Hybrid deep-learning model to detect botnet attacks over internet of things environments

Abstract

In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

The Making of Smart Campus: A Review and Conceptual Framework

“…Finally, the review on the governance domain revealed that cybersecurity development is reaching a heightened level of complex dynamics [110] to counter advanced attacks through deep learning algorithms [115] and blockchain technology [116]. These technologies provide a robust security system needed to protect the data governance function for the information framework in a university [117].…”

“…Cybersecurity has emerged swiftly along with the surge in IoT prompting an urgent challenge for all institutions and business organizations and is growing [110]. Smart campus development involves significant informatization, emphasizing the need for effective cybersecurity [111].…”

Botnet Attack Detection in IoT Devices using Ensemble Classifiers with Reduced Feature Space

“…Hasan et al (2022) [25] had used LSTM which had overcome the limited learning in RNN and in addition to that they have combined DNN & LSTM together which performed with 99.4% detection accuracy. Rust-Nguyen, M. Stamp, (2022) [26] up to 97.3% [31]. Jiyeon et al (2020) [32] Used CNN AND LSTM.…”

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

“…Next, the sensing data was executed to network trained which forecasts the intermediate attack. The authors in [14] present a robust scheme specially for helping detect botnet attacks of IoT devices. It can be complete by innovatively integrating the method of CNN-LSTM technique progress for detecting 2 general and serious IoT attacks (BASHLITE and Mirai) on 4 kinds of security cameras.…”