1. Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network
- Author
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Emrah Tufan, Cihangir Tezcan, and Cengiz Acarturk
- Subjects
I.2 ,FOS: Computer and information sciences ,Anomaly-based ,Computer Science - Cryptography and Security ,probing attacks ,General Computer Science ,C.4 ,Computer science ,Reliability (computer networking) ,02 engineering and technology ,Intrusion detection system ,Machine learning ,computer.software_genre ,Convolutional neural network ,Data modeling ,C.2 ,0202 electrical engineering, electronic engineering, information engineering ,Information system ,General Materials Science ,misuse-based ,business.industry ,General Engineering ,020206 networking & telecommunications ,Benchmarking ,Information security ,Ensemble learning ,intrusion detection systems ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 ,computer ,Cryptography and Security (cs.CR) - Abstract
Cyber attacks constitute a significant threat to organizations with implications ranging from economic, reputational, and legal consequences. As cybercriminals' techniques get sophisticated, information security professionals face a more significant challenge to protecting information systems. In today's interconnected realm of computer systems, each attack vector has a network dimension. The present study investigates network intrusion attempts with anomaly-based machine learning models to provide better protection than the conventional misuse-based models. Two models, namely an ensemble learning model and a convolutional neural network model, were built and implemented on a data set gathered from a real-life, institutional production environment. To demonstrate the models' reliability and validity, they were applied to the UNSW-NB15 benchmarking data set. The type of attack was limited to probing attacks to keep the scope of the study manageable. The findings revealed high accuracy rates, the CNN model being slightly more accurate., Comment: 15 pages, published in IEEE Access 2021, https://ieeexplore.ieee.org/document/9387304
- Published
- 2021
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