Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Abstract

In current era, a tremendous volume of data has been generated by the use of web technologies. The association between different devices and services have also been explored to wisely and widely use recent technologies. Due to the restriction in the available resources, the chance of security violation is increasing highly on the constrained devices. IoT backend with the multi-cloud infrastructure to extend the public services in terms of better scalability and reliability. Several users might access the multi-cloud resources that lead to data threats while handling user requests for IoT services. It poses a new challenge in proposing new functional elements and security schemes. In this paper, an intelligent Intrusion Detection Framework (IDF) is introduced to detect network and application-based attacks. The proposed framework has three phases: data pre-processing, feature selection and classification. Initially, the collected datasets are pre-processed using Integer- Grading Normalization (I-GN) technique that ensures a fair-scaled data transformation process. Secondly, Opposition-based Learning- Rat Inspired Optimizer (OBL-RIO) is designed for the feature selection phase. The progressive nature of rats chooses the significant features. The fittest value ensures the stability of the features from OBL-RIO. Finally, a 2D-Array-based Convolutional Neural Network (2D-ACNN) is proposed as the binary class classifier. The input features are preserved in a 2D-array model to perform on the convoluted set of layers. It detects the normal (or) abnormal traffic. The proposed framework is trained and tested on the Netflow-based datasets. The proposed framework yields 95.20% accuracy, 2.5% false positive rate and 97.24% detection rate.

Recurrent Neural Network (RNN) Inference Cloud Computing

“…The article conducts experiments using real-world datasets from mobile gaming platforms to demonstrate how well the proposed technique captures and predicts resource usage behaviors. The author Syed Mohamed Thameem Nizamudeen(2023) of a technical research study [24] proposes a revolutionary intrusion detection system made for multi-cloud and Internet of Things (IoT) scenarios. It uses a swarm-based deep learning classifier to increase the accuracy and efficacy of intrusion detection in this dynamic and heterogeneous context.…”

Enhancing Intrusion Detection through Unsupervised Deep Learning Models

“…Nizamudeen [13] employed integer-grading normalization (I-GN) for data preprocessing and used opposition-based learning (OBL)-rat inspired optimizer (RIO) for feature selection to retain important features. Experiments on a combined dataset (NF-UQ-NIDS) showed improved detection accuracy compared to other state-of-the-art methods.…”