Exponential Squirrel Search Algorithm-Based Deep Classifier for Intrusion Detection in Cloud Computing with Big Data Assisted Spark Framework

Abstract

Intrusion detection systems (IDS) are extensively employed for detecting suspicious behaviors in hosts. The ability of distributed IDS solutions makes it viable to combine and handle various kinds of sensors and generate alerts to different hosts positioned in distributed platforms. However, to offer secure and feasible services in a cloud platform is an imperative issue due to the impacts of attacks. This paper devises a novel IDS framework using cloud data to counter the influence of attacks. Here, the spark architecture is employed for discovering the intrusions. The pre-processing is applied to the input data for removing artifacts and noise considering input data. Thereafter, the feature extraction and feature fusion are performed in slave nodes. The feature fusion is carried out with the proposed Exponential Squirrel Search Algorithm (ExpSSA) algorithm. The fused features are considered in a deep-stacked autoencoder (Deep SAE) for performing effective intrusion detection. The proposed ExpSSA is adapted to train Deep SAE for tuning optimum weights. The exponential weighted moving average (EWMA) and squirrel search algorithm (SSA) are combined to create the proposed ExpSSA. The proposed ExpSSA-based Deep SAE offered improved performance compared to other techniques with the highest accuracy, detection rate of 0.846, and minimal FPR.

Keywords:

• Big data
• cloud computing
• intrusion detection
• spark architecture
• deep stacked autoencoder