Packet Inspection to Identify Network Layer Attacks Using Machine Learning

Abstract

Abstract: With the advent of technology, computer networks have become an integral part of our lives. However, with the increase in the use of computer networks, network attacks have also increased. Network attacks can cause serious harm to network infrastructure, resulting in loss of data, financial loss, and reputation damage. In this paper, we propose a machine learning algorithm to analyze network packets to detect malicious activities that are aimed at disrupting the normal functioning of a network. Network layer attacks typically target the underlying infrastructure of a network, which includes the IP layer, transport layer, and network access layer. These attacks can cause various problems such as denial of service, data theft, and network downtime. To detect network layer attacks, the proposed approach involves extracting features from network packets. Features refer to the attributes or characteristics of a packet, such as the source and destination IP addresses, protocol type, payload size, etc. These features are then used to train a machine learning model to identify various network layer attacks such as DDoS, ARP spoofing, and ICMP attacks. The machine learning model uses these features to learn the patterns and behaviours of these attacks and uses this knowledge to detect them in real-time. The proposed approach is evaluated using various metrics such as accuracy, precision, recall, and F1-score. The evaluation results demonstrate that the proposed approach is effective in detecting network layer attacks and outperforms existing approaches. The proposed approach can be used to enhance network security and prevent network attacks.