Detection of Multi-Vector DDoS Attack
DOI:
https://doi.org/10.26438/ijcse/v7i6.847851Keywords:
DDoS, vectors, Machine Learning, Confusion MatrixAbstract
In this current technology driven society, internet has become a basic commodity for every individuals as well as organization. Due to the rapid increase of internet dependency of government offices, private company, or corporate sectors, security has become the main concern in all of these organizations. Attack over the network using stochastic approaches has created large chaos. The DDoS attack has created destruction and damages over the network since early 2000’s. DDoS is known for its ability to fade the identity of the source of attack because of multiple address and flooding mechanism. Preventing the attack from its original source is quite difficult. This floods the whole system making the system of the particular sector to be crippled and can be remedied by early detection of the attack. In this work we try to detect the different DDoS attack vectors and classify it. The nature and its mechanism are studied to identify the type of attack. We use scikit learn, a machine learning approach to detect different forms of attacks.
References
[1] Krishna Modi, Prof. Abdul Quadir Md., “Detection and Prevention of DDoS Cloud using Double-TCP Mechanism and HMM-Architecture”, Vol.3,No.2,pp.113–120, April 2014,
[2] Amarpreet Singh, Priya Sharma, “A novel mechanism for detecting DOS attack in VANET using Enhanced Attacked Packet Detection Algorithm (EAPDA)”, IEEE Transaction, Proceedings of 2015 RAECS UIET Panjab University Chandigarh 21-22nd December 2015.
[3] Erwin Adi, “Distributed denial-of-service attacks against HTTP/2 services”, pp.79–86, 2016.
[4] Eric Perraud “Machine Learning Algorithm of Detection of DOS Attack on an Automotive Telmatic Unit” International Journal of Computer Networks & Communications (IJCNC) Vol.11, No.1, 27-43, January 2019
[5] Thwe Thwe Oo, Thandar Phyu, “Statistical Anomaly Detection of DDoS Attacks Using K-Nearest Neighbour”, International Journal of Computer & Communication Engineering Research (IJCCER) Volume.2, Issue.1 January 2014.
[6] Aqueel Sahi, D.Lai, Yan Li, Mohammed Diykh, “An Efficient DDoS TCP Flood Attack Detection and Prevention System in a Cloud Environment”, IEEE Access, pp.1-13, Vol.5, April2017.
[7] Harshita, Ruchikaa Nayyar, “Detection of ICMP Flood DDOS Attack”, International Journal of Computer Science Trends and Technology (IJCST), Vol.5, Issue.2, pp.199-205,March-April2017.
[8] Munazza Shabbir, Muazzam A. Khan,Umair Shafiq Khan, Nazar A. Saqib “Detection and Prevention of Denial of Service Attacks in VANET”, International Conference on Computational Science and Computational Intelligence, pp.970-974, 2016.
[9] Opeyemi Osanaiye, Haibin Cai, Kim-Kwang Raymond Choo, Ali Dehghantanha, Zheng Xu and Mqhele Dlodlo, “Ensemble-based multi-filter feature selection method for DDOS Detection in Cloud Computing”, EURASIP Journal on Wireless Communication and Networking, pp.1-10, 2016.
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