Relevant bibliographies by topics / Computer networks Outliers (Statistics) Anomaly detection (Computer security)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters

Academic literature on the topic 'Computer networks Outliers (Statistics) Anomaly detection (Computer security)'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Computer networks Outliers (Statistics) Anomaly detection (Computer security)"

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Wang, Yajing, Juan Ma, Ashutosh Sharma, et al. "An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks." Journal of Sensors 2021 (May 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/5558860.

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Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and pe
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Abuadlla, Yousef, Goran Kvascev, Slavko Gajin, and Zoran Jovanovic. "Flow-based anomaly intrusion detection system using two neural network stages." Computer Science and Information Systems 11, no. 2 (2014): 601–22. http://dx.doi.org/10.2298/csis130415035a.

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Computer systems and networks suffer due to rapid increase of attacks, and in order to keep them safe from malicious activities or policy violations, there is need for effective security monitoring systems, such as Intrusion Detection Systems (IDS). Many researchers concentrate their efforts on this area using different approaches to build reliable intrusion detection systems. Flow-based intrusion detection systems are one of these approaches that rely on aggregated flow statistics of network traffic. Their main advantages are host independence and usability on high speed networks, since the m
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Dissertations / Theses on the topic "Computer networks Outliers (Statistics) Anomaly detection (Computer security)"

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Liu, Ying. "Outlier detection by network flow." Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2007p/liu-ying.pdf.

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Thesis (Ph. D.)--University of Alabama at Birmingham, 2007.<br>Additional advisors: Elliot J. Lefkowitz, Kevin D. Reilly, Robert Thacker, Chengcui Zhang. Description based on contents viewed Feb. 7, 2008; title from title screen. Includes bibliographical references (p. 125-132).
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Gu, Guofei. "Correlation-based Botnet Detection in Enterprise Networks." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24634.

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Most of the attacks and fraudulent activities on the Internet are carried out by malware. In particular, botnets, as state-of-the-art malware, are now considered as the largest threat to Internet security. In this thesis, we focus on addressing the botnet detection problem in an enterprise-like network environment. We present a comprehensive correlation-based framework for multi-perspective botnet detection consisting of detection technologies demonstrated in four complementary systems: BotHunter, BotSniffer, BotMiner, and BotProbe. The common thread of these systems is correlation analysis,
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Alkadi, Alaa. "Anomaly Detection in RFID Networks." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/768.

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Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing
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Book chapters on the topic "Computer networks Outliers (Statistics) Anomaly detection (Computer security)"

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Duy, Phan The, Nghi Hoang Khoa, Hoang Hiep, et al. "A Deep Transfer Learning Approach for Flow-Based Intrusion Detection in SDN-Enabled Network." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210031.

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Revolutionizing operation model of traditional network in programmability, scalability, and orchestration, Software-Defined Networking (SDN) has considered as a novel network management approach for a massive network with heterogeneous devices. However, it is also highly susceptible to security attacks like conventional network. Inspired from the success of different machine learning algorithms in other domains, many intrusion detection systems (IDS) are presented to identify attacks aiming to harm the network. In this paper, leveraging the flow-based nature of SDN, we introduce DeepFlowIDS, a deep learning (DL)-based approach for anomaly detection using the flow analysis method in SDN. Furthermore, instead of using a lot of network properties, we only utilize essential characteristics of traffic flows to analyze with deep neural networks in IDS. This is to reduce the computational and time cost of attack traffic detection. Besides, we also study the practical benefits of applying deep transfer learning from computer vision to intrusion detection. This method can inherit the knowledge of an effective DL model from other contexts to resolve another task in cybersecurity. Our DL-based IDSs are built and trained with the NSL-KDD and CICIDS2018 dataset in both fine-tuning and feature extractor strategy of transfer learning. Then, it is integrated with the SDN controller to analyze traffic flows retrieved from OpenFlow statistics to recognize the anomaly action in the network.
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