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Saab, Farah, Kayssi, Ayman, Elhajj, Imad, Chehab, Ali.  2016.  Solving Sybil Attacks Using Evolutionary Game Theory. Proceedings of the 31st Annual ACM Symposium on Applied Computing. :2195–2201.

Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.

Tosh, D., Sengupta, S., Kamhoua, C., Kwiat, K., Martin, A..  2015.  An evolutionary game-theoretic framework for cyber-threat information sharing. 2015 IEEE International Conference on Communications (ICC). :7341–7346.

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.

Wang, Zan-Jun, Lin, Ching-Hua Vivian, Yuan, Yang-Hao, Huang, Ching-Chun Jim.  2019.  Decentralized Data Marketplace to Enable Trusted Machine Economy. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE). :246–250.
Transacting IoT data must be different in many from traditional approaches in order to build much-needed trust in data marketplaces, trust that will be the key to their sustainability. Data generated internally to an organization is usually not enough to remain competitive, enhance customer experiences, or improve strategic decision-making. In this paper, we propose a decentralized and trustless architecture through the posting of trade records while including the transaction process on distributed ledgers. This approach can efficiently enhance the degree of transparency, as all contract-oriented interactions will be written on-chain. Storage via an end-to-end encrypted message channel allows transmitting and accessing trusted data streams over distributed ledgers regardless of the size or cost of the device, while simultaneously making a verifiable Auth-compliant request to the platform. Furthermore, the platform will complete matching, trading and refunding processes with-out human intervention, and it also protects the rights of data providers and consumers through trading policies which apply revolutionary game theory to the machine economy.