, necessary conditions on actions, results of actions, success probabilities, and realistic costs and benefits for actions. The game model was run thousands of times with semi-random choices with reinforcement learning through a program created by NPS Professor Neil Rowe. Results showed that our methods could model active cyber defense strategies for defenders against both historical and hypothetical APT campaigns. Our game model is an extensible planning tool to recommend actions for defenders for active cyber defense planning against APTs.Approved for public release. Distribution is unlimited.Captain, United States Marine CorpsCaptain, United States Marine CorpsDISA, Arlington, VA, 2220
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