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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Chandre, Pankaj R. Vanarote, Viresh Patil, Rajkumar Mahalle, Parikshit N. Shinde, Gitanjali R. Nimbalkar, Madhukar Barot, Janki Department of Computer Science and Engineering MIT School of Computing MIT ADT University Loni Kalbhor India Department of Information Technology MIT School of Computing MIT ADT University Loni Kalbhor India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Kondhawa India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhawa India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

Intrusion prevention has become a critical concern in today's increasingly complex and interconnected digital systems. Traditional intrusion prevention methods often rely on rule-based approaches that can be inflexible and prone to false positives and false negatives. The emergence of explainable artificial intelligence (AI) techniques has the potential to provide more accurate and interpretable solutions for intrusion prevention. This review paper surveys the current state of the art in explainable AI techniques for intrusion prevention, including their strengths and limitations. We begin by providing an overview of intrusion prevention and the challenges faced by traditional methods. We then explore the different types of explainable AI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Integrated Gradients, and their application to intrusion prevention. We also discuss the different types of datasets and evaluation metrics used in explainable AI-based intrusion prevention, including public datasets such as NSL-KDD and UNSW-NB15. Finally, we highlight the future research directions in explainable AI-based intrusion prevention, including the need for larger and more diverse datasets, better evaluation metrics, and more comprehensive explainable AI models. Overall, this review paper provides a comprehensive overview of the state of the art in explainable AI for intrusion prevention, and serves as a valuable resource for researchers and practitioners in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Additives

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SAC-ViT: Semantic-Aware Clustering Vision Transformer with Early Exit

arXiv

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arXiv 2025年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Wei, Dawei Li, Zhijun Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in global modeling but faces deployment challenges on resource-constrained devices due to the quadratic computational complexity of its attention mechanism. To address this, we propose the Semantic-Aware Clustering Vision Transformer (SAC-ViT), a non-iterative approach to enhance ViT’s computational efficiency. SAC-ViT operates in two stages: Early Exit (EE) and Semantic-Aware Clustering (SAC). In the EE stage, downsampled input images are processed to extract global semantic information and generate initial inference results. If these results do not meet the EE termination criteria, the information is clustered into target and non-target tokens. In the SAC stage, target tokens are mapped back to the original image, cropped, and embedded. These target tokens are then combined with reused non-target tokens from the EE stage, and the attention mechanism is applied within each cluster. This two-stage design, with end-to-end optimization, reduces spatial redundancy and enhances computational efficiency, significantly boosting overall ViT performance. Extensive experiments demonstrate the efficacy of SAC-ViT, reducing 62% of the FLOPs of DeiT and achieving 1.98× throughput without compromising performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Unveiling the Digital Shadows: A Comprehensive Exploration of Network Forensics Methodologies

Unveiling the Digital Shadows: A Comprehensive Exploration o...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Dhanalakota Sai Jyothika Nandini B. Lanitha H. Azath Ganeshan R Sajjad Ahmed School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore India Department of Artificial Intelligence and Data Science Karpagam Academy of Higher Education Coimbatore India School of Computing Science and Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The Internet has experienced significant growth in attacks that affect network security features such as confidentiality, integrity, and availability. Network security and network forensics work together to ensure system integrity, availability, and confidentiality. Network security measures prevent unau-theorized access, attacks, and breaches, while network forensics investigates and analyses the aftermath of security incidents. Network forensics refers to the process of collecting, analyzing, and preserving digital evidence from network activity in order to investigate and respond to security incidents, breaches, or other malicious activities. It involves reconstructing the events to determine attack methods, compromised data, and potential perpetrators. It lies at the intersection of digital forensics, incident response and network security. This paper is going to analyse the methodologies which are used in network forensics and application of new technologies in each network forensics methodology through ten figures. This paper critically examines network forensics methodologies, focusing on the evolution of these methods and the integration of emerging technologies. It assesses new strategies and discusses inherent issues and limitations of existing techniques, emphasizing the need for cutting-edge techniques to enhance the effectiveness of network forensic processes.

关键词： Protocols Scientific computing Forensics Scalability Prevention and mitigation Organizations Network security Threat assessment Stakeholders Monitoring

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EEG Sleep Stages Classification using Transformer-based Models: A Review

EEG Sleep Stages Classification using Transformer-based Mode...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yuin Torng Yew Jia Hong Puah Chow Khuen Chan Norita Mohd Zain Chean Khim Toa Sim Kuan Goh Department of Biomedical Engineering Faculty of Engineering Universiti Malaya School of Energy and Chemical Engineering Xiamen University Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia School of Computing and Data Science Xiamen University Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Sleep research is vital for understanding the complexities of human physiology and promoting overall health and well-being. Despite its importance, sleep disorders are widespread and can significantly impact daily functioning and quality of life. A critical aspect of sleep research is the classification of sleep stages, traditionally performed manually but now increasingly automated using computational techniques. However, current classification models face challenges, particularly in accurately distinguishing between sleep stages such as N1, REM, and wakefulness, and in classifying different types of sleep, such as micro sleep and macro sleep. Addressing these research challenges can lead to significant advancements in automated sleep scoring and contribute to the development of clinically deployable sleep diagnostic tools. Recently, there is a growing interest in leveraging advanced deep learning models, especially transformers, for analyzing EEG signals, which are crucial for sleep stage classification due to their temporal dependency. In contrast to convolutional neural networks (CNNs), which have been commonly used for this purpose, transformer architectures offer promise in capturing long-range dependencies in time-series data. Furthermore, the integration of multimodal learning approaches, such as combining EEG signals with other physiological data, holds the potential for improving the accuracy and robustness of sleep stage classification. This review article focuses on transformer-based models for sleep stages classification, and also to clarify the current gaps in sleep classification.

关键词： Deep learning Accuracy Sleep Reviews Computational modeling Computer architecture Transformers Brain modeling Electroencephalography Physiology

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Deep Learning-Driven Smart Signal Systems for Advanced Image and Video Processing in Urban Infrastructure

Deep Learning-Driven Smart Signal Systems for Advanced Image...

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Smart Technologies, Communication and Robotics (STCR)

作者： K. S. Bharaty Praveen SR Konduri Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh Department of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The increasing integration of intelligent systems in metropolitan environments emphasizes the important role effective image and video processing performs in applications including the traffic management, the monitoring of safety, and the urban planning. When handling dynamic scenarios, that is, changes in traffic conditions, occlusions, and the need for real-time analysis, conventional signal processing methods sometimes fail. Strong and adaptable solutions able to manage both these complexity as well as others are thus much needed. This work intends to introduce a deep learning-based smart signal system designed to process real-time video feeds for uses connected to urban traffic and safety. The proposed system uses recurrent neural networks (RNNs) for temporal sequence comprehension; conversely, convolutional neural networks (CNNs) are used for spatial data analysis. Two approaches used in order to improve the performance of the model are data augmentation and transfer learning, which increase the accuracy of decision-making using multi-modal data sources, video streams, sensor inputs, environmental parameters. This helps one to get beyond the challenge of small datasets. System performance is discovered using benchmark datasets and video from actual traffic events. Better than the methods now in use, the results show object detection has 95% accuracy and anomaly detection has 92% accuracy. Promising for use in smart cities, the proposed framework exhibits scalability and flexibility over a wide range of events. Its characteristics, traffic optimization, safety monitoring, and data-driven urban planning, which comprise traffic control, safety monitoring, and data-driven urban planning, have the ability to drastically change urban transportation infrastructure management.

关键词： Accuracy Recurrent neural networks Smart cities Urban planning Object detection Streaming media Traffic control Real-time systems Safety Monitoring

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BP-DDI: Drug-drug interaction prediction based on biological information and pharmacological text

BP-DDI: Drug-drug interaction prediction based on biological...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Dou, Mingliang Han, Han Chen, Genlang Guo, Fei Tang, Jijun Tianjin University College of Intelligence and Computing Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China NingboTech University School of Computer and Data Engineering Ningbo China Central South University School of Computer Science and Engineering Changsha China University of South Carolina College of Engineering and Computing United States

ISBN: (纸本)9781665468190

In the treatment of many diseases, combination drug therapy has been widely used and achieved good clinical efficacy. However, drug-drug interaction (DDI) may occur between multiple drugs and pose a huge threat to the health of patients. Therefore, predicting the presence or absence of DDI among multiple drugs is an important part of pharmacovigilance. Currently, various computational methods for DDI prediction usually use biological information such as molecular structures, targets and enzymes of drugs, or construct heterogeneous networks about drugs, diseases, and genes, so as to obtain abundant information related to drugs. In addition to biological data, pharmacology texts also contain a wealth of information about drug properties, but these texts have not yet been applied to DDI predictions. In this study, we first collect six types of pharmacology texts from DrugBank that can reflect properties of drugs, and propose a novel method named BP-DDI which can combine biological information and pharmacological text to realize DDI event prediction. BP-DDI first extracts biological features (chemical substructure features and target features) from biological data, and then extracts specific types of text features from the collected pharmacology text data. Finally, the biological features are fused with different types of pharmacological text features in order to predict DDI events. Our experiments demonstrate that BP-DDI outperforms existing methods on all three types of prediction tasks. BP-DDI achieves 0.9052 on ACC, and achieves 0.9612 on AUPR. © 2022 IEEE.

关键词： Drug interactions

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Generative AI: A Review on Models and Applications 2

Generative AI: A Review on Models and Applications

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Impact of Application Choice on iTracker Model Performance Evaluation: A Perspective 2

Impact of Application Choice on iTracker Model Performance E...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moghlebey, Adam Bou Haydar, Hady Moubayed, Abdallah Injadat, MohammadNoor School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan

ISBN: (纸本)9798350373363

Eye tracking is underpinned by the eye-mind hypothesis, which posits that individuals tend to direct their gaze toward the information they are currently cognitively engaged with. This aspect has garnered significant attention within deep learning research due to its versatile applications and research. The utilization of Artificial Intelligence (AI) in conjunction with webcams for tracking eye movements has gained increasing popularity in recent years and is anticipated to continue flourishing. This literature review aims to assess the current state and depth of the research in this domain and its various applications. We examine the different methods that employ eye tracking across a diverse range of applications, and by re-exploring existing research, we are able to filter out any overlooked potential or setbacks associated with the use of AI-webcam based systems, with the ultimate goal of maximizing their potential contributions to humanity. In our study, we analyze and evaluate a pioneering eye tracking algorithm, iTracker trained on a crowd-sourced data-set, GazeCapture within the context of the applications it may be used for. While we acknowledge inherent limitations, this widely adopted model forms a robust foundation for future refinements. Furthermore, our investigation encompasses diverse applications with varying accuracy requirements, making it invaluable for individuals seeking to integrate eye tracking into their projects. © 2023 IEEE.

关键词： Eye tracking

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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