Generalized Evidential Processing in Multiple Simultaneous Threat Detection in UNIX

2012 ◽  
pp. 104-120
Author(s):  
Zafar Sultan ◽  
Paul Kwan
Keyword(s):  
Data Fusion ◽  
Detection System ◽  
Set Cover ◽  
Threat Detection ◽  
Fusion Model ◽  
Detection Systems ◽  
Size Groups ◽  
On Line ◽  
And Performance ◽  
Statistical Data Fusion

In this paper, a hybrid identity fusion model at decision level is proposed for Simultaneous Threat Detection Systems. The hybrid model is comprised of mathematical and statistical data fusion engines; Dempster Shafer, Extended Dempster and Generalized Evidential Processing (GEP). Simultaneous Threat Detection Systems improve threat detection rate by 39%. In terms of efficiency and performance, the comparison of 3 inference engines of the Simultaneous Threat Detection Systems showed that GEP is the better data fusion model. GEP increased precision of threat detection from 56% to 95%. Furthermore, set cover packing was used as a middle tier data fusion tool to discover the reduced size groups of threat data. Set cover provided significant improvement and reduced threat population from 2272 to 295, which helped in minimizing the processing complexity of evidential processing cost and time in determining the combined probability mass of proposed Multiple Simultaneous Threat Detection System. This technique is particularly relevant to on-line and Internet dependent applications including portals.

Generalized Evidential Processing in Multiple Simultaneous Threat Detection in UNIX

2010 ◽  
Vol 2 (2) ◽  
pp. 51-67
Author(s):  
Zafar Sultan ◽  
Paul Kwan
Keyword(s):  
Data Fusion ◽  
Detection System ◽  
Set Cover ◽  
Threat Detection ◽  
Fusion Model ◽  
Data Set ◽  
Detection Systems ◽  
Processing Cost ◽  
Size Groups ◽  
And Performance

In this paper, a hybrid identity fusion model at decision level is proposed for Simultaneous Threat Detection Systems. The hybrid model is comprised of mathematical and statistical data fusion engines; Dempster Shafer, Extended Dempster and Generalized Evidential Processing (GEP). Simultaneous Threat Detection Systems improve threat detection rate by 39%. In terms of efficiency and performance, the comparison of 3 inference engines of the Simultaneous Threat Detection Systems showed that GEP is the better data fusion model. GEP increased precision of threat detection from 56% to 95%. Furthermore, set cover packing was used as a middle tier data fusion tool to discover the reduced size groups of threat data. Set cover provided significant improvement and reduced threat population from 2272 to 295, which helped in minimizing the processing complexity of evidential processing cost and time in determining the combined probability mass of proposed Multiple Simultaneous Threat Detection System. This technique is particularly relevant to on-line and Internet dependent applications including portals.

scholarly journals Statistical Data Fusion for Cross-Tabulation

1997 ◽  
Vol 34 (4) ◽  
pp. 485-498 ◽  
Author(s):  
Wagner A. Kamakura ◽  
Michel Wedel
Keyword(s):  
Data Fusion ◽  
Statistical Data ◽  
Statistical Tests ◽  
Complete Data ◽  
Discrete Variables ◽  
Fusion Model ◽  
Multiple Imputations ◽  
Tests Of Association ◽  
Cross Tabulation ◽  
Statistical Data Fusion

The authors address the situation in which a researcher wants to cross-tabulate two sets of discrete variables collected in independent samples, but a subset of the variables is common to both samples. The authors propose a statistical data-fusion model that allows for statistical tests of association using multiple imputations. The authors illustrate this approach with an application in which they compare the cross-tabulation results from fused data with those obtained from complete data. Their approach is also compared to the traditional hot-deck procedure.

A dynamic weighted algorithm for multiple radar fusion in an active protection system

2019 ◽  
pp. 154851291989092
Author(s):  
Jinli Wang ◽  
Haiping Song ◽  
Riming Chen ◽  
Yaning Zhang
Keyword(s):  
Detection System ◽  
Radar Data ◽  
Weighting Factor ◽  
Protection System ◽  
Threat Detection ◽  
Fusion Model ◽  
Time Dynamic ◽  
Weighted Fusion ◽  
Active Protection ◽  
Weighted Algorithm

The threat detection system based on short-range radars is an essential part of the active protection system (APS) of armored vehicles. The multi-radar data fusion problem is one of the crucial issues in the APS. Firstly, a general algorithm for multi-radar coordinates transformation is given. Then, based on the weighted fusion model and the trajectory characteristics of targets in the APS, a real-time dynamic weighting factor derivation algorithm is proposed. The algorithm is simulated in a dual-radar threat tracking and ballistic prediction scenario. The results prove the correctness and effectiveness of the algorithm.

An Insider Threat Detection Method Based on Business Process Mining

2017 ◽  
Vol 13 (2) ◽  
pp. 83-98 ◽  
Author(s):  
Taiming Zhu ◽  
Yuanbo Guo ◽  
Ankang Ju ◽  
Jun Ma ◽  
Xuan Wang
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Detection System ◽  
Detection Methods ◽  
Insider Threat ◽  
Threat Detection ◽  
Detection Algorithms ◽  
Business Cases ◽  
Business Process Mining ◽  
And Performance

Current intrusion detection systems are mostly for detecting external attacks, but the “Prism Door” and other similar events indicate that internal staff may bring greater harm to organizations in information security. Traditional insider threat detection methods only consider the audit records of personal behavior and failed to combine it with business activities, which may miss the insider threat happened during a business process. The authors consider operators' behavior and correctness and performance of the business activities, propose a business process mining based insider threat detection system. The system firstly establishes the normal profiles of business activities and the operators by mining the business log, and then detects specific anomalies by comparing the content of real-time log with the corresponding normal profile in order to find out the insiders and the threats they have brought. The relating anomalies are defined and the corresponding detection algorithms are presented. The authors have performed experimentation using the ProM framework and Java programming, with five synthetic business cases, and found that the system can effectively identify anomalies of both operators and business activities that may be indicative of potential insider threat.

Research of Automotive Controller Area Network Bus Detection System

2013 ◽  
Vol 662 ◽  
pp. 736-739
Author(s):  
Hong Wei Cui
Keyword(s):  
Data Fusion ◽  
Detection Method ◽  
Can Bus ◽  
Detection System ◽  
Fault Analysis ◽  
Controller Area Network ◽  
Failure Diagnosis ◽  
Area Network ◽  
Fusion Model ◽  
Work Condition

The detection method of automotive controller area network bus is studied in this paper. The composition of detection system is introduced. By analyzing and processing the data of CAN bus and sensors, work condition of automotive is achieved. Multi-pattern data fusion model and algorithm for failure diagnosis is researched. The detection system designed in this paper can be applied to automotive fault analysis, troubleshooting and maintenance.

scholarly journals Hybrid ModifiedK-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wathiq Laftah Al-Yaseen ◽  
Zulaiha Ali Othman ◽  
Mohd Zakree Ahmad Nazri
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Processing Time ◽  
Detection System ◽  
Network Size ◽  
Intrusion Detection Systems ◽  
Data Networks ◽  
Detection Accuracy ◽  
Detection Systems ◽  
And Performance

Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modifiedK-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modifiedK-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces the IDS processing time by up to 70%, while improving the detection accuracy.

Research of Intelligent On-Line Detection Equipment for Arresting Wires

2014 ◽  
Vol 577 ◽  
pp. 673-676
Author(s):  
Zheng Wang ◽  
Xing Dong Zhu ◽  
Jian Hua Song
Keyword(s):  
Detection System ◽  
Line Detection ◽  
Fast Detection ◽  
Detection Systems ◽  
Aircraft Landing ◽  
Detection Mode ◽  
On Line ◽  
Industry Ethernet ◽  
Current Detection ◽  
Detection Speed

The Arresting wires are the key assembly of aircraft landing system on the carrier, the most of all the current detection systems have not the ability of ration detection, and the precision of the system are low. For these deficiency, an on-line detection system for arresting wires was designed, the on-line automatic monitor algorithm, local fault (LF) algorithm were studied. The system detecting destructive by the on-scene detection mode, fast transferring the detection data to the distant PC by industry Ethernet, analyzing and processing the data by software in PC; the experiment shows that the system designed having the ability to ration assessment of LF, detection speed being fast, detection precision being high, and compliance to operational condition.

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