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TANDI: Threat assessment of network data and information

TANDI: Threat assessment of network data and information

引用

conference on Multisensor, Multisource Information fusion

作者： Holsopple, Jared Yang, Shanchieh Jay Sudit, Moises Rochester Inst Technol 83 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)0819462985

Current practice for combating cyber attacks typically use Intrusion Detection sensors (IDSs) to passively detect and block multi-stage attacks. This work leverages Level-2 fusion that correlates IDS alerts belonging to the same attacker, and proposes a threat assessment algorithm to predict potential future attacker actions. The algorithm, TANDI, reduces the problem complexity by separating the models of the attacker's capability and opportunity, and fuse the two to determine the attacker's intent. Unlike traditional Bayesian-based approaches, which require assigning a large number of edge probabilities, the proposed Level-3 fusion procedure uses only 4 parameters. TANDI has been implemented and tested with randomly created attack sequences. The results demonstrate that TANDI predicts future attack actions accurately as long as the attack is not part of a coordinated attack and contains no insider threats. In the presence of abnormal attack events, TANDI will alarm the network analyst for further analysis. The attempt to evaluate a threat assessment algorithm via simulation is the first in the literature, and shall open up a new avenue in the area of high level fusion.

关键词： information fusion threat assessment impact assessment cyber attacks

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Higher-level fusion for military operations based on abductive inference: Proof of principle

Higher-level fusion for military operations based on abducti...

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conference on Multisensor, Multisource Information fusion

作者： Pantaleev, Aleksandar Josephson, John Ohio State Univ Lab Artificial Intelligence Res 2015 Neil Ave Columbus OH 43210 USA

ISBN: (纸本)0819462985

The ability of contemporary military commanders to estimate and understand complicated situations already suffers from information overload, and the situation can only grow worse. We describe a prototype application that uses abductive inferencing to fuse information from multiple sensors to evaluate the evidence for higher-level hypotheses that are close to the levels of abstraction needed for decision making (approximately JDL levels 2 and 3). Abductive inference (abduction, inference to the best explanation) is a pattern of reasoning that occurs naturally in diverse settings such as medical diagnosis, criminal investigations, scientific theory formation, and military intelligence analysis. Because abduction is part of common-sense reasoning, implementations of it can produce reasoning traces that are very human understandable. Automated abductive inferencing can be deployed to augment human reasoning, taking advantage of computation to process large amounts of information, and to bypass limits to human attention and short-term memory. We illustrate the workings of the prototype system by describing an example of its use for small-unit military operations in an urban setting. Knowledge was encoded as it might be captured prior to engagement from a standard military decision making process (MDMP) and analysis of commander's priority intelligence requirements (PIR). The system is able to reasonably estimate the evidence for higher-level hypotheses based on information from multiple sensors. Its inference processes can be examined closely to verify correctness. Decision makers can override conclusions at any level and changes will propagate appropriately.

关键词： abduction abductive inference best-explanation reasoning multisource information fusion multisensor fusion higher-level fusion priority intelligence requirements PIR JDL level 2 JDL level 3

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Multi-source evidence fusion for cyber-situation assessment

Multi-source evidence fusion for cyber-situation assessment

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conference on Multisensor, Multisource Information fusion

作者： Sabata, Bikash Ornes, Chester Aginova Inc 7901 Henry AveB-109 Philadelphia PA 19128 USA Informat Extract & Transport Inc Arlington VA 22209 USA

ISBN: (纸本)0819462985

Novel methods of detecting cyber attacks on networks have been developed that are able to detect an increasing diverse variety of malicious cyber-events. However, this has only resulted in additional information burden on the network analyst. The integration of the distributed evidence from multiple sources is missing or ad-hoc at best. Only with the fusion of the multi-source evidence can we reason at a higher semantic level to detect and identify attacks and attackers. Further, integration at a higher semantic level will reduce the cognitive load on the security officer and will make it possible for reasonable responses. This paper presents an overview of the D-Force system that uses a Bayesian Evidential Framework for fusing the multi-source evidence in a network to detect and recognize attacks. Attack hypothesis are generated as a result of evidence at the different network and host sensors. The hypotheses are verified or denied with additional evidence. Based on our initial experiments and tests the D-Force system promises to be a powerful tool in the information security officers arsenal.

关键词： cyber situation assessment intrusion detection cyber attacks

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Prediction of adverse outcomes of Acute Coronary Syndrome using intelligent fusion of triage information with HUMINT

Prediction of adverse outcomes of Acute Coronary Syndrome us...

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conference on Multisensor, Multisource Information fusion

作者： McCullough, Claire L. Novobilski, Andrew J. Fesmire, Francis M. Univ Tennessee 615 McCallie Ave Chattanooga TN 37403 USA UT Coll Med Erlanger Heart Stroke Ctr Chattanooga TN 37403 USA

ISBN: (纸本)0819462985

Faculty from the University of Tennessee at Chattanooga and the University of Tennessee College of Medicine, Chattanooga Unit, have used data mining techniques and neural networks to examine a set of fourteen features, data items, and HUMINT assessments for 2,148 emergency room patients with symptoms possibly indicative of Acute Coronary Syndrome. Specifically, the authors have generated Bayesian networks describing linkages and causality in the data, and have compared them with neural networks. The data includes objective information routinely collected during triage and the physician's initial case assessment, a HUMINT appraisal. Both the neural network and the Bayesian network were used to fuse the disparate types of information with the goal of forecasting thirty-day adverse patient outcome. This paper presents details of the methods of data fusion including both the data mining techniques and the neural network. Results are compared using Receiver Operating Characteristic curves describing the outcomes of both methods, both using only objective features and including the subjective physician's assessment. While preliminary, the results of this continuing study are significant both from the perspective of potential use of the intelligent fusion of biomedical informatics to aid the physician in prescribing treatment necessary to prevent serious adverse outcome from ACS and as a model of fusion of objective data with subjective HUMINT assessment. Possible future work includes extension of successfully demonstrated intelligent fusion methods to other medical applications, and use of decision level fusion to combine results from data mining and neural net approaches for even more accurate outcome prediction.

关键词： data mining neural networks Bayesian networks data fusion biomedical informatics

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A novel image fusion algorithm based on human vision system

A novel image fusion algorithm based on human vision system

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conference on Multisensor, Multisource Information fusion

作者： Miao Qiguang Wang Baoshu Xidian Univ Sch Comp Sci Xian 710071 Peoples R China Guilin Univ Electron Technol Guilin 541004 Peoples R China

ISBN: (纸本)0819462985

The proposed new fusion algorithm is based on the improved pulse coupled neural network(PCNN) model, the fundamental characteristics of images and the properties of human vision system. Compared with the traditional algorithm where the linking strength of each neuron is the same and its value is chosen through experimentation, this algorithm uses the contrast of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image taking part in the fusion. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. Furthermore, by this algorithm, other parameters, for example, Delta, the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do image fusion.

关键词： image fusion pulse-coupled neural network(PCNN) human vision system contrast linking strength fire mapping image

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sensor validation in non-destructive evaluation using clustering

Sensor validation in non-destructive evaluation using cluste...

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Smart Structures and Materials 2006 conference

作者： Avasarala, Viswanath Celaya, Jose R. Goebel, Kai Eklund, Neil Penn State Univ Coll IST University Pk PA 16801 USA

ISBN: (纸本)0819462209

Non-destructive evaluation (NDE) techniques for condition monitoring in remote solid structures have evolved vastly in the last few years. algorithms for estimation of sensor integrity and for noise correction form a crucial aspect of NDE. This paper presents a sensor validation approach that verifies sensor integrity, identifies and corrects noise effects and selects the best possible array of sensors for multi-sensor fusion. The proposed methodology uses a novel change detection algorithm for noise correction and a clustering algorithm to isolate useful signal information from the sensor data. It was used for sensor selection in a NDE field study, where multiple sensors were used to examine a solid structure. The methodology achieved 97% accuracy in the experiments, indicating its efficacy.

关键词： clustering sensor validation cusum method

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Overlapping coalition formation for efficient data fusion in multi-sensor networks

Overlapping coalition formation for efficient data fusion in...

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21st National conference on Artificial Intelligence and the 18th Innovative applications of Artificial Intelligence conference, AAAI-06/IAAI-06

作者： Dang, Viet Dung Dash, Rajdeep K. Rogers, Alex Jennings, Nicholas R. School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom

ISBN: (纸本)1577352815

This paper develops new algorithms for coalition formation within multi-sensor networks tasked with performing wide-area surveillance. Specifically, we cast this application as an instance of coalition formation, with overlapping coalitions. We show that within this application area sub-additive coalition valuations are typical, and we thus use this structural property of the problem to derive two novel algorithms (an approximate greedy one that operates in polynomial time and has a calculated bound to the optimum, and an optimal branch-and-bound one) to find the optimal coalition structure in this instance. We empirically evaluate the performance of these algorithms within a generic model of a multi-sensor network performing wide area surveillance. These results show that the polynomial algorithm typically generated solutions much closer to the optimal than the theoretical bound, and prove the effectiveness of our pruning procedure. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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Decision fusion strategy for target recognition in hyperspectral images

Decision fusion strategy for target recognition in hyperspec...

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conference on Unmanned/Unattended sensors and sensor Networks III

作者： Acito, N. Greco, M. Diani, M. Corsini, G. Dipartimento Ingn Informaz Via G Caruso 14 I-56122 Pisa Italy

ISBN: (纸本)9780819464927

Hyperspectral sensors allow a considerable improvement in the performance of a target recognition process to be achieved. This characteristic is particular interesting in a lot of military and civilian remote sensing applications, such as automatic target recognition (ATR) and surveillance of wide areas. In this framework, real time processing of the observed scenario is becoming a key issue, because it permits the operator to provide immediate assessment of the surveyed area. In the literature is presented a line-by-line real time implementation of the widely used Constrained Energy Minimization (CEM) target detector. However, experimental results show that sometimes the CEM filter produces False Alarms (FAs) corresponding to rare objects, whose spectra are angularly very different from the target signature and from the natural background classes in the image. A solution to such a problem is presented in this work: the proposed strategy is based on the decision fusion of the CEM and the SAM algorithms. Only those pixels that pass the CEM-stage are processed by the SAM algorithm. The second stage allows false alarms to be reduced by preserving most of target pixels. The fusion strategy is applied to an experimental hyperspectral data set to recognize a known green target. Detection performance is numerically evaluated and compared to the one of the classical CEM detector.

关键词： hyperspectral target recognition CEM SAM decision fusion real time implementation

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A QoS-based adaptive clustering algorithm for wireless sensor networks

A QoS-based adaptive clustering algorithm for wireless senso...

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IEEE International conference on Mechatronics and Automation

作者： Chen, Weike Li, Wenfeng Shou, Heng Yuan, Bing Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China Hunan Met Profess Tech Coll Dept Mech Engn Zhuzhou 412000 Peoples R China Hunan Railway Profess Tech Coll Dept Teaching Management Zhuzhou 412000 Peoples R China

ISBN: (纸本)1424404657

Existing hierarchical. routing algorithms for wireless sensor networks mainly focus on distributing energy load among all the nodes. They pay little attention to Quality of Services (QoS) support in wireless sensor networks. In this paper, we developed a algorithm, QAC (QoS-based Adaptive Clustering algorithm), which not only concerns energy consumption but also can improve the reliability and the steadiness of wireless sensor networks by establishing a dual cluster-head model. QAC proposes a local-centralized mechanism for electing cluster-head and suggests a parameter to measure the QoS support in hierarchical applications of WSNs. Our model can increase the reliability and the steadiness of wireless sensor network by distributing evenly the communication load and the load of data fusion among cluster-heads. The dual cluster-head model can also improve the survival ability of wireless sensor networks and makes the network fitting in with the fierce changes of environment.

关键词： wireless sensor networks hierarchical model QoS support routing algorithm data fusion

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Feature extraction and fusion for protein structure identification in cryo-electron microscopic images using Independent Component Analysis and the Projection-Slice Synthetic Filter

Feature extraction and fusion for protein structure identifi...

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conference on Multisensor, Multisource Information fusion

作者： Riasati, Vahid R. Braunreiter, Dennis Sci Applicat Int Corp 10260 Campus Point Dr San Diego CA 92121 USA

ISBN: (纸本)0819462985

In this paper we utilize the Projection-Slice Synthetic Discriminant Function Filters, PSDF, in concert with an Independent Component Analysis technique to simultaneously reduce the data set that represents each of the training images and to emphasize subtle differences in each of the training images. These differences are encoded into the PSDF in order to improve the filter sensitivity for the recognition and identification of protein images formed from a cryo-electron microscopic imaging process. The PSDF and Independent Component Analysis provide a premise not only for the identification of the class of structures under consideration, but also for detecting the orientation of the structures in these images. The protein structures found in cryo-electron microscopic imaging represent a class of objects that have low resolution and contrast and subtle variation. This poses a challenge in design of filters to recognize these structures due to false targets that often have similar characteristics as the protein structures. The incorporation of a component analysis and eigen values conditioning in forming the filter provides an enhanced approach of de-correlating images prior to their incorporation into the filter. We present our method of filter synthesis and the results of the application of this modified filter to a protein structure recognition problem.

关键词： Independent Component Analysis Projection-Slice Synthetic Discriminant Functions

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