Information Security Commons^™

Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …

Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …

Back Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Masthead

Journal of Digital Forensics, Security and Law

No abstract provided.

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Panorama: Multi-Path Ssl Authentication Using Peer Network Perspectives, William P. Harris

Computer Engineering

SSL currently uses certificates signed by Certificate Authorities (CAs) to authenticate connections. e.g. Google will pay a CA to sign a certificate for them, so that they can prove that they're not someone pretending to be Google. Unfortunately, this system has had multiple problems, and many believe that an alternative needs to be found.

One of the ideas for alternatives is using multiple "network perspectives" to authenticate a server. The idea behind this is that, though playing man-in-the-middle (MITM) with one connection is easy, it should be difficult for an adversary to do so with many connections, especially if they …

Gpuhelib And Distributedhelib: Distributed Computing Variants Of Helib, A Homomorphic Encryption Library, Ethan Andrew Frame

Master's Theses

Homomorphic Encryption, an encryption scheme only developed in the last five years, allows for arbitrary operations to be performed on encrypted data. Using this scheme, a user can encrypt data, and send it to an online service. The online service can then perform an operation on the data and generate an encrypted result. This encrypted result is then sent back to the user, who decrypts it. This decryption produces the same data as if the operation performed by the online service had been performed on the unencrypted data. This is revolutionary because it allows for users to rely on online …

Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding

Research Collection School Of Computing and Information Systems

Determining if a first encrypted data of a first data value is equal to a second encrypted data of a second data value. Comprising: a first cyclic group; a second cyclic group including a first element. Applying an operation to the first cyclic group to map its elements to an element in the second cyclic group. Randomly selecting a second element from the first cyclic group; producing the first encrypted data by mapping the second element and the first data value into one or more elements of the first cyclic group. Randomly selecting a third element from the first cyclic …

Continuous Non-Malleable Key Derivation And Its Application To Related-Key Security, Baodong Qin, Shenli Liu, Tsz Hon Yuen, Robert H. Deng, Kefei Chen

Research Collection School Of Computing and Information Systems

Related-Key Attacks (RKAs) allow an adversary to observe the outcomes of a cryptographic primitive under not only its original secret key e.g., s, but also a sequence of modified keys ϕ(s), where ϕ is specified by the adversary from a class Φ of so-called Related-Key Derivation (RKD) functions. This paper extends the notion of non-malleable Key Derivation Functions (nm-KDFs), introduced by Faust et al. (EUROCRYPT’14), to continuous nm-KDFs. Continuous nm-KDFs have the ability to protect against any a-priori unbounded number of RKA queries, instead of just a single time tampering attack as in the definition of …

Support Vector Machines And Metamorphic Malware Detection, Tanuvir Singh

Master's Projects

Metamorphic malware changes its internal structure with each infection, which makes it challenging to detect. In this research, we test several scor- ing techniques that have shown promise in metamorphic detection. We then perform a careful robustness analysis by employing morphing strategies that cause each score to fail. Finally, we show that combining scores using a Sup- port Vector Machine (SVM) yields results that are significantly more robust than we obtained using any of the individual scores.

Cryptanalysis Of Classic Ciphers Using Hidden Markov Models, Rohit Vobbilisetty

Master's Projects

Cryptanalysis is the study of identifying weaknesses in the implementation of cryptographic algorithms. This process would improve the complexity of such algo- rithms, making the system secure.

In this research, we apply Hidden Markov Models (HMMs) to classic cryptanaly- sis problems. We show that with sufficient ciphertext, an HMM can be used to break a simple substitution cipher. We also show that when limited ciphertext is avail- able, using multiple random restarts for the HMM increases our chance of successful decryption.

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.

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.

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 …

Firefox Add-On For Metamorphic Javascript Malware Detection, Sravan Kumar Reddy Javaji

Master's Projects

With the increasing use of the Internet, malicious software has more frequently been designed to take control of users computers for illicit purposes. Cybercriminals are putting a lot of efforts to make malware difficult to detect. In this study, we demonstrate how the metamorphic JavaScript malware can effect a victim’s machine using a malicious or compromised Firefox add-on. Following the same methodology, we develop another add-on with malware static detection technique to detect metamorphic JavaScript malware.

Tracking Criminals On Facebook: A Case Study From A Digital Forensics Reu Program, Daniel Weiss, Gary Warner

Annual ADFSL Conference on Digital Forensics, Security and Law

The 2014 Digital Forensics Research Experience for Undergraduates (REU) Program at the University of Alabama at Birmingham (UAB) focused its summer efforts on tracking criminal forums and Facebook groups. The UAB-REU Facebook team was provided with a list of about 60 known criminal groups on Facebook, with a goal to track illegal information posted in these groups and ultimately store the information in a searchable database for use by digital forensic analysts. Over the course of about eight weeks, the UAB-REU Facebook team created a database with over 400 Facebook groups conducting criminal activity along with over 100,000 unique users …

Towards A Digital Forensics Competency-Based Program: Making Assessment Count, Rose Shumba

Annual ADFSL Conference on Digital Forensics, Security and Law

This paper describes an approach that UMUC has initiated to revise its graduate programs to a Competency-Based Education (CBE) curriculum. The approach, which is Learning Demonstration (LD) centric, includes the identification of learning goals and competences, identification and description of the LDs, mapping of the LDs to the competences, scripting the LDs, placing the LDs into the respective courses, validating the developed materials, and the development of the open learning resources. Programs in the Cybersecurity and Information Assurance Department, including the Digital Forensics and Cyber Investigations program, are being revised. An LD centric approach to curriculum development helps align programs …

Phishing Intelligence Using The Simple Set Comparison Tool, Jason Britt, Alan Sprague, Gary Warner

Annual ADFSL Conference on Digital Forensics, Security and Law

Phishing websites, phish, attempt to deceive users into exposing their passwords, user IDs, and other sensitive information by imitating legitimate websites, such as banks, product vendors, and service providers. Phishing investigators need fast automated tools to analyze the volume of phishing attacks seen today. In this paper, we present the Simple Set Comparison tool. The Simple Set Comparison tool is a fast automated tool that groups phish by imitated brand allowing phishing investigators to quickly identify and focus on phish targeting a particular brand. The Simple Set Comparison tool is evaluated against a traditional clustering algorithm over a month's worth …

Identifying Common Characteristics Of Malicious Insiders, Nan Liang, David Biros

Annual ADFSL Conference on Digital Forensics, Security and Law

Malicious insiders account for large proportion of security breaches or other kinds of loss for organizations and have drawn attention of both academics and practitioners. Although methods and mechanism have been developed to monitor potential insider via electronic data monitoring, few studies focus on predicting potential malicious insiders. Based on the theory of planned behavior, certain cues should be observed or expressed when an individual performs as a malicious insider. Using text mining to analyze various media content of existing insider cases, we strive to develop a method to identify crucial and common indicators that an individual might be a …

Cheating Detection In Online Examinations, Gaurav Kasliwal

Master's Projects

In this research, we develop and analyze a tool that monitor student browsing activity during online examination. Our goal is to detect cheating in real time. In our design, a server capture packets using KISMET and detects cheating based on either a whitelist or blacklist of URLs. We provide implementation details and give experimental results, and we analyze various attack strategies. Finally, we show that the system is practical and lightweight in comparison to other available tools.