An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory

Abstract

In wireless sensors networks, nodes may be easily captured and act non-cooperatively, for example by not defending forwarding packets in response to their own limited resources. If most of these nodes are obtained by attackers, and an attack by an internal malicious node occurs, the entire network will be paralyzed and not be able to provide normal service. Low power consumption indicates that the rational sensor nodes tend to be very close to the mean; high power consumption indicates that the rational sensor nodes are spread out over a large range of values. This paper offers an active defense model for wireless sensor networks based on evolutionary game theory. We use evolutionary game theory to analyze the reliability and stability of a wireless sensor network with malicious nodes. Adding a defense model into the strategy space of the rational nodes and establishing a preventive mechanism forces the malicious node to abandon the attack and even switch to cooperative strategies. Thus, this paper argues that the stability and reliability of wireless sensor networks can be improved. Numerical experiments were conducted to evaluate the proposed defense model, and these results verified our conclusions based on a theoretical analysis that showed that, compared with the existing algorithms, our approach has lower energy consumption, lower deviation, and a higher probability to quickly switch each node to cooperative strategies.

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

“…Deception-based defenses are potent weapons that have been proven to work in various domains. Their efficacy is based on the fact that they are programmed to exploit key biases to appear realistic but misleading substitutes to the hidden reality [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 ]. As a result, one will require a thorough understanding of both offensive and defensive trickery to implement a perfect Deception strategy.…”

“…The advantages of machine learning algorithms can be extended for deploying Defensive Deception frameworks [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. Deception has been employed in honeypots, which are legal traps and honeynets (honeypot networks), as a defensive tool for information systems to keep attackers occupied [ 90 , 91 , 92 , 93 ,…”

A Game-Theoretic Analysis for Distributed Honeypots

“…The game theory is adopted for limiting activities of a sensor and its neighbors to save battery energy. Based on evolutionary game theory, the work [26] presents an active defense model in wireless sensor network. The reliability and stability in a network equipped with malicious nodes are analyzed.…”

Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

“…In the evolutionary game process, which is driven by the learning mechanism and the difference of game return, the dominant strategy will gradually spread among the players before finally forming the Evolutionary Stable Strategy (ESS). Some researchers have proposed a defense strategy modeling analysis method based on evolutionary game to explore the evolution of both strategies in the process of attacker and defender confrontation, as in [10][11][12][13][14][15]. Learning mechanism is the core of the evolutionary game model, and it is also the essential feature that distinguishes it from the classical game model.…”