A Hybrid Detection System for DDoS Attacks Based on Deep Sparse Autoencoder and Light Gradient Boost Machine

Abstract

In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.

Containerized cloud-based honeypot deception for tracking attackers

“…We executed a deep dive into DDOSPOT honeypot as it had been found to report the highest frequency of hits at both instances. A variety of automated tools and services offered by third-party suppliers for DDoS as a service make it simple to start DDoS attacks 47 , 48 . It is easy for even a beginner in attacking to initiate sophisticated DDoS assaults by using the key aspects of cloud technology.…”

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

“…Ensuring uniform feature map sizes is necessary for conducting feature fusion operations. Subsequently, the convolutional block attention module (CBAM) [5] is incorporated into the lateral connections to fine-tune the feature maps layer by layer. This integration aids in reducing false detections and enhancing feature extraction accuracy.…”

“…They might cause the loss of priceless sensitive data, such as hospital files, military records, etc. Furthermore, they can disable phone and computer Future Internet 2023, 15, 297 2 of 19 networks, making data unavailable or rendering systems unusable [5][6][7]. Banking and government networks are particularly vulnerable because of the tremendous value of the data they contain.…”

Network intrusion detection system using bacterial foraging optimization with random forest

“…The compressed form of the input is stored in the latent space. The AE technique [33] tries to recreate the input at the output to get comparable input and output, i.e., .…”