Information Security Commons^™

Multivariate Fairness For Paper Selection, Reem Alsaffar

Graduate Theses and Dissertations

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select …

Analyzing And Estimating Cyberattack Trends By Performing Data Mining On A Cybersecurity Data Set, Chan Young Koh

Honors Program Theses and Projects

More than five billion personal information has been compromised over the past eight years through data breaches from notable companies, and the damage related to cybercrime is expected to reach six trillion USD annually by the year of 2021. Interestingly, recent cyberattacks were aimed specifically at credit agencies and companies that hold credit information of their customers and employees. The question is: “Why is it difficult to protect against or evade cyberattacks even for these prestigious companies?”. The purpose of this research is to bring the notion of notorious, rapidly-multiplying cyberthreats. Hence, the research focuses on analyzing cyberattack techniques and …

Intelligent Malware Detection Using File-To-File Relations And Enhancing Its Security Against Adversarial Attacks, Lingwei Chen

Graduate Theses, Dissertations, and Problem Reports

With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make …

Significant Permission Identification For Android Malware Detection, Lichao Sun

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

A recent report indicates that a newly developed malicious app for Android is introduced every 11 seconds. To combat this alarming rate of malware creation, we need a scalable malware detection approach that is effective and efficient. In this thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant …

Privacy Preserving Data Mining For Numerical Matrices, Social Networks, And Big Data, Lian Liu

Theses and Dissertations--Computer Science

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary.

First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help …

Analysis Into Developing Accurate And Efficient Intrusion Detection Approaches, Priya Rabadia, Craig Valli

Australian Digital Forensics Conference

Cyber-security has become more prevalent as more organisations are relying on cyber-enabled infrastructures to conduct their daily actives. Subsequently cybercrime and cyber-attacks are increasing. An Intrusion Detection System (IDS) is a cyber-security tool that is used to mitigate cyber-attacks. An IDS is a system deployed to monitor network traffic and trigger an alert when unauthorised activity has been detected. It is important for IDSs to accurately identify cyber-attacks against assets on cyber-enabled infrastructures, while also being efficient at processing current and predicted network traffic flows. The purpose of the paper is to outline the importance of developing an accurate and …

Reeling In Big Phish With A Deep Md5 Net, Brad Wardman, Gary Warner, Heather Mccalley, Sarah Turner, Anthony Skjellum

Journal of Digital Forensics, Security and Law

Phishing continues to grow as phishers discover new exploits and attack vectors for hosting malicious content; the traditional response using takedowns and blacklists does not appear to impede phishers significantly. A handful of law enforcement projects — for example the FBI's Digital PhishNet and the Internet Crime and Complaint Center (ic3.gov) — have demonstrated that they can collect phishing data in substantial volumes, but these collections have not yet resulted in a significant decline in criminal phishing activity. In this paper, a new system is demonstrated for prioritizing investigative resources to help reduce the time and effort expended examining this …

Data Mining Techniques In Fraud Detection, Rekha Bhowmik

Journal of Digital Forensics, Security and Law

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.

Virtual Radicalisation: Challenges For Police, Simon O'Rourke

Australian Information Warfare and Security Conference

Recent advances in communications technology are providing a medium for individuals or groups to subscribe to extremist worldviews and form networks, access training and obtain information, whilst remaining virtually undetected in the online world. Whilst the Internet is facilitating global virtual communities like Second Life, MySpace and Facebook it is also providing an anonymous meeting place for disenfranchised individuals to gather, share ideas, post and exchange information regarding their particular ideology. This virtual community provides a sense of belonging to a global cause in which the actions of an individual can be aligned to, and seen to contribute towards something …