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2009
DOI: 10.1016/j.cose.2009.04.007
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Utilizing bloom filters for detecting flooding attacks against SIP based services

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Cited by 60 publications
(51 citation statements)
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References 27 publications
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“…The selection of these symbols reflects the different parts of a SIP message that an attacker could craft in order to launch a resource consumption or other type of attack. In fact, this method of assault is well-documented and evaluated in various researches so far [7,8,9,31]. For instance, a malicious actor could fabricate different SIP messages by modifying some of their parts 135 such as <Via>, <From>, <To>, <Call-ID> headers or even the First Line (corresponding to symbols S2 -S5, and S1 in Figure 1) depending on the situation at hand.…”
Section: Symbol Definitionmentioning
confidence: 99%
See 1 more Smart Citation
“…The selection of these symbols reflects the different parts of a SIP message that an attacker could craft in order to launch a resource consumption or other type of attack. In fact, this method of assault is well-documented and evaluated in various researches so far [7,8,9,31]. For instance, a malicious actor could fabricate different SIP messages by modifying some of their parts 135 such as <Via>, <From>, <To>, <Call-ID> headers or even the First Line (corresponding to symbols S2 -S5, and S1 in Figure 1) depending on the situation at hand.…”
Section: Symbol Definitionmentioning
confidence: 99%
“…with the aim to paralyze the victim as reported in [31,37] or execute a low-volume DoS to silently consume valuable network resources. This is for sure to gradually increase user discontent, which in turn leads to reducing provider's market share.…”
mentioning
confidence: 99%
“…Geneiatakis et al [5] surveyed SIP security mechanisms. In a later paper, Geneiatakis et al [6] detailed memory usage of a SIP proxy under a flooding attack but did not discuss effects of such an attack on the UAC host. Additionally, they presented a bloom filter system to track call state in order to detect Invite floods.…”
Section: Related Workmentioning
confidence: 99%
“…Roh et al [12] propose whitelist-based countermeasure scheme based on none-member ratio by utilizing CBF. Geneiatakis et al [13], [14] take advantage of CBF to calculate session distance of SIP to detect anomalies with the assumption that flooding attack is associated with incomplete sessions and there exists correlations between different SIP attributes. Rebahi et al [15] also consider the half-open connection issue, and propose a non-parametric CUSUM algorithm to detect gradual change in means of time series.…”
Section: Related Workmentioning
confidence: 99%