Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
Articles 1 - 5 of 5
Full-Text Articles in Computer Sciences
Malware Analysis On Android Using Supervised Machine Learning Techniques, Md Shohel Rana, Andrew H. Sung
Malware Analysis On Android Using Supervised Machine Learning Techniques, Md Shohel Rana, Andrew H. Sung
Faculty Publications
In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a …
A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur
A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur
Faculty Publications
In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on …
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) …