Intrusion detection in IoT networks using machine learning and deep learning approaches for MitM attack mitigation

The Internet of Things (IoT) revolutionizes connectivity but introduces significant security challenges, particularly concerning man-in-the-middle (MitM) attacks. These attacks, where adversaries intercept or alter communications, threaten the integrity and privacy of IoT networks due to the limited computational resources and security features of IoT devices. This study investigates machine learning and deep learning techniques to detect and mitigate MitM attacks in IoT environments. Three approaches, Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM), were assessed using a pre-prepared dataset that simulates network traffic and various attack scenarios. Random Forest achieved the highest accuracy (94%), combining robustness and balanced performance. LSTM excelled in analyzing sequential data with 92% accuracy, while SVM struggled with high false positive rates despite 85.7% accuracy. These findings highlight the potential of machine learning, particularly Random Forest and LSTM, to enhance IoT security. They pave the way for more resilient and effective frameworks for detecting and mitigating MitM attacks. The aggregate network of linked devices and the technology that enables communication between devices and the cloud, as well as between devices, are referred to as the Internet of Things, or IoT. We now have billions of devices connected to the internet because of the development of low-cost computer chips and high-bandwidth communications. This implies that commonplace appliances like vacuums, vehicles, machinery, and toothbrushes can employ sensors to gather information and react to consumers in a clever way. A man-in-the-middle (MITM) attack is a type of cyberattack where a hacker intercepts communications between two internet targets, like a user and a web service, in order to obtain confidential data. MITM attackers surreptitiously insert themselves into two-party interactions, then intercept private information including login credentials, account information, and credit card numbers. Hackers then utilize the data to carry out additional online crimes like identity theft, financial account takeover, and unlawful transactions. An MITM attacker may intercept private conversations between two individuals in addition to user-application exchanges. In order to manage the conversation, the attacker in this scenario redirects and passes communications between the two parties, occasionally changing or replacing messages. In order to identify and lessen MitM assaults in IoT contexts, this study explores machine learning and deep learning approaches. Using a pre-made dataset that mimics network traffic and different attack situations, three methods—Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM)—were evaluated. Random Forest combined robustness and balanced performance to attain the best accuracy of 94%. While SVM struggled with significant false positive rates despite 85.7% accuracy, LSTM performed exceptionally well in evaluating sequential data with 92% accuracy. These results demonstrate how machine learning, specifically Random Forest and LSTM, may improve IoT security. They open the door to frameworks for mitigating and detecting MitM assaults that are more robust and efficient. The aggregate network of linked devices and the technology that enables communication between devices and the cloud, as well as between devices, are referred to as the Internet of Things, or IoT. We now have billions of devices connected to the internet because of the development of low-cost computer chips and high-bandwidth communications. This implies that commonplace appliances like vacuums, vehicles, machinery, and toothbrushes can employ sensors to gather information and react to consumers in a clever way. A man-in-the-middle (MITM) attack is a type of cyberattack where a hacker intercepts communications between two internet targets, like a user and a web service, in order to obtain confidential data. MITM attackers surreptitiously insert themselves into two-party interactions, then intercept private information including login credentials, account information, and credit card numbers. Hackers then utilize the data to carry out additional online crimes like identity theft, financial account takeover, and unlawful transactions. An MITM attacker may intercept private conversations between two individuals in addition to user-application exchanges. In order to manage the conversation, the attacker in this scenario redirects and passes communications between the two parties, occasionally changing or replacing messages. In order to identify and lessen MitM assaults in IoT contexts, this study explores machine learning and deep learning approaches. Using a pre-made dataset that mimics network traffic and different attack situations, three methods—Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM)—were evaluated. Random Forest combined robustness and balanced performance to attain the best accuracy of 94%. While SVM struggled with significant false positive rates despite 85.7% accuracy, LSTM performed exceptionally well in evaluating sequential data with 92% accuracy. These results demonstrate how machine learning, specifically Random Forest and LSTM, may improve IoT security. They open the door to frameworks for mitigating and detecting MitM assaults that are more robust and efficient.