Secure Wireless Sensor Network Energy Optimization Model with Game Theory and Deep Learning Algorithm

AnishFathima, B.; Mahaboob, M.; Kumar, S.Gokul; Jabakumar, A.Kingsly

doi:10.1109/icaccs54159.2022.9785348

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…In general, the bar graph shows how often escape attacks work with different defense strategies and frameworks, which helps you figure out how well they protect against hostile threats. The table (4) gives an in-depth look at a number of different computer defense strategies and frameworks, focused on different evaluation factors such as F1 score, accuracy, precision, area under the curve (AUC), sensitivity, and specificity. By looking at these factors, you can see how well each defense plan works at stopping hostile machine learning (AML) attacks and keeping you safe from online dangers.…”

Section: Resultsmentioning

confidence: 99%

“…The suggested method, which probably combines a number of different defense systems and strategic factors, comes out on top in every evaluation criterion. The table (4) shows that the suggested method does a better job of stopping AML attacks and online risks, with an F1 score of 95%, an accuracy score of 92%, a precision score of 88%, and an AUC score of 92%. It also gets a balance between sensitivity and precision, which shows that it can correctly identify both true positive and true negative cases.…”

Section: Resultsmentioning

confidence: 99%

“…It also gets a balance between sensitivity and precision, which shows that it can correctly identify both true positive and true negative cases. The Figure (4) shows a bar graph that shows the escape attack success rate (%) for different cybersecurity defense strategies and frameworks. There are different defense strategies or frameworks shown by each bar.…”

Section: Resultsmentioning

confidence: 99%

“…In the area of cybersecurity, there is always an arms race going on between attackers and defenses. Defenders, which can be anyone from a single person to a large organization, use a variety of security measures to keep their systems and data safe from theft, alteration, and access by people who aren't supposed to have it [3] [4]. These steps include many types of technology, like firewalls, attack detection systems, encryption protocols, and more recently, systems that use machine learning to find and stop threats and strange behavior.…”

Section: Introductionmentioning

confidence: 99%

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Game Theory and Adversarial Machine Learning: Analyzing Strategic Interactions in Cybersecurity

Bhagawati Chunilal Patil

2024

cana

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When it comes to cybersecurity, the way that attackers and defenders work together strategically is becoming more and more like how games work in general. Adversarial machine learning (AML) has become an important area of hacking. In AML, attackers use complex methods to avoid being caught and take advantage of flaws in machine learning models. The goal of this study is to give a full picture of how strategies combine in cybersecurity by looking at where game theory and AML meet. You can think of the strategic exchanges in cybersecurity as a game between attackers and defenders. Defenders want to keep systems and data safe, and enemies want to break into them for bad reasons. In this game, players have to make a lot of decisions. Defenders have to think ahead and prepare for possible attacks, while attackers change their plans all the time to avoid being caught and take advantage of defenses' weaknesses. Game theory gives us a way to formally model these interactions, which lets us look at the best tactics and results that are in balance. During AML, defenders use machine learning models to find and stop security risks, while attackers change these models using methods like escape attacks, poisoning attacks, and model inversion attacks. In these hostile tactics, there is a strategic element added to cybersecurity, where defenders must think about the goals and skills of attackers when creating and using defenses. This paper looks at the research that has already been done on using game theory to study hacking and how these models can be used to study how strategies combine in AML. It talks about different types of games, like rigid and dynamic games, and what they mean for protecting against threats from other players. Besides that, it looks into how uneven knowledge, doubt, and strategy learning affect the results of computer games. This paper helps us understand the strategy problems that both attackers and defenders face in cybersecurity better by combining game theory with AML. It shows how important it is to think strategically and build weapons that can change to deal with risks that change, and it sets the stage for future study in this field, which is changing very quickly.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Game Theory and Adversarial Machine Learning: Analyzing Strategic Interactions in Cybersecurity

Bhagawati Chunilal Patil

2024

cana

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PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

Liu,

Zheng

2024

Complex Intell. Syst.

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The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

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Melanoma Unveiled: Harnessing Convolutional Neural Network ResNet 50 for Precise Segmentation and Detection

Mahaboob,

Ramalingam,

et al. 2023

2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Secure Wireless Sensor Network Energy Optimization Model with Game Theory and Deep Learning Algorithm

Cited by 4 publications

References 11 publications

Game Theory and Adversarial Machine Learning: Analyzing Strategic Interactions in Cybersecurity

Game Theory and Adversarial Machine Learning: Analyzing Strategic Interactions in Cybersecurity

PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

Melanoma Unveiled: Harnessing Convolutional Neural Network ResNet 50 for Precise Segmentation and Detection

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