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ABIDAntibody Identification (test)
ABIDAustralian Bird Image Database
ABIDAl Baraka Investment and Development (Saudi Arabia; financial services company)
ABIDAnomaly-Based Intrusion Detection (mobile technology)
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References in periodicals archive ?
Two approaches for analyzing events in order to detect attacks [9]: misuse intrusion detection and anomaly-based intrusion detection are often combined to obtain better features than used separately [10].
[16] Prashant Kushwaha, Himanshu Buckchash, and Balasubramanian Raman, "Anomaly-Based Intrusion Detection Using Filter Based Feature Selection on KDD-CUP 99," Proceeding of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017.
Siris, and Nikolaos Petroulakis, "Anomaly-Based Intrusion Detection Algorithms for Wireless Networks", E.
Anomaly-based intrusion detection can be functionally categorized into three groups: statistical-based methods, knowledge-based methods, and machine learning based methods.
Sequeira and Zaki [29] proposed a method and named it ADMIT (anomaly-based intrusion detection technique) with use of the KDD dataset which achieved 80% detection rate with a 15% FAR ratio.
In nature, anomaly-based intrusion detection is a classification problem.
Anomaly-Based Intrusion Detection. The anomaly-based technique assumes that malicious activities are significantly different from expected behavior, and that can be studied quantitatively.
Anomaly-based intrusion detection systems tend to have high false positive rates.
Tylman, "Anomaly-based intrusion detection using Bayesian networks," in Proceedings of the International Conference on Dependability of Computer Systems, pp.
According to the method of analysis, these models can be divided into two categories: feature-based intrusion detection model and anomaly-based intrusion detection model.
Paper topics include vulnerability modeling for the analysis of network attacks, fuzzy strategies in modeling effects of technological implementations, dependability in e-learning systems, a true random number generator with attack detection, verification and implementation of dependable computers, Non-centralized key pre-distribution in networks, anomaly-based intrusion detection using Bayesian networks, aspects of maintenance of compiled programs, constraint and test verification in parallel database systems, and the dependability of fault-tolerant Adder structures.
[53] Aljawarneh, S., Aldwairi, M., Yassein, M.B, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model," Journal of Computational Science, 2017.