Evolving Malware and DDoS Attacks: Decadal Longitudinal Study

Authors- Balaji. L, Professor Dr.Prasanna.S

Abstract- With the emergence of network-based computing technologies like Cloud Computing, Fog Computing, and IoT (Internet of Things), the context of digitizing confidential data over the network is being adopted by various organizations where the security of that sensitive data is considered a major concern. Over a decade there has been a massive growth in the usage of the internet along with the technological advancements that demand the need for the development of efficient security algorithms that could withstand various patterns of security breaches These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization’s site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper used a machine learning approach for DDoS attack types classification and prediction For this purpose, used LSTM and CNN classification algorithms. To access the research proposed dataset UNWS-np-15 extracted a complete framework for DDoS attack prediction to get better accuracy.

DOI: /10.61463/ijset.vol.13.issue3.121