Network Security: Threat Model, Attacks, and IDS Using Machine Learning
| Autor: | Anupriya Sharma, Nidhi Mehra, Atika Gupta, Sudhanshu Maurya, Divya Kapil |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Network security Big data Entertainment industry 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Naive Bayes classifier Statistical classification 020901 industrial engineering & automation Threat model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Computer technology |
| Zdroj: | 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). |
| DOI: | 10.1109/icais50930.2021.9395884 |
| Popis: | Nowadays, computer technology has become necessary in our day-to-day life in various aspects such as communication, entertainment, education, banking, etc. In the digital era Network, security is essential, and the most challenging issue is identifying the intrusion attacks. An intrusion Detection System is a technique that monitors the network for anomalous activities and when these actions are discovered, then it generates an alert. An intrusion Detection System analyses big data due to heavy traffic and it protects data and computer networks from malicious actions. So, a fast and efficient classification technique is required to classify the normal and suspicious activities. For intrusion detection, various techniques have come into existence that leverage the machine learning approach. Various machine learning-based IDS techniques are described and categorized in this paper. Also, this research work presents a threat model in various networking layers. For experimental analysis, the NSL_KDD dataset are used and Naive Bayes, Random forest, and J 48 classification algorithms are used and the results are shown for TPR, precision FPR, F-measure, recall parameters. |
| Databáze: | OpenAIRE |
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