Detecting Anomalies in Intelligent Vehicle Charging and Station Power Supply Systems With Multi-Head Attention Models

Li, Yidong; Zhang, Li; Lv, Zhuo; Wang, Wei

doi:10.1109/tits.2020.3018259

Cited by 38 publications

(8 citation statements)

References 36 publications

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“…Moreover, it has been shown that using linearly projected queries, keys, and values ℎ times with learned linear projections contributes to extracting relationships between data [Li, Zhang, Lv & Wang (2021); Vaswani et al (2017)]. Thus, multi-head attention (MHA) modules perform attention functions in parallel on each of the projected versions of queries, keys, and values, and then concatenate their outputs as…”

Section: Self-attentionmentioning

confidence: 99%

“…Similarly to TCNs, attention mechanisms make it possible to extract dependencies among data and have been shown to outperform LSTM networks in several sequence modeling tasks. They are more capable of extracting features than LSTM networks, which produces more accurate models [Li et al (2021)]. In addition, they can process sequences as a whole and they enable more computation parallelization as MHA heads can run in parallel.…”

Section: Self-attentionmentioning

confidence: 99%

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Towards Intelligent Security Mechanisms for Connected Things

Araujo-Filho,

Campelo,

Kaddoum

2024

Anais Do XXXVII Concurso De Teses E Dissertações (CTD 2024)

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The widespread adoption of connected devices and the adoption of machine learning enable attackers to launch several cyber-attacks and adversarial attacks. Therefore, the goals of this thesis are to investigate and develop cutting-edge solutions to enhance the security of systems by effectively and efficiently detecting cyber-attacks while also defending systems that rely on ML from adversarial attacks. The main results of our thesis comprehend multiple awards, the publication of eight papers in prestigious journals, three conference papers, two patents, and one software registration. Furthermore, our research has been recognized and awarded as one of the two 2022 Microsoft Research Ph.D. Fellowship recipients in Security, Privacy, and Cryptography worldwide.

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Section: Self-attentionmentioning

confidence: 99%

Section: Self-attentionmentioning

confidence: 99%

Towards Intelligent Security Mechanisms for Connected Things

Araujo-Filho,

Campelo,

Kaddoum

2024

Anais Do XXXVII Concurso De Teses E Dissertações (CTD 2024)

View full text Add to dashboard Cite

show abstract

“…As a result, the security of the cyber level is of paramount importance for a secured and optimal operation of the overall CPS [68], [69]. Extensive research on the cyber-physical system security has been reported for green buildings [70], smart grids [71], [72], [73], [74], [75], transportation systems [76], [77], [78], medical CPS [79], [80], [81], autonomous robot system [82], [83], [84], smart city [85], [86], [87], and smart agricultural system [88], [89], [90].…”

Section: ) Cyber Levelmentioning

confidence: 99%

A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

et al. 2023

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With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.

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“…11 We also employed various machine learning methods for malware detection, [12][13][14][15][16][17][18][19] or for anomaly detection. [20][21][22] There has been much research on sentiment analysis in social networks. Traditionally, it mainly focuses on the polarity classification of the sentiment expressed in the text or the multi-classification of emotions such as disgust, anger, sadness, and happiness.…”

Section: Related Workmentioning

confidence: 99%

“…11 We also employed various machine learning methods for malware detection, 12–19 or for anomaly detection. 20–22…”

Section: Related Workmentioning

confidence: 99%

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang

Zhu

et al. 2021

International Journal of Distributed Sensor Networks

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“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

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Detecting Anomalies in Intelligent Vehicle Charging and Station Power Supply Systems With Multi-Head Attention Models

Cited by 38 publications

References 36 publications

Towards Intelligent Security Mechanisms for Connected Things

Towards Intelligent Security Mechanisms for Connected Things

A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

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