Information Security Commons^™

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

Attitudes About Cybersecurity Articulation Agreements And Transfer Students: A Statewide Survey Of Faculty Members And Advisors, Brian K. Payne, Tracy Vandecar-Burdin, Daniela Cigularova

Sociology & Criminal Justice Faculty Publications

In this study, cybersecurity faculty and academic advisors from community colleges and 4-year universities in the southeast region of the United States completed a survey assessing attitudes about and support for articulation agreements and related transfer policies. Hypothesizing that professional structures shape attitudes and experiences, the researchers conducted an exploratory quantitative study with primarily descriptive analyses. The results reveal differences in attitudes between community college and 4-year stakeholders and between faculty and academic advisors. The results of this study are discussed in relation to faculty and advisor training and communication.

Civiic: Cybercrime In Virginia: Impacts On Industry And Citizens Final Report, Randy Gainey, Tancy Vandecar-Burdin, Jay Albanese, Thomas Dearden, James Hawdon, Katalin Parti

Sociology & Criminal Justice Faculty Publications

[First paragraph] Victimization from cybercrime is a major concern in Virginia, the US, and the world. As individuals and businesses spend more time online, it becomes increasingly important to understand cybercrime and how to protect against it. Such an understanding is dependent on valid and reliable baseline data that identifies the specific nature, extent, and outcomes of cybercrime activity. A better understanding of cybercrime activity is needed to target and prevent it more effectively, minimize its consequences, and provide support for both individual and corporate victims. Before that can occur, however, better baseline data are required, and this project was …

Precursors Of Email Response To Cybersecurity Scenarios: Factor Exploration And Scale Development, Miguel A. Toro-Jarrin, Pilar Pazos-Lago, Miguel Padilla

Engineering Management & Systems Engineering Faculty Publications

In the last decade, information security research has further expanded to include human factors as key elements of the organization's cybersecurity infrastructure. Numerous factors from several theories have been explored to explain and predict the multitude of information security-related behaviors in organizations. Lately, there has been a call for the study of specific cybersecurity behaviors in contextualized scenarios that reflect specific and realistic situations of a potential cyber-attack. This paper focuses on precursors of email response in situations that can be the origin of cybersecurity incidents in organizations (i.e., phishing attacks, ransomware, etc.). This study explores participants' intentions to follow …

Contextualized Vector Embeddings For Malware Detection, Vinay Pandya

Master's Projects

Malware classification is a technique to classify different types of malware which form an integral part of system security. The aim of this project is to use context dependant word embeddings to classify malware. Tansformers is a novel architecture which utilizes self attention to handle long range dependencies. They are particularly effective in many complex natural language processing tasks such as Masked Lan- guage Modelling(MLM) and Next Sentence Prediction(NSP). Different transfomer architectures such as BERT, DistilBert, Albert, and Roberta are used to generate context dependant word embeddings. These embeddings would help in classifying different malware samples based on their similarity …

Generative Adversarial Networks For Image-Based Malware Classification, Huy Nguyen

Master's Projects

Malware detection and analysis are important topics in cybersecurity. For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. With the rise in computing power and the advent of cloud computing, deep learning models for malware analysis has gained in popularity. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine

(SVM), XGBoost, and Restricted Boltzmann Machines …

Faking Sensor Noise Information, Justin Chang

Master's Projects

Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …

Robustness Of Image-Based Malware Analysis, Katrina Tran

Master's Projects

Being able to identify malware is important in preventing attacks. Image-based malware analysis is the study of images that are created from malware. Analyzing these images can help identify patterns in malware families. In previous work, "gist descriptor" features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library, we create images from malware executables and from malware that we obfuscate. We …

Investigating Lattice-Based Cryptography, Michaela Molina

Master's Projects

Cryptography is important for data confidentiality, integrity, and authentication. Public key cryptosystems allow for the encryption and decryption of data using two different keys, one that is public and one that is private. This is beneficial because there is no need to securely distribute a secret key. However, the development of quantum computers implies that many public-key cryptosystems for which security depends on the hardness of solving math problems will no longer be secure. It is important to develop systems that have harder math problems which cannot be solved by a quantum computer.

In this project, two public-key cryptosystems which …

Virtual Machine For Spartangold, William Wang

Master's Projects

The field of blockchain and cryptocurrencies can be both difficult to grasp and improve upon, which makes aids that can assist in these tasks very useful. SpartanGold is a simplified blockchain-based cryptocurrency created at San Jose State University as a learning aid for blockchain and cryptocurrencies. In its current state, it closely resembles Bitcoin, and it is also easily expandable to implement other features.

This project extends SpartanGold with a virtual machine resembling the Ethereum Virtual Machine. Implementing this feature results in SpartanGold having Ethereum- related features, which would allow the cryptocurrency to both be a helpful learning aid for …

Proxy Re-Encryption In Blockchain-Based Application, Wangcheng Yuan

Master's Projects

Nowadays, blockchain-based technology has risen to a new dimension. With the advantage of the decentralized identity, data are transferred through decentralized and public ledgers. Those new contracts provide great visibility. However, there is still a need to keep some data private in many cases. Those private data should be encrypted while still benefiting from the decentralized on-chain protocol. Securing those private data in such a decentralized blockchain-based system is thus a critical problem. Our solution provides a decentralized protocol that lets users grant access to their private data with proxy re-encryption in SpartanGold (a blockchain-based cryptocurrency). We implement a third-party …

Evaluating Similarity Of Cross-Architecture Basic Blocks, Elijah L. Meyer

Browse all Theses and Dissertations

Vulnerabilities in source code can be compiled for multiple processor architectures and make their way into several different devices. Security researchers frequently have no way to obtain this source code to analyze for vulnerabilities. Therefore, the ability to effectively analyze binary code is essential. Similarity detection is one facet of binary code analysis. Because source code can be compiled for different architectures, the need can arise for detecting code similarity across architectures. This need is especially apparent when analyzing firmware from embedded computing environments such as Internet of Things devices, where the processor architecture is dependent on the product and …

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

Secure Authenticated Key Exchange For Enhancing The Security Of Routing Protocol For Low-Power And Lossy Networks, Sarah Mohammed Alzahrani

Browse all Theses and Dissertations

The current Routing Protocol for Low Power and Lossy Networks (RPL) standard provides three security modes Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and Authenticated Secure Mode (ASM). The PSM and ASM are designed to prevent external routing attacks and specific replay attacks through an optional replay protection mechanism. RPL's PSM mode does not support key replacement when a malicious party obtains the key via differential cryptanalysis since it considers the key to be provided to nodes during the configuration of the network. This thesis presents an approach to implementing a secure authenticated key exchange mechanism for RPL, which ensures …

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation, Jyoti Fakirah, Lauhim Mahfuz Zishan, Roshni Mooruth, Michael L. Johnstone, Wencheng Yang

Annual ADFSL Conference on Digital Forensics, Security and Law

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS …

Human-Controlled Fuzzing With Afl, Maxim Grishin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Fuzzing techniques are applied to reveal different types of bugs and vulnerabilities. American Fuzzy Lop (AFL) is a free most popular software fuzzer used by many other fuzzing frameworks. AFL supports autonomous mode of operation that uses the previous step output into the next step, as a result fuzzer spends a lot of time analyzing minor code sections. By making fuzzing process more focused and human controlled security expert can save time and find more bugs in less time. We designed a new module that can fuzz only the specified functions. As a result, the chosen ones will be inspected …

The Amorphous Nature Of Hackers: An Exploratory Study, Kento Yasuhara, Daniel Walnycky, Ibrahim Baggili, Ahmed Alhishwan

Annual ADFSL Conference on Digital Forensics, Security and Law

In this work, we aim to better understand outsider perspectives of the hacker community through a series of situation based survey questions. By doing this, we hope to gain insight into the overall reputation of hackers from participants in a wide range of technical and non-technical backgrounds. This is important to digital forensics since convicted hackers will be tried by people, each with their own perception of who hackers are. Do cyber crimes and national security issues negatively affect people’s perceptions of hackers? Does hacktivism and information warfare positively affect people’s perception of hackers? Do individual personality factors affect one’s …

Graph Neural Networks For Malware Classification, Vrinda Malhotra

Master's Projects

Malware is a growing threat to the digital world. The first step to managing this threat is malware detection and classification. While traditional techniques rely on static or dynamic analysis of malware, the generation of these features requires expert knowledge. Function call graphs (FCGs) consist of program functions as their nodes and their interprocedural calls as their edges, providing a wealth of knowledge that can be utilized to classify malware without feature extraction that requires experts. This project treats malware classification as a graph classification problem, setting node features using the Local Degree Profile (LDP) model and using different graph …

Adversarial Attacks On Android Malware Detection And Classification, Srilekha Nune

Master's Projects

Recent years have seen an increase in sales of intelligent gadgets, particularly those using the Android operating system. This popularity has not gone unnoticed by malware writers. Consequently, many research efforts have been made to develop learning models that can detect Android malware. As a countermeasure, malware writers can consider adversarial attacks that disrupt the training or usage of such learning models. In this paper, we train a wide variety of machine learning models using the KronoDroid Android malware dataset, and we consider adversarial attacks on these models. Specifically, we carefully measure the decline in performance when the feature sets …

Darknet Traffic Classification, Nhien Rust-Nguyen

Master's Projects

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic to block these criminal activities. This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN) and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN) for classification of network traffic and the underlying application types. We find that our RF model outperforms the state-of-the-art machine learning techniques used by prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our RF classifier, we degrade …

Bfv-Based Homomorphic Encryption For Privacy-Preserving Cnn Models, Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu

Engineering Technology Faculty Publications

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers …

Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …

Development Of Experiential Learning Experiences For K-12 Students Focusing On Smart Cities, Murat Kuzlu, Vukica Jovanovic, Nathan Puryear, Patrick J. Martin, Sherif Abdelwahed, Özgür Güler

Engineering Technology Faculty Publications

The main objective of this paper is to describe a project focused on the development of experiential learning experiences for undergraduate and graduate students focusing on smart cities. The future workforce needs students with various data analytics skills, service reliability, and sustainability. The team of researchers from Old Dominion University and Virginia Commonwealth University is developing a virtual smart city lab environment at both universities and collaborating on multiple research projects. The main purpose of this virtual labs is to provide a testbed that can be used for students who are interested in careers related to cyber-physical systems (CPS). These …

Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran

Electronic Theses and Dissertations, 2020-2023

Today's hyper-connected consumers demand convenient ways to tune into information without switching between devices, which led the industry leaders to the wearables. Wearables such as smartwatches, fitness trackers, and augmented reality (AR) glasses can be comfortably worn on the body. In addition, they offer limitless features, including activity tracking, authentication, navigation, and entertainment. Wearables that provide digestible information stimulate even higher consumer demand. However, to keep up with the ever-growing user expectations, developers keep adding new features and interaction methods to augment the use cases without considering their privacy impacts. In this dissertation, we explore the privacy dimension of wearables …

Editorial, Michael E. Whitman, Herbert J. Mattord, Hossain Shahriar

Journal of Cybersecurity Education, Research and Practice

Since 2016, it has been the mission of the Journal of Cybersecurity Education, Research, and Practice (JCERP) to be a premier outlet for high-quality information security and cybersecurity-related articles of interest to teaching faculty and students. This is the 13^th edition of the (JCERP) and, as ever, we are seeking authors who produce high-quality research and practice-oriented articles focused on the development and delivery of information security and cybersecurity curriculum, innovation in applied scholarship, and industry best practices in information security and cybersecurity in the enterprise for double-blind review and publication. The journal invites submissions on Information Security, Cybersecurity, …

Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware …

A Blockchain-Based Self-Tallying Voting Protocol In Decentralized Iot, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused …