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Search Results (2)

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Keywords = intrusion scenario discovery

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17 pages, 4021 KiB  
Article
Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
by Kai Zhang, Fei Zhao, Shoushan Luo, Yang Xin, Hongliang Zhu and Yuling Chen
Appl. Sci. 2020, 10(7), 2596; https://doi.org/10.3390/app10072596 - 10 Apr 2020
Cited by 4 | Viewed by 2818
Abstract
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not [...] Read more.
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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16 pages, 1014 KiB  
Article
Inefficiency of IDS Static Anomaly Detectors in Real-World Networks
by Edward Guillen, Jeisson Sánchez and Rafael Paez
Future Internet 2015, 7(2), 94-109; https://doi.org/10.3390/fi7020094 - 6 May 2015
Cited by 7 | Viewed by 8310
Abstract
A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at [...] Read more.
A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method is employed to generate detectors, some classes of new detectors are created. Thereafter, detectors are supposed to be deployed in real network environments in order to achieve detection with good results for false positives and detection rates. Since the traffic behavior is quite different according to the user’s network activities over available services, restrictions and applications, it is supposed that behavioral-based detectors are not well suited to all kind of networks. This paper presents the differences of detection results between some network scenarios by applying traditional detectors that were calculated with artificial neural networks. The same detector is deployed in different scenarios to measure the efficiency or inefficiency of static training detectors. Full article
(This article belongs to the Special Issue Internet Security)
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