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Full-Text Articles in Computer Sciences
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Theses and Dissertations
Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …
Applicability Of Latent Dirichlet Allocation To Multi-Disk Search, George E. Noel, Gilbert L. Peterson
Applicability Of Latent Dirichlet Allocation To Multi-Disk Search, George E. Noel, Gilbert L. Peterson
Faculty Publications
Digital forensics practitioners face a continual increase in the volume of data they must analyze, which exacerbates the problem of finding relevant information in a noisy domain. Current technologies make use of keyword based search to isolate relevant documents and minimize false positives with respect to investigative goals. Unfortunately, selecting appropriate keywords is a complex and challenging task. Latent Dirichlet Allocation (LDA) offers a possible way to relax keyword selection by returning topically similar documents. This research compares regular expression search techniques and LDA using the Real Data Corpus (RDC). The RDC, a set of over 2400 disks from real …
Using Plsi-U To Detect Insider Threats By Datamining Email, James S. Okolica, Gilbert L. Peterson, Robert F. Mills
Using Plsi-U To Detect Insider Threats By Datamining Email, James S. Okolica, Gilbert L. Peterson, Robert F. Mills
Faculty Publications
Despite a technology bias that focuses on external electronic threats, insiders pose the greatest threat to an organisation. This paper discusses an approach to assist investigators in identifying potential insider threats. We discern employees' interests from e-mail using an extended version of PLSI. These interests are transformed into implicit and explicit social network graphs, which are used to locate potential insiders by identifying individuals who feel alienated from the organisation or have a hidden interest in a sensitive topic. By applying this technique to the Enron e-mail corpus, a small number of employees appear as potential insider threats.
Multi-Class Classification Averaging Fusion For Detecting Steganography, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Multi-Class Classification Averaging Fusion For Detecting Steganography, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Faculty Publications
Multiple classifier fusion has the capability of increasing classification accuracy over individual classifier systems. This paper focuses on the development of a multi-class classification fusion based on weighted averaging of posterior class probabilities. This fusion system is applied to the steganography fingerprint domain, in which the classifier identifies the statistical patterns in an image which distinguish one steganography algorithm from another. Specifically we focus on algorithms in which jpeg images provide the cover in order to communicate covertly. The embedding methods targeted are F5, JSteg, Model Based, OutGuess, and StegHide. The developed multi-class steganalvsis system consists of three levels: (1) …
Detecting Potential Insider Threats Through Email Datamining, James S. Okolica
Detecting Potential Insider Threats Through Email Datamining, James S. Okolica
Theses and Dissertations
No abstract provided.
Efficient Generation Of Social Network Data From Computer-Mediated Communication Logs, Jason Wei Sung Yee
Efficient Generation Of Social Network Data From Computer-Mediated Communication Logs, Jason Wei Sung Yee
Theses and Dissertations
The insider threat poses a significant risk to any network or information system. A general definition of the insider threat is an authorized user performing unauthorized actions, a broad definition with no specifications on severity or action. While limited research has been able to classify and detect insider threats, it is generally understood that insider attacks are planned, and that there is a time period in which the organization's leadership can intervene and prevent the attack. Previous studies have shown that the person's behavior will generally change, and it is possible that social network analysis could be used to observe …
Using Sequence Analysis To Perform Application-Based Anomaly Detection Within An Artificial Immune System Framework, Larissa A. O'Brien
Using Sequence Analysis To Perform Application-Based Anomaly Detection Within An Artificial Immune System Framework, Larissa A. O'Brien
Theses and Dissertations
The Air Force and other Department of Defense (DoD) computer systems typically rely on traditional signature-based network IDSs to detect various types of attempted or successful attacks. Signature-based methods are limited to detecting known attacks or similar variants; anomaly-based systems, by contrast, alert on behaviors previously unseen. The development of an effective anomaly-detecting, application based IDS would increase the Air Force's ability to ward off attacks that are not detected by signature-based network IDSs, thus strengthening the layered defenses necessary to acquire and maintain safe, secure communication capability. This system follows the Artificial Immune System (AIS) framework, which relies on …
Data Mining Feature Subset Weighting And Selection Using Genetic Algorithms, Okan Yilmaz
Data Mining Feature Subset Weighting And Selection Using Genetic Algorithms, Okan Yilmaz
Theses and Dissertations
We present a simple genetic algorithm (sGA), which is developed under Genetic Rule and Classifier Construction Environment (GRaCCE) to solve feature subset selection and weighting problem to have better classification accuracy on k-nearest neighborhood (KNN) algorithm. Our hypotheses are that weighting the features will affect the performance of the KNN algorithm and will cause better classification accuracy rate than that of binary classification. The weighted-sGA algorithm uses real-value chromosomes to find the weights for features and binary-sGA uses integer-value chromosomes to select the subset of features from original feature set. A Repair algorithm is developed for weighted-sGA algorithm to guarantee …