Intrusion detection system: a python-based approach for network security

The internet-of-things device is constantly changing and so is the nature of cyber-attacks. Therefore, the security of the infrastructure of a network is a severe issue affecting organizations globally. IDS should identify and prevent malicious activity that affects the security of a network. The paper presents an IDS system based on Python, implemented using Scapy and Pyshark libraries, to identify and inspect network anomalies pertaining to port scanning, SYN flood attacks, and unauthorized attempts. The system processes real-time network traffic, identifies packet-level data, and uses both rule-based and anomaly-based detection algorithms to discern any potential threats. The system uses lightweight Python libraries and gives an efficient, adaptable, and cost-effective solution for small and medium-sized networks while shunning the resource-intensive nature of traditional IDS tools like Snort and Suricata, which often require consuming large computational resources and expertise. This performance analysis uses large-scale simulations and allows the use of real-world datasets, such as the CICIDS2017 dataset, to deliver a high detection rate without many false positives. The system’s modularity also opens up avenues for further improvement in the future in terms of adding machine learning algorithms to support more complex anomaly detection. This work contributes to the growing lightweight, open-source IDS solutions and provides a very practical and scalable solution that can enhance network security in resource-scarce environments.