Dual-stage detection of MITM attacks using PCA and ensemble machine learning Saswati Chatterjee, Mukesh Choudhary, Rinkal Dharmesh Sarvaiya, Saumil B Trivedi, Sumit Kumar Soni, Mohit Waghela Connecting Intelligence Trends in Computation and Data Communication, 2026 The study looks at the growing danger of Man-in-the-Middle (MITM) attacks alongside network security developments. Even with modern security, finding who started the attack is not easy. This work introduces an approach using machine learning to identify and analyze MITM attacks in a refined manner. It creates a dual detection process: PCA is first used to simplify data, and then a selection of predictive models, such as Support Vector Machines (SVM), Random Forest, XG-Boost, and Gradient Boosting, is used to classify the attacks identified by principal component analysis (PCA). Collected and analyzed data is supported using Wireshark, Xplico, and SNORT. The trial results show that the method is reliable at spotting MITM attacks. The use of digital forensics and machine learning, supported by reducing dimensionality in the method, helps boost the reliable and precise detection of threats on networks, dealing with the current issues of tracking and stopping MITM attacks. This research also emphasizes the growing complexity of internet traffic, which necessitates more adaptive and intelligent security solutions. By leveraging big data and machine learning, the framework aims to strengthen digital forensic capabilities and proactively respond to sophisticated cyber threats.
MACHINE LEARNING TECHNIQUES FOR IDENTIFYING DDOS ATTACKS IN CLOUD COMPUTING BACKGROUND Saswati Chatterjee, Mukesh Choudhary, Rinkal Dharmesh Sarvaiya, M. D. Faruk Abdulla Iet Conference Proceedings, 2025 In a Denial of Service (DoS) attack, malicious actors aim to disrupt the normal functioning of network infrastructure, preventing legitimate programs from establishing connections. This form of cyberwarfare involves inundating the targeted system with a substantial volume of traffic, depleting its resources, and causing network congestion. Unlike other cyber threats, the objective here is not to compromise sensitive data or infiltrate credential files but to render the victim system inaccessible. The attackers exploit the difficulty of managing an overwhelming volume of data traffic, hindering the successful delivery of packets to their intended destinations. Typically, network connections involve a three-way handshake process. The client initiates the connection by sending a request to the server, which, in turn, allocates capacity within its reservoir connection. In this paper, we have designed a DDoS detection system based on the C.4.5 algorithm, Naïve Bayes, and K-NN classifier to prevent the DDoS threat. This algorithm, combined with signature detection techniques, produces a decision tree to accomplish automatic, effective detection of signature attacks for DDoS flooding attacks. To evaluate the system, we formulated additional machine-learning methodologies related to them to obtain the desired results.
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