A machine learning based attack detection and mitigation using a secure SaaS framework

Abstract

Software-as-a-service (SaaS) is a license for access to a specific cloud application through the Internet. However, these services are delayed and sometimes totally disrupted because of the Internet’s unavailability that provides opportunities for a lot of attacks. Research into the security of the cloud focuses mainly on prohibiting malicious users from using the cloud to launch attacks, such as those currently done by botnets, including launching a DDoS attack, sending spam, etc. Because SaaS uses computational power from both servers of cloud computing providers and machines of customers, we argue that SaaS may be elaborately exploited in an unprecedented way as an attack vector for botnets. The previous research work has developed a novel framework for detecting the attacks with the aid of a deep learning approach (Doriguzzi-Corin et al., 2020). Although the attack node in the network has been identified, they were not diminished. An enhanced and powerful adversary model is to be provided as a solution to this problem. Hence, this paper intends to develop a novel framework for attack node mitigation using a secure SaaS Framework. The main contribution of this paper provides an attack detection process that takes place in Deep Belief Network (DBN), in which the weight, as well as activation function, are fine-tuned with Median Fitness oriented Sea Lion Optimization algorithm (MFSLnO). If DBN detects an attack node, the control is transferred to a lightweight bait approach that reliably mitigates the most common attack nodes without disrupting regular connections will be deployed. The performance of the proposed work yielded the best results over the traditional models with a packet loss ratio of 16% and throughput of 89%.

Keywords

• SaaS framework
• SDN
• Attack detection in DBN
• MFSLnO
• Attack mitigation with bait