Information Security Commons^™

Malware Detection Using The Index Of Coincidence, Bhavna Gurnani

Master's Projects

In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.

Machine Learning With Personal Data: Is Data Protection Law Smart Enough To Meet The Challenge?, Fred H. Cate, Christopher Kuner, Dan Jerker B. Svantesson, Orla Lynskey, Christopher Millard

Articles by Maurer Faculty

No abstract provided.

Security Analytics: Using Deep Learning To Detect Cyber Attacks, Glenn M. Lambert Ii

UNF Graduate Theses and Dissertations

Security attacks are becoming more prevalent as cyber attackers exploit system vulnerabilities for financial gain. The resulting loss of revenue and reputation can have deleterious effects on governments and businesses alike. Signature recognition and anomaly detection are the most common security detection techniques in use today. These techniques provide a strong defense. However, they fall short of detecting complicated or sophisticated attacks. Recent literature suggests using security analytics to differentiate between normal and malicious user activities.

The goal of this research is to develop a repeatable process to detect cyber attacks that is fast, accurate, comprehensive, and scalable. A model …

Rationality, Parapsychology, And Artificial Intelligence In Military And Intelligence Research By The United States Government In The Cold War, Guy M. Lomeo

Theses and Dissertations

A study analyzing the roles of rationality, parapsychology, and artificial intelligence in military and intelligence research by the United States Government in the Cold War. An examination of the methodology behind the decisions to pursue research in two fields that were initially considered irrational.

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

An Approach To Automatic Detection Of Suspicious Individuals In A Crowd, Satabdi Mukherjee

Dissertations and Theses

This paper describes an approach to identify individuals with suspicious objects in a crowd. It is based on a well-known image retrieval problem as applied to mobile visual search. In many cases, the process of building a hierarchical tree uses k-means clustering followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is randomly generated. This may lead to a congested clustering which can cause problems in grouping large real-time data. To overcome this problem we have applied the Indian Buffet stochastic process approach in this paper to the clustering problem. We present …

Malware Detection Using Dynamic Analysis, Swapna Vemparala

Master's Projects

In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach.

A Comparison Of Clustering Techniques For Malware Analysis, Swathi Pai

Master's Projects

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering.

Clustering Versus Svm For Malware Detection, Usha Narra

Master's Projects

Previous work has shown that we can effectively cluster certain classes of mal- ware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of clas- sifying new samples based on these existing clusters. We compare results obtained using �-means and Expectation Maximization (EM) clustering to those obtained us- ing Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One …

Metalogic Notes, Saverio Perugini

Computer Science Working Papers

A collection of notes, formulas, theorems, postulates and terminology in symbolic logic, syntactic notions, semantic notions, linkages between syntax and semantics, soundness and completeness, quantified logic, first-order theories, Goedel's First Incompleteness Theorem and more.

Statistics Notes, Saverio Perugini

Computer Science Working Papers

A collection of terms, definitions, formulas and explanations about statistics.

Masquerade Detection Using Singular Value Decomposition, Sweta Vikram Shah

Master's Projects

Information systems and networks are highly susceptible to attacks in the form of intrusions. One such attack is by the masqueraders who impersonate legitimate users. Masqueraders can be detected in anomaly based intrusion detection by identifying the abnormalities in user behavior. This user behavior is logged in log files of different types. In our research we use the score based technique of Singular Value Decomposition to address the problem of masquerade detection on a unix based system. We have data collected in the form of sequential unix commands ran by 50 users. SVD is a linear algebraic technique, which has …

Unsupervised Learning Trojan, Arturo Geigel

CCAC Theses and Dissertations

This work presents a proof of concept of an Unsupervised Learning Trojan. The Unsupervised Learning Trojan presents new challenges over previous work on the Neural network Trojan, since the attacker does not control most of the environment. The current work will presented an analysis of how the attack can be successful by proposing new assumptions under which the attack can become a viable one. A general analysis of how the compromise can be theoretically supported is presented, providing enough background for practical implementation development. The analysis was carried out using 3 selected algorithms that can cover a wide variety of …

Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg

Research Collection School Of Computing and Information Systems

In this paper we focus on authentication and privacy aspects of an application scenario that utilizes mobile crowd sensing for the benefit of amusement park operators and their visitors. The scenario involves a mobile app that gathers visitors’ demographic details, preferences, and current location coordinates, and sends them to the park’s sever for various analyses. These analyses assist the park operators to efficiently deploy their resources, estimate waiting times and queue lengths, and understand the behavior of individual visitors and groups. The app server also offers visitors optimal recommendations on routes and attractions for an improved dynamic experience and minimized …

Active Malware Analysis Using Stochastic Games, Simon Williamson, Pradeep Reddy Varakantham, Debin Gao, Chen Hui Ong

Research Collection School Of Computing and Information Systems

Cyber security is increasingly important for defending computer systems from loss of privacy or unauthorised use. One important aspect is threat analysis - how does an attacker infiltrate a system and what do they want once they are inside. This paper considers the problem of Active Malware Analysis, where we learn about the human or software intruder by actively interacting with it with the goal of learning about its behaviours and intentions, whilst at the same time that intruder may be trying to avoid detection or showing those behaviours and intentions. This game-theoretic active learning is then used to obtain …

Holistic Network Defense: Fusing Host And Network Features For Attack Classification, Jenny W. Ji

Theses and Dissertations

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon …

Valuations Of Possible States (Vps): A Unifying Quantitative Framework For Evaluating Privacy In Collaboration, Rajiv T. Maheswaran, Jonathan Pearce, Pradeep Varakantham, Emma Bowring, Milind Tambe

Research Collection School Of Computing and Information Systems

For agents deployed in real-world settings, such as businesses, universities and research laboratories, it is critical that agents protect their individual users’ privacy when interacting with others entities. Indeed, privacy is recognized as a key motivating factor in design of several multiagent algorithms, such as distributed constraint optimization (DCOP) algorithms. Unfortunately, rigorous and general quantitative metrics for analysis and comparison of such multiagent algorithms with respect to privacy loss are lacking. This paper takes a key step towards developing a general quantitative model from which one can analyze and generate metrics of privacy loss by introducing the VPS (Valuations of …

System Login Authentication Using Voice Recognition And Other Ai Methods, Azizul Hasan Zati Hakim

Student Works (2000-2009)

As computer becomes a commodity in work places and households appliances used by all - be it male or female young or old - the issue of security and privacy are increasing from day to day. Today, every user wishes for a better way to protect sensitive data. For all the talk about beefing up system security, the most effective way to breach a user's personal files and applications is to guess his or her password. That's because in the interest of easy recall, familiar terms are most preferred by users in general when it come too remembering a password. …

The "Hoover" Project: Home Occupants Vehicular Electronic Reconnaissance, Kelvin Chan

SWITCH

The article explains telepresence, as well as its potential in safety and security, along with its traditional usage of traversing dangerous situations. This article describes a dystopian plan to place drones equipped with cameras and microphones in all homes in the Silicon Valley as a vehicle for telepresence. The data achieved through this method would also be stored into a public domain browser on the internet, free for anyone in the public to view, including larger corporations and the government. The idea behind this is the assimilation of data behind all cultures for understanding and to assist law enforcement in …