With the enhanced usage of artificial-intelligence-driven applications, the researchers often face challenges in improving the accuracy of data classification models, while trading off the complexity. In this article,...
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Software aging is the gradual decline in performance that occurs with continuous software operation. As the demands on software systems increase, assessing the risk of software aging becomes crucial, especially in mod...
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The efficient distribution of function blocks across programmable logic controllers (PLCs) is critical to improving the performance of distributed automation systems. This paper presents a novel randomized greedy opti...
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作者：
Alsharif, MaramRawat, Danda B.
Department of Electrical Engineering & Computer Science WashingtonDC20059 United States
Security solutions based on machine learning enabled Intrusion detection systems (ML-IDS) have been popular for IoT edge device security. The cloud support has been considered to leverage heavy workloads off IoT edge ...
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Vision language models (VLMs) such as LLaVA, ChatGPT-4, and Gemini have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data showing impressive performa...
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Open Radio Access Networks (O-RANs) are transforming the landscape of telecommunications to better performance and higher cost-efficiency by enabling network operators to integrate diverse vendor components. Neverthel...
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Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we add...
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Personalized federated learning (PFL) has emerged as a promising technique for addressing the challenge of data heterogeneity. While recent studies have made notable progress in mitigating heterogeneity associated wit...
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作者：
Alfardus, AsmaRawat, Danda B.
Department of Electrical Engineering and Computer Science WashingtonDC20059 United States
The complex distributed systems installed in vehicles represent the cutting edge of the automotive industry. Electronic control units communicate with each other by sending and receiving messages over a well-known pro...
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ISBN:
(纸本)9798331510633
The complex distributed systems installed in vehicles represent the cutting edge of the automotive industry. Electronic control units communicate with each other by sending and receiving messages over a well-known protocol called the Controller Area Network (CAN) bus system. Car Broadcast is a new, but insecure, way to communicate between external electronic devices. However, today's vehicles are on the brink of security because the CAN network lacks a secure authentication and authorization mechanism. The rise in cyber attacks such as spoofing, spoofing and most commonly denial of service attacks is the result of uncertain measures in the CAN bus network. Although many intrusion detection systems have been developed to provide more secure communication in the vehicle, CAN is still far from being the most secure communication protocol. Since cyber attacks can come from a little-known or completely unknown source, it is essential to take a probabilistic approach based on previous observations from previous attacks. Therefore, we propose a new intrusion detection system that uses binary logistic regression (BLR) to detect and mitigate attacks on a CAN bus network. Binary logistic regression is a very popular predictive model that is widely used in various fields. In binary logistic regression, data is first analyzed, then the probability of individual events is estimated by observing previous data, and then a binary classification model is created. An evaluation of the well-known Nsl-kdd and Kdd-99 datasets shows that our proposed method has a dominant overall performance. The final detection rate is 099.031% using Nsl-kdd with a positive rate as low as 00.073% and the rate of detection is 099.043% using kdd-99 with a positive false rate as low as 00.046%˙ Specifically, to detect denial of service (DoS) attacks, the proposed system achieved a detection rate of 099.061% and 099.098% in Nsl-kdd and kdd-99 dataset respectively. Comparative evaluation confirmed that BLR i
Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes f...
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Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes foreground information to facilitate background *** uses noise-based weights to fine-tune the *** both noise and foreground information contribute to the recovery of the *** jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is *** fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background *** describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint *** temporal constraint is adjusted *** foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability *** experimental results of vast real videos illustrate the effectiveness of our *** with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate ***-noise experiments show the noise robustness of our method.
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