State of the art literature review on Network Anomaly Detection
Bodström, T., & Hämäläinen, T. (2018). State of the art literature review on Network Anomaly Detection. In O. Galinina, S. Andreev, S. Balandin, & Y. Koucheryavy (Eds.), NEW2AN : Proceedings of the 18th International Conference on Next Generation Wired/Wireless Advanced Networks and Systems (pp. 89-101). Springer. Lecture Notes in Computer Science, 11118. https://doi.org/10.1007/978-3-030-01168-0_9
Published inLecture Notes in Computer Science
© Springer Nature Switzerland AG 2018
As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additionally, the expected number of connected devices as well as the fast-paced development caused by the Internet of Things, raises huge risks in cyber security that must be dealt with accordingly. When considering all above-mentioned reasons, there is no doubt that there is plenty of room for more advanced methods in network anomaly detection hence more advanced statistical methods and machine learning based techniques have been proposed recently in detecting anomalies. ...
Parent publication ISBN978-3-030-01167-3
ConferenceInternational Conference on Next Generation Wired/Wireless Advanced Networks and Systems
Is part of publicationNEW2AN : Proceedings of the 18th International Conference on Next Generation Wired/Wireless Advanced Networks and Systems
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Bodström, Tero; Hämäläinen, Timo (Springer, 2018)As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems ...
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets Vahdani Amoli, Payam (University of Jyväskylä, 2015)Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast ...
Juvonen, Antti (University of Jyväskylä, 2014)
Bodström, Tero; Hämäläinen, Timo (MDPI AG, 2019)We present a novel Deep Learning (DL) stack for detecting Advanced Persistent threat (APT) attacks. This model is based on a theoretical approach where an APT is observed as a multi-vector multi-stage attack with a ...
Kumar, Sanjay; Viinikainen, Ari; Hämäläinen, Timo (IEEE, 2018)Mobile malware attacks increased three folds in the past few years and continued to expand with the growing number of mobile users. Adversary uses a variety of evasion techniques to avoid detection by traditional systems, ...