A Proposal to Distinguish DDoS Traffic in Flash Crowd Environments

A Flash Crowd (FC) event occurs when network traffic increases suddenly due to a specific reason (e.g. e-commerce sale). Despite its legitimacy, this kind of situation usually decreases the network resource performance. Furthermore, attackers may simulate FC situations to introduce undetected attacks, such as Distributed Denial of Service (DDoS), since it is very difficult to distinguish between legitimate and malicious data flows. To differentiate malicious and legitimate traffic we propose applying zero inflated count data models in conjunction with the Correlation Coefficient Flow (CCF) method – a well-known method used in FC situations. Our results were satisfactory and improve the accuracy of CCF method. Furthermore, since the environment toggles between normal and FC situations, our method has the advantage of working in both situations.

Detection of DDoS Attack in TCP protocol using Hybrid Machine Learning Techniques

Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself.

An intelligent peer-to-peer streaming model for minimizing flash crown

Peer-to-peer streaming systems (P2PSS) have become well deployed over the Internet in recent times due to its robustness, scalability, distributive nature and cost effectiveness. It is not un-common that peers arrive on the system in large numbers at a particular time in order to retrieve multimedia files. The large spike in the number of peers arriving at a time can be referred to as flash crowd. Several methods have been proposed, models have been designed aiming at providing solution to the problem. This paper attempts to alleviate flash crowd that may occur in the system using a fuzzy logic control system. Inputs were created and fuzzified, rules were developed and then the outputs were defuzzified. Analysis of the results derived from the MATLAB simulation reveal that under a flash crowd scenario, our fuzzy logic controller functions appropriately by the detecting flash crowd when it is about to occur and then necessary actions are taken. Also, with the fuzzy logic control system peer access in and out of the system is successfully controlled. The system allows for new peers to connect more with high bandwidth peers aiming at making the upload rate of each parent proportional to its upload bandwidth thereby utilizing the limited bandwidth resources of the entire P2P system more effectively.Keywords: Peer-to-peer networks, video streaming and flash crowdVol. 26, No. 1, June, 2019