SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted SOCIAL EXCHANGE THEORY IN THE CONTEXT OF X (TWITTER) AND FACEBOOK SOCIAL MEDIA PLATFORMS WITH A FOCUS ON PRIVCAY CONCERNS AMONG SAUDI STUDENTS(Saudi Digital Library, 2023-12-16) Alqahtani, Sameer Mohammed; Prybutok, Victor RExamining rewards, costs, and comparison levels, the Social Exchange Theory (SET) in sociology underpins our comprehension of self-interest-driven social relationships. Trust, authority, and reciprocity have a substantial impact on these interactions. The Social Exchange Theory (SET) is a valuable lens for understanding human relationships, including online interactions. Social media platforms, such as X (Twitter) and Facebook, have become indispensable communication tools in our daily lives. Nevertheless, due to their user base, they also attract cybercriminals. X (Twitter) offers a variety of security features, such as password protection, two-factor authentication, privacy settings, and app controls, but users must remain vigilant against fraud attempts. Facebook collects vast amounts of private information, which increases the importance of comprehending and implementing security settings. Security awareness is essential for data protection, risk reduction, and conformance with privacy laws. Awareness allows users to manage interactions with security in mind and results in a more secure digital environment, mitigating risks such as identity fraud. Various methodological approaches have allowed the investigation of these two digital phenomena, and the current research contributes to the literature by examining the use of social media and its security settings using a SET lens within a Saudi student environment. This research followed a traditional format for a dissertation, which includes an introduction, literature review, methodology, results, and conclusion with the results section presented the findings from the three essays. The first essay employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology of SET. PRISMA's systematic and exhaustive approach to literature evaluation increases the likelihood of obtaining high-quality, reproducible findings. In the second essay, which focuses on awareness of X’s (Twitter) security settings, a quantitative research approach was utilized. A sample of former and current Saudi students (graduate and undergraduate) at the University of North Texas participated in the investigation. This research provides an empirical examination of the use of X (Twitter) and its security features within this community by employing statistical analysis of the data from respondents. Likewise, the same sample of Saudi students from the University of North Texas was used for the third essay in which the use of Facebook's security settings was examined. Having a consistent sample across both studies enables a comparison and a greater understanding of the security awareness and practices of this group across various social media platforms. The findings across the different studies extend our understanding of the role of culture in privacy and security concerns related to social media.58 0Item Restricted DETECTING MANIPULATED AND ADVERSARIAL IMAGES: A COMPREHENSIVE STUDY OF REAL-WORLD APPLICATIONS(UCF STARS, 2023-11-06) Alkhowaiter, Mohammed; Zou, CliffThe great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and be- lief or sway the viewers’ perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study pro- poses a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detect- ing manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.31 0Item Restricted Artificial Intelligence Applied To Cybersecurity And Health Care(NDSU, 2023-09-21) Alenezi, Rafa; Ludwig, SimoneNowadays, artificial intelligence is being considered a potential solution for various problems, including classification and regression optimization, in different fields such as science, technology, and humanities. It can also be applied in areas such as cybersecurity and healthcare. With the increasing complexity and impact of cybersecurity threats, it is essential to develop mechanisms for detecting new types of attacks. Hackers often target the Domain Name Server (DNS) component of a network architecture, which stores information about IP addresses and associated domain names, to gain access to a server or compromise network connectivity. Machine learning techniques can be used not only for cyber threat detection but also for other applications in various fields. In this dissertation, the first research investigates the use of classification models, including Random Forest classifiers, Keras Sequential algorithms, and XGBoost classification, for detecting attacks. Additionally, Tree, Deep, and Kernel of Shapley Additive Explanations (SHAP) can be used to interpret the results of these models. The second research focuses on detecting DNS attacks using appropriate classifiers to enable quick and effective responses. In the medical field, there is a growing trend of using algorithms to identify diseases, particularly in medical imaging. Deep learning models have been developed to detect pneumonia, but their accuracy is not always optimal and they require large data sets for training. Two studies were conducted to develop more accurate detection models for pneumonia in chest X-ray images. The third study developed a model based on Reinforcement Learning (RL) with Convolutional Neural Network (CNN) and showed improved accuracy values. The fourth study used Teaching Learning Based Optimization (TLBO) with Convolutional Neural Network (CNN) to improve pneumonia detection accuracy, which resulted in high-level accuracy rates. Overall, all these studies provide insights into the potential of artificial intelligence in improving disease detection and cyber treat detection.37 0Item Restricted Physics and AI-Driven Anomaly Detection in Cyber-Physical Systems(Saudi Digital Library, 2023) Alotibi, Faris; Tipper, DavidOrganizations across various sectors are moving rapidly to digitization. Multiple applications in cyber-physical systems (CPSs) emerged from interconnectivity such as smart cities, autonomous vehicles, and smart grids, utilizing advanced capabilities of the Internet of Things (IoTs), cloud computing, and machine learning. Interconnectivity also becomes a critical component in industrial systems such as smart manufacturing, smart oil, and gas distribution grid, smart electric power grid, etc. These critical infrastructures and systems rely on industrial IoT and learning-enabled components to handle the uncertainty and variability of the environment and increase autonomy in making effective operational decisions. The prosperity and benefits of systems interconnectivity demand the fulfillment of functional requirements such as interoperability of communication and technology, efficiency and reliability, and real-time communication. Systems need to integrate with various communication technologies and standards, process and analyze shared data efficiently, ensure the integrity and accuracy of exchanged data, and execute their processes with tolerable delay. This creates new attack vectors targeting both physical and cyber components. Protection of systems interconnection and validation of communicated data against cyber and physical attacks become critical due to the consequences of disruption attacks pose to critical systems. In this dissertation, we tackle one of the prominent attacks in the CPS space, namely the false data injection attack (FDIA). FDIA is an attack executed to maliciously influence decisions, that is CPSs operational decisions such as opening a valve, changing wind turbine configurations, charging/discharging energy storage system batteries, or coordinating autonomous vehicles driving. We focus on the development of anomaly detection techniques to protect CPSs from this emerging threat. The anomaly detection mechanisms leverage both physics of CPSs and AI to improve their detection capability as well as the CPSs' ability to mitigate the impact of FDIA on their operations.51 0Item Open Access TOWARDS A TRANSDISCIPLINARY CYBER FORENSICS GEO-CONTEXTUALIZATION FRAMEWORK(Purdue University Graduate School, 2023-08-04) Mirza, Mohammad Meraj; Karabiyik, UmitTechnological advances have a profound impact on people and the world in which they live. People use a wide range of smart devices, such as the Internet of Things (IoT), smartphones, and wearable devices, on a regular basis, all of which store and use location data. With this explosion of technology, these devices have been playing an essential role in digital forensics and crime investigations. Digital forensic professionals have become more able to acquire and assess various types of data and locations; therefore, location data has become essential for responders, practitioners, and digital investigators dealing with digital forensic cases that rely heavily on digital devices that collect data about their users. It is very beneficial and critical when performing any digital/cyber forensic investigation to consider answering the six Ws questions (i.e., who, what, when, where, why, and how) by using location data recovered from digital devices, such as where the suspect was at the time of the crime or the deviant act. Therefore, they could convict a suspect or help prove their innocence. However, many digital forensic standards, guidelines, tools, and even the National Institute of Standards and Technology (NIST) Cyber Security Personnel Framework (NICE) lack full coverage of what location data can be, how to use such data effectively, and how to perform spatial analysis. Although current digital forensic frameworks recognize the importance of location data, only a limited number of data sources (e.g., GPS) are considered sources of location in these digital forensic frameworks. Moreover, most digital forensic frameworks and tools have yet to introduce geo-contextualization techniques and spatial analysis into the digital forensic process, which may aid digital forensic investigations and provide more information for decision-making. As a result, significant gaps in the digital forensics community are still influenced by a lack of understanding of how to properly curate geodata. Therefore, this research was conducted to develop a transdisciplinary framework to deal with the limitations of previous work and explore opportunities to deal with geodata recovered from digital evidence by improving the way of maintaining geodata and getting the best value from them using an iPhone case study. The findings of this study demonstrated the potential value of geodata in digital disciplinary investigations when using the created transdisciplinary framework. Moreover, the findings discuss the implications for digital spatial analytical techniques and multi-intelligence domains, including location intelligence and open-source intelligence, that aid investigators and generate an exceptional understanding of device users' spatial, temporal, and spatial-temporal patterns.45 0Item Restricted Ensemble Defense System: Combining Signature-based and Behavioral-based Intrusion Detection Tools(2023-08-04) Alharbi, Sarah; De Lucia, MichaelCyber attacks are becoming increasingly sophisticated, which poses significant challenges for organizations in detecting and preventing these attacks. Implementing robust defense mechanisms that can detect, prevent, and respond to these threats and attacks is crucial. In this thesis, we design, develop, and evaluate a novel Ensemble Defense System (EDS), addressing the critical need for advanced defense systems. The EDS combines the capabilities of Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) to provide an effective defense against cyber threats. The EDS incorporates hybrid-based IDS technologies, leveraging the strengths of signature-based IDS tools like Zeek and Suricata and behavioral-based IDS tools like Slips. By utilizing hybrid-based IDS, the EDS provides a more effective system for countering cyber threats. Moreover, the EDS integrates open-source SIEM, specifically Elasticsearch, to provide data management and analysis capabilities and create user-friendly visualization. The effectiveness of the EDS has been evaluated through a designed bash script that performs several attacks, such as port scanning, privilege escalation, and Denial-of-Service (DoS). This research contributes to better cybersecurity by introducing an EDS that can detect various cyber attacks.56 0Item Restricted Lightweight Cryptographic Mechanisms for Internet of Things and Embedded Systems(2023-03) Bin Rabiah, Abdulrahman; Abu-Ghazaleh, Nael; Richelson, SilasToday, IoT devices such as health monitors and surveillance cameras are widespread. As the industry matures, IoT systems are becoming pervasive. This revolution necessitates further research in network security, as IoT systems impose constraints on network design due to the use of lightweight, computationally weak devices with limited power and network connectivity being used for varying and unique applications. Thus, specialized secure protocols which can tolerate these constraints are needed. This dissertation examines three problems in the constrained IoT setting: 1) Key exchange, 2) Authentication and 3) Key management. First, IoT devices often gather critical information that needs to be communicated in a secure manner. Authentication and secure communication in an IoT environment can be difficult because of constraints, in computing power, memory, energy and network connectivity. For secure communication with the rest of the network, an IoT device needs to trust the gateway through which it communicates, often over a wireless link. An IoT device needs a way of authenticating the gateway and vice-versa, to set up that secure channel. We introduce a lightweight authentication and key exchange system for IoT environments that is tailored to handle the IoT-imposed constraints. In our system, the gateway and IoT device communicate over an encrypted channel that uses a shared symmetric session key which changes periodically (every session) in order to ensure perfect forward secrecy. We combine both symmetric-key and public-key cryptography based authentication and key exchange, thus reducing the overhead of manual configuration. We study our proposed system, called Haiku, where keys are never exchanged over the network. We show that Haiku is lightweight and provides authentication, key exchange, confidentiality, and message integrity. Haiku does not need to contact a Trusted Third Party (TTP), works in disconnected IoT environments, provides perfect forward secrecy, and is efficient in compute, memory and energy usage. Haiku achieves 5x faster key exchange and at least 10x energy consumption reductions. Second, signature-based authentication is a core cryptographic primitive essential for most secure networking protocols. We introduce a new signature scheme, MSS, that allows a client to efficiently authenticate herself to a server. We model our new scheme in an offline/online model where client online time is premium. The offline component derives basis signatures that are then composed based on the data being signed to provide signatures efficiently and securely during run-time. MSS requires the server to maintain state and is suitable for applications where a device has long-term associations with the server. MSS allows direct comparison to hash chain-based authentication schemes used in similar settings, and is relevant to resource-constrained devices e.g., IoT. We derive MSS instantiations for two cryptographic families, assuming the hardness of RSA and decisional Diffie-Hellman (DDH) respectively, demonstrating the generality of the idea. We then use our new scheme to design an efficient time-based one-time password (TOTP) system. Specifically, we implement two TOTP authentication systems from our RSA and DDH instantiations. We evaluate the TOTP implementations on Raspberry Pis which demonstrate appealing gains: MSS reduces authentication latency and energy consumption by a factor of ∼82 and 792, respectively, compared to a recent hash chain-based TOTP system. Finally, we examine an important sub-component of the massive IoT technology, namely connected vehicles (CV)/Internet of Vehicles (IoV). In the US alone, the US department of transportation approximates the number of vehicles to be around 350 million. Connected vehicles is an emerging technology, which has the potential to improve the safety and efficiency of the transportation system. To maintain the security and privacy of CVs, all vehicle-to-vehicle (V2V) communications are typically established on top of pseudonym certificates (PCs) which are maintained by a vehicular public key infrastructure (VPKI). However, the state-of-the-art VPKIs (including SCMS; the US VPKI standard for CV) often overlooked the reliability constraint of wireless networks (which eventually degrades the VPKI security) that exists in high-mobility environments such as CV networks. This constraint stems from the short coverage time between an on-board unit (OBU) inside a fast moving vehicle and a stationary road-side unit (RSU). In this work, we present TVSS, a novel VPKI design that pushes critical VPKI operations to the edge of the network; the RSU, while maintaining all security and privacy assumptions in the state-of-the-art VPKIs. Our real-life testbed shows a reduced PC generation latency by 28.5x compared to recent VPKIs. Furthermore, our novel local pseudonym certificate revocation lists (PCRLs) achieves 13x reduction in total communication overhead for downloading them compared to delta PCRLs.33 0