Conference Papers

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    Techniques to Secure Address Resolution Protocol
    (Institute of Electrical and Electronics Engineers Inc., 2020) Selvarajan, S.; Mohan, M.; Chandavarkar, B.R.
    Address Resolution Protocol was developed to create a standard for translating IP addresses to physical addresses. ARP takes (IP, Protocol) as input and converts to physical address. ARP can be easily spoofed because it lacks security. The inventors of ARP thought that internal to the network threats were minimum, and ARP had to be simple for its efficient and dynamic working. A machine in the network, which can work at the data link layer, can be easily spoofed because of the vulnerability in ARP protocol, leading to a man-in-the-middle attack. Securing ARP is not an easy task because state information should be preserved for authentication of ARP frames. However, the protocol is stateless, and making changes to the ARP protocol itself is not practical since the protocol is currently being widely used. Our objective in this paper is to provide a solution to detect and mitigate ARP spoofing attacks without any changes to the protocol itself. The proposed system provides improvement to an existing solution using ICMP to detect ARP spoofing. © 2020 IEEE.
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    Deep Learning based framework for dynamic Detection and Mitigation of ARP Spoofing attacks
    (Institute of Electrical and Electronics Engineers Inc., 2023) Puram, H.; Kumar, R.; Chandavarkar, B.R.
    Address Resolution Protocol (ARP) is a protocol that links the IP address of a network node to the Media Access Control (MAC) address of another node for communication. An attack known as ARP spoofing affects a network's data-link layer and permits malicious access to network data. The sending device can be tricked, and potentially valuable data can be stolen, by connecting the attacker's MAC address to the IP address of the receiving device. Several approaches exist today to detect ARP attacks accurately and efficiently but have drawbacks in various aspects such as speed of detection, accuracy, dynamicity, and scalability. To overcome these issues, we propose DL-ARP, a novel dynamic framework based on an XGBoost Classifier followed by a CNN-LSTM architecture. This technique can identify and mitigate ARP spoofing assaults in real-time by collecting packets of data as they are received. The model automatically categorizes them and creates entry cache logs in the process. This paper aims to show the effectiveness and the potential of the suggested methodology for real-time ARP spoofing detection and prevention, this study also assess the performance of the proposed methodology in comparison to other existing methods. © 2023 IEEE.