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 (CNN-LSTM) 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.
Highlights
Due to an increase in cybercrime, researchers have been focusing on identifying intrusions in networks [1]
Traditional computer networks and personal computers were the focus of cyberattacks, but communication infrastructure encompasses the internet of things (IoT) [3], the internet of connected vehicles [5], the internet of medical things [4] and 5G [7], these have become the targets of many cyberattacks
This study presents the convolutional neural network and long short-term memory (CNNLSTM) model to detect and classify BASHLITE and Mirai attacks of security camera devices connected to IoT applications
Summary
Due to an increase in cybercrime, researchers have been focusing on identifying intrusions in networks [1]. Grouping strategies is a machine learning technique to solve intrusion detection and prediction problems. Many machine learning algorithms have been used to detect cyberattacks, but these fail the sanctity of big data traffic. Zhou et al proposed the M-AdaBoost-A algorithm for predicting network intrusion, and they used methods such as POS to group several classifiers according to M-AdaBoost-A. Their method has achieved good performance in detecting intrusions in both wireless and traditional networks [48]
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