Conference Papers

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    Analysis of Mirai Malware and Its Components
    (Springer Science and Business Media Deutschland GmbH, 2023) Kumar, S.; Chandavarkar, B.R.
    Mirai malware is the most famous malware in the field of IoT. It created much destruction around the end of the year 2016. With just a common password vulnerability of IoT devices, it created a large botnet of 600K–700K and was able to launch DDoS attacks that were double and triple the volume of DDoS attacks launched so far. It is implemented in such a beautiful and unsophisticated way that more attacks will be later implemented and appended. Releasing its source code provides a way for the attackers to create its variants and provides researchers with a path in the right direction to tackle upcoming variants of Mirai malware. Although the mechanism of attacks and implementation of Mirai seems easy, its implementation is challenging. The following paper provides a guided way to understand Mirai malware’s functionality and launch it in an isolated environment to do further research on it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    TCP SYN Flood Attack Detection Using Logistic Regression and Multi-Agent Reinforcement Learning
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sanjay, M.; Arun Raj Kumar, P.
    In the realm of cybersecurity, Distributed Denial of Service (DDoS) attacks remain a continuous threat, particularly TCP SYN flood attacks due to their stealthiness and potential for disruption. In this paper, we propose a combination of Multi-Agent Reinforcement Learning (MARL) with logistic regression for enhancing TCP SYN attack detection, leveraging Actor-Critic as the reinforcement learning algorithm. A novel approach is introduced for hyperparameter optimization using MARL, offering an alternative to traditional techniques such as GridSearchCV and RandomSearchCV. We present a comparative analysis between traditional logistic regression and MARL enhanced approaches, evaluating their performance using metrics such as accuracy, false negatives, and false positives. Results demonstrate that our proposed approach significantly improves detection accuracy and reduces false positives, underscoring its potential in bolstering cybersecurity defenses against sophisticated DDoS threats. © 2025 IEEE.