Browsing by Subject "Distributed Systems"

Now showing 1 - 3 of 3
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    Design, development and experimentation of a discovery service with multi-level matching
    (2013-11-20) Pileththuwasan Gallege, Lahiru Sandakith; Raje, Rajeev; Hill, James H. (James Haswell); Tuceryan, Mihran
    The contribution of this thesis focuses on addressing the challenges of improving and integrating the UniFrame Discovery Service (URDS) and Multi-level Matching (MLM) concepts. The objective was to find enhancements for both URDS and MLM and address the need of a comprehensive discovery service which goes beyond simple attribute based matching. It presents a detailed discussion on developing an enhanced version of URDS with MLM (proURDS). After implementing proURDS, the thesis includes details of experiments with different deployments of URDS components and different configurations of MLM. The experiments and analysis were carried out using proURDS produced MLM contracts. The proURDS referred to a public dataset called QWS dataset. This dataset includes actual information of software components (i.e., web services), which were harvested from the Internet. The proURDS implements the different matching operations as independent operators at each level of matching (i.e., General, Syntactic, Semantic, Synchronization, and QoS). Finally, a case study was carried out with the deployed proURDS. The case study addresses real world component discovery requirements from the earth science domain. It uses the contracts collected from public portals which provide geographical and weather related data.
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    Investigation of Backdoor Attacks and Design of Effective Countermeasures in Federated Learning
    (2024-08) Palanisamy Sundar, Agnideven; Zou, Xukai; Li, Feng; Luo, Xiao; Hu, Qin; Tuceryan, Mihran
    Federated Learning (FL), a novel subclass of Artificial Intelligence, decentralizes the learning process by enabling participants to benefit from a comprehensive model trained on a broader dataset without direct sharing of private data. This approach integrates multiple local models into a global model, mitigating the need for large individual datasets. However, the decentralized nature of FL increases its vulnerability to adversarial attacks. These include backdoor attacks, which subtly alter classification in some categories, and byzantine attacks, aimed at degrading the overall model accuracy. Detecting and defending against such attacks is challenging, as adversaries can participate in the system, masquerading as benign contributors. This thesis provides an extensive analysis of the various security attacks, highlighting the distinct elements of each and the inherent vulnerabilities of FL that facilitate these attacks. The focus is primarily on backdoor attacks, which are stealthier and more difficult to detect compared to byzantine attacks. We explore defense strategies effective in identifying malicious participants or mitigating attack impacts on the global model. The primary aim of this research is to evaluate the effectiveness and limitations of existing server-level defenses and to develop innovative defense mechanisms under diverse threat models. This includes scenarios where the server collaborates with clients to thwart attacks, cases where the server remains passive but benign, and situations where no server is present, requiring clients to independently minimize and isolate attacks while enhancing main task performance. Throughout, we ensure that the interventions do not compromise the performance of both global and local models. The research predominantly utilizes 2D and 3D datasets to underscore the practical implications and effectiveness of proposed methodologies.
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    Wireless ICS Training Platform
    (2019-04-29) Cassandra Boman, Nick Pohlman; Lin, William
    Essential public services, such as Electric, Water and Gas Utilities, are becoming increasingly reliant on network connected devices to control their processes. Wireless control systems are becoming more common in distributed systems, since they offer many advantages over hard wired alternatives. While cyber physical systems such as PLCs offer many advantages, they are also vulnerable to cyber-attacks. Military force readiness for defense of critical infrastructure against cyber-attacks requires state of the industry industrial control systems for cyber security training. A remote terminal unit using broad spectrum radio was integrated into an existing Water Treatment Plant SCADA system and provided to the US Army for training.