In today’s fast paced world, one cannot imagine life without Internet, which is required in diverse fields, namely, communication, education, business shopping, and the list is infinite. We have analyzed the relevant studies and the results of the SLR are categorized into five main research areas: (i) the different types of DDoS attack detection deep learning approaches, (ii) the methodologies, strengths, and weaknesses of existing deep learning approaches for DDoS attacks detection (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature, and (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature (v) the research gaps, and future directions. The authors have explored four extensively used digital libraries (IEEE, ACM, ScienceDirect, Springer) and one scholarly search engine (Google scholar) for searching the recent literature. This paper systematically reviews the prominent literature specifically in deep learning to detect DDoS. Since the volume of data generated and stored has increased manifolds, the traditional detection mechanisms are not appropriate for detecting novel DDoS attacks. The attackers are updating their skill strategies continuously and hence elude the existing detection mechanisms. One of the lethal threat surfacing is the Distributed Denial of Service (DDoS) attack that can cripple down Internet-based services and applications in no time. It leads to exponential increase in intrusions and attacks over the Internet-based technologies. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutes has shifted from offline to online. In today’s world, technology has become an inevitable part of human life.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |