Survey On Low Rate Denial of Service Attack (LDoS) on RED and its Counter Strategies

Mohan, Lija

doi:10.5013/ijssst.a.13.05.02

Cited by 2 publications

(1 citation statement)

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“…In the same year, Mohan et al in [17] produced a survey of the evolution proposed by Mathew and Katkar in [16], adding other detection mechanisms, of which some are based on the AQM (Active Queue Management) RED (Random Early Detection) algorithm and its variations. The authors also presented a defense mechanism based on a modified version of RED-PD (Preferential Dropping) that detected LDoS attacks, but did not specify which LDoS type of attack had been detected, even though the architecture of the proposal demonstrated that the attack was probably a Shrew attack.…”

Section: Review Of Prior Surveys On Ldos Attacksmentioning

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: Review Of Prior Surveys On Ldos Attacksmentioning

confidence: 99%

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

et al. 2022

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A new metric for flow‐level filtering of low‐rate DDoS attacks

Şimşek

2015

Security Comm Networks

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Low-rate distributed denial-of-service (LDDoS) attacks dramatically reduce transmission control protocol throughput by exploiting the vulnerability in the transmission control protocol congestion control mechanism. The current study proposes a new metric called mean Internet Protocol (IP) packet delay variation (mipdv) to detect LDDoS flows and a filtering method called ipdv-based LDDoS filtering (ILF) using mipdv. Receiving first seven packets from a flow is sufficient to calculate the mipdv metric. Subsequently, mipdv can be recalculated for each received packet. This makes the detection of LDDoS flows possible in a short time (in a few tens of milliseconds in most cases). Ns2 simulations were conducted to evaluate the performance of ILF. Experimental results show that ILF detects LDDoS flows in a very short time with very high accuracy.

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Survey On Low Rate Denial of Service Attack (LDoS) on RED and its Counter Strategies

Cited by 2 publications

References 0 publications

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

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

A new metric for flow‐level filtering of low‐rate DDoS attacks

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