Abstract
Nowadays, with the immense amount of data that is circulating every second, the cybersecurity concerns are also growing. In recent years, cybersecurity-intrusion detection has become a very difficult research area in communication network and big data. Hence, traditional intrusion detection systems (IDSs) could not respond to the new security challenges. Therefore, IDSs require an effective and improved detection mechanism capable of detecting distributed intrusive activities and serious threats to network security. In this paper, we have proposed A Multi-Agent System (MAS), which is very suitable for IDSs as it meets the features required by the networks and Big Data issues, through cooperation, autonomy, and proactivity between agents to ensure the effective detection of intrusions without the intervention of an expert. Moreover, some experiments were conducted to evaluate the performance of our model in a Microsoft Azure Cloud, as it provides both processing power and storage capabilities using Apache Spark, and its Machine Learning Library (MLlib) to detect intrusions. A Random Forest algorithm is used to provide for the nature of the incoming data. Also, the use of the recent CSE-CIC-IDS2018 dataset will give better perspective about the system abilities against cyber-attacks. The results show that the proposed solution is much accurate than traditional intrusion detection systems.
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Tesnim, Y., Farah, J. (2021). A Multi-Agent-Based System for Intrusion Detection. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2021. Smart Innovation, Systems and Technologies, vol 241. Springer, Singapore. https://doi.org/10.1007/978-981-16-2994-5_15
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