Machine Learning-Based Anomaly Detection for Man-in-the-Middle Attack Identification in Cloud Computing Infrastructures

Cloud computing is rapidly provisioning resources within a computer system, particularly processing power and data storage, without requiring direct user administration. Cyberattacks known as MITM (Man in the Middle) attacks occur when an attacker eavesdrops on communication between a client and a cloud server. The objective of a Man-in-the-Middle (MitM) attack is not only to obtain the victim's private data. Breach of confidentiality as per the data can be accomplished by intercepting, replaying, and modulation of the communication between the parties, especially to modify the data and gain unauthorized access to the systems involved. The attack can enable a host of undesirable actions, which range from identity theft, financial scams, and social disorder. Due to their extraordinary ingenuity, these network defects persist and obstruct safe traffic. the attackers alter their conduct dynamically to avoid detection. Critical aspects of cloud computing, such as man-in-the-middle attacks (MITM), are examined in this research, and a machine learning model utilizing K-Nearest Neighbours (KNN), Random Forest (RF), and Support Vector Machine (SVM) is being constructed to effectively identify these attacks. XG Boost is used to Train the model for better detection for selected features These models apply the existing datasets, and the result show an accuracy of 99. 17%”. %.