SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
7 results
Search Results
Item Restricted A RISK-ADAPTIVE ACCESS CONTROL MODEL FOR THE SERVICE MESH IN A MICROSERVICES ARCHITECTURE(The University of Tulsa, 2025-05) Alboqmi, Rami; Gamble, RoseCloud computing has transformed our lives by enabling applications to be deployed at scale, allowing a broad range of customers to access services seamlessly. However, as cloud computing has evolved, several challenges have emerged, such as meeting high customer demands while maintaining system stability and scalability. As a result, the cloud community introduced cloud-native computing in 2015, enabling applications to be scaled efficiently to meet customers’ demands. The microservices architecture (MSA) is a key enabler of cloud-native application development. It allows developers to build an application's components loosely and independently as microservices (also referred to as services). Following and applying the MSA architecture has many benefits, such as a failure within a microservice may not affect the entire deployed MSA application. For example, a failure in the temperature display microservice functionality does not affect the core functionalities of other microservices, such as map navigation. The map navigation microservice will still operate without temperature data. As a result, an MSA application becomes more resilient to failure. However, MSA introduces challenges in securing communication between microservices where orchestration solutions cannot ensure secure communications. A rogue microservice could act as a backdoor, compromising other microservices within the MSA application after initial authentication and authorization at deployment. Thus, service mesh technology was introduced as an infrastructure layer within an orchestration solution in 2017 to handle robust security, such as secure microservices-to-microservices communication with features like mutual TLS. Nevertheless, the current service mesh solutions are not mature yet and still rely on static AC policies set at deployment. In addition, these static policies operate with implicit trust between microservices, which do not adapt to changes in response to the trustworthiness of microservice. As a result, the service mesh limits its ability to detect compromised microservices at runtime, requires manual AC policy updates, and creates security gaps. A dynamic AC model for the service mesh is crucial to continuously assess the trustworthiness of microservices based on their behavior and vulnerability posture to align with the Zero Trust (ZT) principle of “never trust, always verify.” Additionally, any proposed dynamic AC model for the service mesh must not only offer dynamic and adaptive AC policies but also address the research gap in service mesh in the lack of capabilities such as sharing threat intelligence and enforcing automated microservice owner compliance requirements at runtime. These capabilities are essential for continuous monitoring and adaptive security responses for MSA applications at runtime. To dynamically adjust AC policies at runtime based on the trustworthiness of microservices, this research introduces the Service Mesh risk-Adaptive Access Control (SMAAC). SMAAC consists of three components: (1) Runtime Trust Evaluator (RTE) that assigns a trust metric (TM) to all microservices based on their behaviors and vulnerabilities; (2) Threat Intelligence Sharing (TIS) that shares TM values and vulnerability reports of all microservices; and (3) Access Policy Generation (APG) that creates dynamic AC policies when the TM of a microservice falls below a compliant threshold. Evaluated on three research MSA applications μBench, Lakeside Mutual, and Train Ticket, SMAAC effectively shows an adaptive mechanism for creating compliant AC policies to secure the operations of microservices and reduce security risks.7 0Item Restricted Testing Privacy and Security of Voice Interface Applications in the IoT Era(Temple University, 2024-04-04) Shafei, Hassan Ali; Tan, Chiu C.Voice User Interfaces (VUI) are rapidly gaining popularity, revolutionizing user interaction with technology through the widespread adoption in devices such as desktop computers, smartphones, and smart home assistants, thanks to significant advancements in voice recognition and processing technologies. Over a hundred million users now utilize these devices daily, and smart home assistants have been sold in massive numbers, owing to their ease and convenience in controlling a diverse range of smart devices within the home IoT environment through the power of voice, such as controlling lights, heating systems, and setting timers and alarms. VUI enables users to interact with IoT technology and issue a wide range of commands across various services using their voice, bypassing traditional input methods like keyboards or touchscreens. With ease, users can inquire in natural language about the weather, stock market, and online shopping and access various other types of general information. However, as VUI becomes more integrated into our daily lives, it brings to the forefront issues related to security, privacy, and usability. Concerns such as the unauthorized collection of user data, the potential for recording private conversations, and challenges in accurately recognizing and executing commands across diverse accents, leading to misinterpretations and unintended actions, underscore the need for more robust methods to test and evaluate VUI services. In this dissertation, we delve into voice interface testing, evaluation for privacy and security associated with VUI applications, assessment of the proficiency of VUI in handling diverse accents, and investigation into access control in multi-user environments. We first study the privacy violations of the VUI ecosystem. We introduced the definition of the VUI ecosystem, where users must connect the voice apps to corresponding services and mobile apps to function properly. The ecosystem can also involve multiple voice apps developed by the same third-party developers. We explore the prevalence of voice apps with corresponding services in the VUI ecosystem, assessing the landscape of privacy compliance among Alexa voice apps and their companion services. We developed a testing framework for this ecosystem. We present the first study conducted on the Alexa ecosystem, specifically focusing on voice apps with account linking. Our designed framework analyzes both the privacy policies of these voice apps and their companion services or the privacy policies of multiple voice apps published by the same developers. Using machine learning techniques, the framework automatically extracts data types related to data collection and sharing from these privacy policies, allowing for a comprehensive comparison. Next, researchers studied the voice apps' behavior to conduct privacy violation assessments. An interaction approach with voice apps is needed to extract the behavior where pre-defined utterances are input into the simulator to simulate user interaction. The set of pre-defined utterances is extracted from the skill's web page on the skill store. However, the accuracy of the testing analysis depends on the quality of the extracted utterances. An utterance or interaction that was not captured by the extraction process will not be detected, leading to inaccurate privacy assessment. Therefore, we revisited the utterance extraction techniques used by prior works to study the skill's behavior for privacy violations. We focused on analyzing the effectiveness and limitations of existing utterance extraction techniques. We proposed a new technique that improved prior work extraction techniques by utilizing the union of these techniques and human interaction. Our proposed technique makes use of a small set of human interactions to record all missing utterances, then expands that to test a more extensive set of voice apps. We also conducted testing on VUI with various accents to study by designing a testing framework that can evaluate VUI on different accents to assess how well VUI implemented in smart speakers caters to a diverse population. Recruiting individuals with different accents and instructing them to interact with the smart speaker while adhering to specific scripts is difficult. Thus, we proposed a framework known as AudioAcc, which facilitates evaluating VUI performance across diverse accents using YouTube videos. Our framework uses a filtering algorithm to ensure that the extracted spoken words used in constructing these composite commands closely resemble natural speech patterns. Our framework is scalable; we conducted an extensive examination of the VUI performance across a wide range of accents, encompassing both professional and amateur speakers. Additionally, we introduced a new metric called Consistency of Results (COR) to complement the standard Word Error Rate (WER) metric employed for assessing ASR systems. This metric enables developers to investigate and rewrite skill code based on the consistency of results, enhancing overall WER performance. Moreover, we looked into a special case related to the access control of VUI in multi-user environments. We proposed a framework for automated testing to explore the access control weaknesses to determine whether the accessible data is of consequence. We used the framework to assess the effectiveness of voice access control mechanisms within multi-user environments. Thus, we show that the convenience of using voice systems poses privacy risks as the user's sensitive data becomes accessible. We identify two significant flaws within the access control mechanisms proposed by the voice system, which can exploit the user's private data. These findings underscore the need for enhanced privacy safeguards and improved access control systems within online shopping. We also offer recommendations to mitigate risks associated with unauthorized access, shedding light on securing the user's private data within the voice systems.31 0Item Restricted LIGHTWEIGHT MUTUAL AUTHENTICATION PROTOCOLS FOR IOT SYSTEMS(University of Maryland Baltimore County, 2024) Alkanhal, Mona; Younis, MohamedThe Internet of Things (IoT) refers to the large-scale internetworking of diverse devices, many of them with very limited computational resources. Given the ad-hoc formation of the network and dynamic membership of nodes, device authentication is critical to prevent malicious devices from joining the network and impersonating legitimate nodes. The most popular authentication strategy in the literature is to pursue asymmetric cryptography. Such a solution is costly in terms of computing resources and power consumption and thus is not suitable for IoT devices which are often resource constrained. Moreover, due to the autonomous nature of the IoT nodes, relying on an intermediary server to manage the authentication process induces overhead and consequently decreases the network efficacy. Thus, the authentication process should be geared for nodes that operate autonomously. This dissertation opts to fulfill the aforementioned requirements by developing a library of lightweight authentication protocols that caterers for variant IoT applications. We consider a hardware-based security primitive, namely Physical Unclonable Functions (PUFs). A PUF benefits from the random and uncontrollable variations experienced during the manufacturing of integrated circuits in constructing a device signature that uniquely maps input bits, referred to as challenge, into an output bit(s) that reflects the PUF response. A fundamental issue with distributed authentication using PUFs is that the challenge-response exchange is among IoT nodes rather than the secure server and hence becomes subject to increased vulnerability to attacks. Particularly, eavesdroppers could intercept the inter-node interactions to collect sufficient challenge-response pairs (CRPs) for modeling the underlying PUF using machine learning (ML) techniques. Obfuscating the challenge and response through encryption is not practical since it requires network-wide management of secret keys and diminishes the advantages of PUFs. The dissertation tackles the aforementioned challenges. We first develop a novel authentication mechanism that is based on the incorporation of a PUF in each device. Our mechanism enables the challenge bit string intended by a verifier δy to be inferred by a prover δx rather than being explicitly sent. The proposed mechanism also obfuscates the shared information to safeguard it from eavesdroppers who strive to model the underlying PUF using machine learning techniques. Secondly, we further combine the advantage of PUFs, and the agility and configurability of physical-layer communication mechanisms, specifically the Multi-Input Multi Output (MIMO) method. We devise a protocol that utilizes an innovative method to counter attackers who might intercept the communication between δy and δx and uncover a set of CRPs to model δx’s PUF. Our protocol encodes the challenge bit using MIMO antennas array in a manner that is controlled by the verifier and that varies overtime. Additionally, we derive a two-factors authentication protocol by associating a Radio Frequency (RF) fingerprint with PUF. Such a unique combination obviates the need for traditional identification methods that rely on key storage for authentication. This identification mechanism enables the protocol to obfuscate the PUF response, circumventing the need for the incorporation of cryptographic primitives. Since both the PUF and the RF-fingerprint are based on unintended variations caused by manufacturing, we aim to increase robustness and mitigate the potential effect of noise by applying the fuzzy extractor. Such a protocol does not retain CRPs of a node during the enrollment phase, nor does it incorporate a cryptosystem. All the aforementioned techniques enable mutual authentication of two devices without the involvement of a trusted third party. The experimental results demonstrate the efficacy of the proposed protocols against modeling attacks and impersonation attempts.18 0Item Restricted The Humanitarian Vehicle Routing Problem with Non-Routineness of Trips(Purdue University, 2024-04-22) Alturki, Ibrahim; Lee, SeokcheonThe escalating frequency and impact of natural disasters have necessitated the study of Humanitarian Logistics (HL) optimization to mitigate human and financial losses. This dissertation encompasses three pivotal studies that collectively seek to address some of the numerous gaps identified in the nascent literature of HL optimization, particularly in conflict-ridden and low-security environments. The first study conducts a comprehensive survey on the application of Multi-Criteria Decision Making (MCDM) methods in HL, identifying a significant gap between academic research and practical challenges, and highlighting underexplored areas within multicriteria optimization in HL. The second study introduces innovative deterministic and possibilistic models to improve the safety and security of humanitarian personnel by developing a vehicle routing model that minimizes the predictability of trips, a novel aspect in HL research. This includes the introduction of the Humanitarian Vehicle Routing Problem with Non-Routineness of Trips (HVRPNRT), creation of a unique index to measure trip routineness and the provision of an approximate closed-form solution for the aid allocation subproblem, and introduces a novel case study from the ongoing civil unrest in South Sudan. The third study presents a novel heuristic solution algorithm for the HVRPNRT, which is the first of its kind, and outperforms the commercial solver CPLEX on some instances. This algorithm offers near-optimal solutions with reduced computational times and maintains feasibility under stringent security conditions, thereby advancing the field of security-aware HL optimization. Collectively, these studies offer significant contributions to the field of HL optimization, providing a recent through survey of the field, novel practical models, methodologies, and an algorithm that address both operational efficiency and security challenges, in an effort to bridge the gap between theoretical research and real-world humanitarian needs.36 0Item Restricted Applying Push-Pull-Mooring model to investigate non-malicious workarounds behavior(Saudi Digital Library, 2023) Aljohani, Nawaf; Warkentin, MerrillMore than half of the violations of information systems security policies are initiated by non-malicious activities of insiders. To investigate these non-malicious activities, we utilized the theory of workaround and argued that the application of neutralization techniques impacts the use of workarounds. We built our model using three theories: the theory of workaround, push pull-mooring theory, and techniques of neutralization. We identified the elements of workarounds related to non-malicious violations and proposed a theoretical perspective using the push-pull-mooring theory to investigate non-malicious workarounds empirically. We propose that non-malicious activities of insiders can be seen as a switching behavior, with push factors such as system dissatisfaction and time pressure, and pull factors such as convenience and alternative attractiveness. The mooring factors in our model are techniques of neutralization, including denial of injury, denial of responsibility, and defense of necessity. We employed the scenario-based factorial survey method to mitigate the effect of social desirability bias. Our mixed model analysis indicates that time pressure, convenience, denial of injury, and defense of necessity significantly impact an individual's likelihood of engaging in non-malicious workarounds. Additionally, the relative weight analysis of our model shows that convenience and time pressure explain most of the variance in our model.24 0Item Restricted An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for In-Vehicle Network(Saudi Digital Library, 2023-11-13) Alalwany, Easa; Mahgoub, ImadeldinConnectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this dissertation, to address all these concerns, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers, to detect normal and abnormal activities in the CAN bus. Moreover, we design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle's driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. Ensemble learning aims to achieve better classification results through the use of different classifiers that are combined into a single classifier. Furthermore, in the pursuit of real-time attack detection and classification, we propose IDS scheme that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa architecture. The Kappa architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. We build this system using the most recent CAN intrusion dataset provided by the IEEE DataPort. We carried out the performance evaluation of the proposed system in terms of accuracy, precision, recall, F1-score, and area under curve receiver operator characteristic (ROC-AUC). For the binary classification, the ensemble classifiers outperformed the individual supervised ML classifiers and improved the effectiveness of the classifier. For detecting and classifying CAN bus attacks, the ensemble learning methods resulted in a robust and accurate multi-classification IDS for common CAN bus attacks. The stacking ensemble method outperformed other recently proposed methods, achieving the highest performance. For the real-time attack detection and classification, the ensemble methods significantly enhance the accuracy of real-time CAN bus attack detection and classification. By combining the strengths of multiple models, the stacking ensemble technique outperformed individual supervised models and other ensembles.14 0Item Restricted The Use of Text Recognition, Lip Reading, and Object Detection for Protecting Sensitive Information from Shoulder Surfing Attacks(2023-07-19) Aldossari, Marran; Zhang, DongsongThe portability and convenience of laptops have propelled their use in public venues. However, the risk of unauthorized view of sensitive information displayed on these devices, including business data, emails, banking information, online trading information, and private chats, raises privacy concerns. In particular, shoulder-surfing attacks pose a significant threat, whereby individuals can steal sensitive information by looking over one’s shoulder. While researchers have developed various approaches to protect users' screens, such as text modification-based, gesture-based, and external tool-based, those methods have limitations in terms of effectiveness, protection, and usability. To address these limitations, this dissertation proposes, develops, and evaluates three novel methods for protecting sensitive information from shoulder-surfing attacks: detection and labeling (D&L), recognizing and labeling sensitive information in text entry (RLSITE), and “someone is close” (SIC). D&L is a method designed to protect sensitive information while browsing. It works by recognizing and labeling sensitive information in text entry and replacing it with a category label. The labeled and hidden sensitive information is then read to users through their headphones when they click the label. RLSITE is a method designed to protect sensitive information while typing. It works by automatically capturing and interpreting users' lip movements of the sensitive information, then replacing it with a category label and reading it to users through their headphones when they click the label. Finally, the SIC method automatically detects whether someone is close to a user. If so, it will alert the user while labeling the sensitive information and reading it to users through their headphones. The proposed methods have been empirically evaluated in controlled laboratory settings using various measures, including usability, effectiveness, and protection. Evaluation results demonstrate that D&L, RLSITE, and SIC outperform baseline methods in all measures. Furthermore, these innovations have significant practical implications, making them more resistant to shoulder-surfing attacks to browse or enter sensitive content on devices without compromising the usability of these devices.37 0