Steps to defend against DoS attacks

Habib, Ahsan; Roy, Debashish

doi:10.1109/iccit.2009.5407310

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Among the most common DoS attacks there are flooding attacks which exploit holes in used network protocols [5]. In our work we focus on proposal of modification of SYN flood attack detection mechanism and its implementation into KaTaLyzer.…”

Section: Introductionmentioning

confidence: 99%

Syn Flood Attack Detection and Type Distinguishing Mechanism Based on Counting Bloom Filter

Halagan¹,

Kováčik²,

Truchly³

et al. 2015

Information and Communication Technology

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Part 1: Networks and System ArchitectureInternational audiencePresented work focuses onto proposal, implementation and evaluation of the new method for detection and type identification of SYN flood (DoS) attacks. The method allows distinguishing type of detected SYN flood attacks – random, subnet or fixed. Based on Counting Bloom filter, the attack detection and identification algorithm is proposed, implemented and evaluated in KaTaLyzer network traffic monitoring tool. Proof of correctness of the approach for TCP SYN flood attack detection and type identification is provided – both in practical and theoretical manners. In practice, new module for KaTaLyzer is implemented and TCP attacks are detected, identified and network administrator is notified about them in real-time

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Section: Introductionmentioning

confidence: 99%

Syn Flood Attack Detection and Type Distinguishing Mechanism Based on Counting Bloom Filter

Halagan¹,

Kováčik²,

Truchly³

et al. 2015

Information and Communication Technology

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“…Spam is used by bots to overload the email inboxes and increase the traffic on a server which eventually leads to DoS. A number of approaches have been taken in order to defend against DoS attacks [4]. Around 80% of the received emails today are spam.…”

Section: Introductionmentioning

confidence: 99%

Filtering Spam by Hybrid Approach

Tabris¹,

Al-Nashif²,

Hariri³

2011

Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Spam has become a main cause of financial loss in most of the organization. It was seen that 81.6% of the email Traffic in 2006 was spam [1]. The loss incurred by the companies is growing exponentially and so is the number of spam emails. This makes spam detection and spam filters are critically important. There are various techniques used in order to filter spam, two of most prominent techniques available are IP blacklisting/white-listing and content-based filtering. An email is identified as spam based on the reputation of the source done by grey listing the source from their previous history [2]. In this paper we introduce a method for improving the spam filters by using a hybrid technique (Content Based & Anomaly based detection approach). In this work, we show how to identify whether the email is spam or not by implementing models that capture the nature of emails' headers and patterns found in the emails' content. The general behavior of spam and legitimate emails for each of these models is obtained and assigned a score; the value of this score is used to differentiate between a legitimate emails and spam. By using this hybrid approach, we were able to detect spam with a false positive rate of .54% and a false negative rate of 1.34%. We also discuss the relation between phishing and spam and how some anti-phishing techniques can be used in spam filters.

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Steps to defend against DoS attacks

Cited by 2 publications

References 9 publications

Syn Flood Attack Detection and Type Distinguishing Mechanism Based on Counting Bloom Filter

Syn Flood Attack Detection and Type Distinguishing Mechanism Based on Counting Bloom Filter

Filtering Spam by Hybrid Approach

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