ML Associated DoS and DDoS Attack Observation in Protection - Pratiksha Malekar; Sayli Bhosale; Sanika Dhumal (International Journal of Satellite Remote Sensing, 2024)
DoS and DDoS assaults are significant risks to the availability and integrity of online services and networks. Attack traffic might come from a variety of geographical regions, making it difficult to filter and neutralize the attack. DDoS attacks are far more sophisticated and powerful than DoS attacks. They use a network of compromised devices, known as a botnet, to launch a coordinated attack on a target. Monitoring and evaluating the network for odd trends, such as rapid increases in traffic volume or changes in traffic distribution. This paper presents an approach for the detection of DoS and DDoS attacks using a combination of mathematical and entropy-based methods. The proposed approach lever ages the inherent characteristics of these attacks to develop robust detection mechanisms that enhance network security. Machine learning algorithms, particularly those based on supervised and unsupervised learning, are becoming increasingly prevalent in the detection of DoS and DDoS attacks. This paper provides insights into the ap plication of machine learning for attack classification and the development of predictive models to anticipate new attack vectors.
Keywords: DDoS attack detection; DoS attack detection; Machine Learning; entropy methods; mathematical methods