TS-SVM: Detect LDoS Attack in SDN Based on Two-step Self-adjusting SVM

Liu, Boru; Tang, Dan; Yan, Yudong; Zheng, Zhiqing; Zhang, Shihan; Zhou, Jiangmeng

doi:10.1109/trustcom53373.2021.00100

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Liu et al in [108] created a Low-rate detection mechanism which was compounded by the union of the self-adjusting SVM method with the APSO (Adaptive Particle Swarm Optimization) algorithm. The self-adjusting SVM method has the aim of enhancing the generalization ability of the pure SVM algorithm and it was made using 2 approaches, which are: the adjustment of the gamma parameter and the adoption of the optimal degree value from the training data.…”

Section: B Roq Attackmentioning

confidence: 99%

Detection and Mitigation of Low-Rate Denial-of-Service Attacks: A Survey

et al. 2022

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The potential for being the target of Denial of Service (DoS) attacks is one of the most severe security threats on the Internet. Attackers have been modifying their attack format over the years, damaging specific conditions of operating systems and protocols in an attempt to deny or diminish the quality of the service provided to legitimate users. Nowadays, attacks are stealthier and mimic legitimate user traffic in such a way that detection mechanisms against High-rate DoS attacks are no longer sufficient. This evolving type of attack, known as LDoS (Low-rate Denial of Service) attacks, has the potential to produce more damage than its predecessor due to its stealth nature and the lack of suitable detection and defense methods. This survey summarizes and complements previous studies and surveys related to this specific type of attack. First, we propose a taxonomy of the LDoS attacks, which were divided into three broad categories based on their modus operandi: QoS attacks, Slow rate attacks, and Service queue attacks. Next, we detail numerous detection mechanisms and counter-measures available against eight types of LDoS attacks. More specifically, we describe the methods used to throttle the attack traffic. Finally, we provide a feature comparison table for some existing attack tools. This survey aims at providing an extensive review of the literature for helping researchers and network administrators find up-to-date knowledge on LDoS attacks.

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Section: B Roq Attackmentioning

confidence: 99%

Detection and Mitigation of Low-Rate Denial-of-Service Attacks: A Survey

et al. 2022

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Securing internet of things using machine and deep learning methods: a survey

Ghaffari,

Jelodari,

pouralish

et al. 2024

Cluster Comput

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The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people’s daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges.

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