Browsing by Author "Hameed Addeen, Hajar"

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A Dynamic F A Dynamic Fault Tolerance Model for Micr ance Model for Microservices Ar vices Architectur chitecture
(South Dakota State University, 2019-08) Hameed Addeen, Hajar; Yi, Liu
Microservices architecture is popular for its distributive system styles due to the independent character of each of the services in the architecture. Microservices are built to be single and each service has its running process and interconnecting with a lightweight mechanism that called application programming interface (API). The interaction through microservices needs to communicate internally. Microservices are a service that is likely to become unreachable to its consumers because, in any distributed setup, communication will fail on occasions due to the number of messages passing between services. Failures can occur when the networks are unreliable, and thus the connections can be latent which may lead to failure or slow response. This might be a problem for synchronous remote calls actively waiting for a response. If they do not use a proper timeout mechanism, they may end up waiting for an extended amount of time. Applications usually set a timeout for all remote calls to avoid hanging of the whole application due to network failure or component failure. However, this timeout needs to be set carefully to make the system or microservice application to work as required. This would prevent further problems because if a remote call is waiting too long for a reply, it can slow down the system in its entirety, and if a connection timeout is extremely fast, it may ignore a response that is sent after timeout. This thesis proposes a dynamic fault tolerance (DFTM) Model to improve the stability and resilience of the microservices architecture. The Model is designed using a two-states Circuit Breaker called Switch Circuit Breaker with Markov-Chain. In addition, a modified Circuit Breaker (three states – open, closed, and half-open) to Switch Circuit Breaker (two states – open and closed) is presented here. The Circuit Breaker uses timeout to detect fault but timeouts usage hinges on assumptions about the real-time behavior of the system and awaiting process can be deduced from the occurrence of a timeout that a failure has occurred. Therefore, DFTM model adopted Markov Chain based model to detect fault without a timeout. Then, it sends the fault directly to Switch Circuit Breaker that uses a 2-states to cover the faults. An important finding is that the DFTM model presents a solution to the problem of transient failures or faults in the inter- service communication of microservices architecture. Also, it improves the performance and reliability of microservices architecture.
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Cyber Physical Attacks and Detection MEthods in Water Distribution Systems
(The University of Alabama, 2025) Hameed Addeen, Hajar; Yang, Xaio
Modern technologies adopt Internet of Things (IoT) devices to increase water management efficiency and enhance water quality services. However, the limitations of IoT devices, such as small sizes and poor security, weaken the Water Distribution System (WDS) security and many attackers compromise the critical components of WDS. Cyber-physical attacks (CPAs) are considered one of the biggest challenges that decrease the security factors in WDS by disrupting normal operations and tampering with the critical data of the water system. Therefore, this dissertation proposes an anomaly detection method to detect cyber-physical attacks and mitigate their bad impacts on the components of WDS. First, we study the current state-of-art for the common cyber-physical attacks and common detection mechanisms for the WDS. Also, we compare CPA attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, and limitations. Second, we propose a deep learning model based on a conditional variational autoencoder (CVAE) to detect cyber-physical attacks. The CVAE model shows a highly effective way to maximize the chance of generating the desired output and detecting CPA attacks quickly. We also train CVAE on (BATADAL) real medium-sized water distribution dataset and demonstrate high-efficiency results. Experiment results indicate that our proposed method of CVAE can detect all the listed attacks with high accuracy and reduce false alarm issues. Then, we evaluate the proposed models’ performance using various metrics, including accuracy, precision, recall, and F1 score. In addition, we compare the CVAE model with existing models to detect CPA attacks, and the results show that we reach a high accuracy of 98%. Third, we designed an adversarial attack on our CVAE model to show the security risks of this attack and the negative impact on the model’s accuracy. We apply the Fast Sign gradient method to fool the CVAE model and predict the anomalies as normal data rather than anomalies. Then, we propose our novel defense approach, the CVAE defense model, to detect adversarial attacks. The CVAE defense model consists of adversarial detection and the CVAE defense models. The adversarial detection model adopts CNN and LSTM methods to classify data as adversarial or clean. The CVAE defense model takes the output of the adversarial detection model and passes it to our proposed noise generation method. After that, the noise generation method is produced and passed to the CVAE model and activation function. Finally, we calculate the Euclidean distance between the reconstructed output and input vectors and compare it to the threshold. If the output is less than the threshold, there is no attack. Otherwise, the output should be one, and there is an attack. The results show that our CVAE defense model can detect adversarial attacks and increase the performance to an overall 92%.
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