Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Abstract

Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique.

Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization

“…Dataset. This section verifies the effectiveness of the FR-APPSO-BiLSTM model based on the datasets NSL-KDD, UNSW-NB15, and CICIDS-2017 [36][37][38].…”