scholarly journals Integral Misuse and Anomaly Detection and Prevention System

10.5772/15439 ◽  
2011 ◽  
Author(s):  
Yoseba K. ◽  
Igor Ruiz-Agndez ◽  
Pablo G.
Keyword(s):  
Anomaly Detection ◽  
Prevention System

scholarly journals Cyber anomaly detection: Using tabulated vectors and embedded analytics for efficient data mining

2018 ◽  
Vol 12 (4) ◽  
pp. 293-310 ◽  
Author(s):  
Robert J Gutierrez ◽  
Kenneth W Bauer ◽  
Bradley C Boehmke ◽  
Cade M Saie ◽  
Trevor J Bihl
Keyword(s):  
Anomaly Detection ◽  
Mahalanobis Distance ◽  
High Velocity ◽  
Graphical Analysis ◽  
Cyber Forensics ◽  
Prevention System ◽  
Efficient Data ◽  
Detection Capabilities ◽  
Gain Access ◽  
Vector Approach

Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of analysts to find events for cyber forensics analysis. A tabulated vector approach is proposed to create meaningful state vectors from time-oriented blocks. Multivariate and graphical analysis is then used to analyze state vectors in human–machine collaborative interface. Statistical tools, such as the Mahalanobis distance, factor analysis, and histogram matrices, are employed for outlier detection. This research also introduces the breakdown distance heuristic as a decomposition of the Mahalanobis distance, by indicating which variables contributed most to its value. This work further explores the application of the tabulated vector approach methodology on collected firewall logs. Lastly, the analytic methodologies employed are integrated into embedded analytic tools so that cyber analysts on the front-line can efficiently deploy the anomaly detection capabilities.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors
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