Distributed Denial of Service (DDoS) attacks are performed from multiple\nagents towards a single victim. Essentially, all attacking agents generate multiple\npackets towards the victim to overwhelm it with requests, thereby overloading\nthe resources of the victim. Since it is very complex and expensive to\nconduct a real DDoS attack, most organizations and researchers result in using\nsimulations to mimic an actual attack. The researchers come up with diverse\nalgorithms and mechanisms for attack detection and prevention. Further,\nsimulation is good practice for determining the efficacy of an intrusive\ndetective measure against DDoS attacks. However, some mechanisms are ineffective\nand thus not applied in real life attacks. Nowadays, DDoS attack has\nbecome more complex and modern for most IDS to detect. Adjustable and\nconfigurable traffic generator is becoming more and more important. This\npaper first details the available datasets that scholars use for DDoS attack detection.\nThe paper further depicts the a few tools that exist freely and commercially\nfor use in the simulation programs of DDoS attacks. In addition, a\ntraffic generator for normal and different types of DDoS attack has been developed.\nThe aim of the paper is to simulate a cloud environment by\nOMNET++ simulation tool, with different DDoS attack types. Generation\nnormal and attack traffic can be useful to evaluate developing IDS for DDoS\nattacks detection. Moreover, the result traffic can be useful to test an effective\nalgorithm, techniques and procedures of DDoS attacks.
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