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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jadon, Aryan Singh Vanusha, D. Delsi Robinsha, S. Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350365269

The Content Restriction System (CRS) harnesses the power of machine learning to mitigate the proliferation of harmful content in digital spaces. The current systems are mostly made by private social media giants and there is a lack of a wholesome all-round CRS for all. This can be useful to remove bad comments in the comment section, removing it from the reach of children as well. It detects profanity, harmful and violent behavior. We are creating this using Long Short Term Memory (LSTM) and the algorithm is modified such that it detects the comment as quickly as possible. This is an improved version of this algorithm that can give us faster results. For this we have implemented several filters before sending a text object for analysis minimizing the need for analyzing all the data objects. After these filters are applied, we send the data objects for analysis and even this stage takes very less time. This algorithm is meant to work for large datasets with over a million data points. In the future, we can use it as a scanner tool to check the client's database for such harmful content and mark it up for them. FAIRMod will increase the overall health of the internet and its accessibility. Our goal is to achieve 90% accuracy while having faster detection speed. © 2024 IEEE.

关键词： Application programming interfaces (API)

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Network Intrusion Detection System Using Optuna 5

Network Intrusion Detection System Using Optuna

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Parekh, Nishank Sen, Arzob Rajasekaran, P. Jayaseeli, J. D. Dorathi Robert, P. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chengalpattu India School of Computing Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331518097

The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the

关键词： Network intrusion

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning 31

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhai, Jiaqi Shi, Xuanhua Huang, Kaiyi Ye, Chencheng Hu, Weifang He, Bingsheng Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore

ISBN: (纸本)9798331506476

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication. © 2025 IEEE.

关键词： Embeddings

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Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems 4

Enhanced CNN Model for Lettuce Disease Identification in Ind...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Malaisamy, Janani Dept. of Computing Technologies School of Computing College of Engineering & Technology Srm Institute of Science & Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

ISBN: (纸本)9798331540364

India, like many other nations, heavily relies on agricultural exports to fund its national budget. Economic losses may occur when crop and plant diseases reduce production quality and quantity. Hence, it is crucial to detect infections early on in crop farming. Commercial crops grown using aeroponic vertical farming techniques include lettuce in particular. To increase the crop yield, the crop should be free from disease-causing agents like viral, bacterial, or fungal. As a response, scientists have utilized deep learning algorithms for the automatic identification of abnormalities in both indoor and outdoor farming crops by capturing leaf images. In this work, fine-tuned convolutional neural networks (CNN) were utilized to identify diseases in the aeroponic lettuce crops in an effective manner. Further, the Convolutional Neural Networks (CNN) were fine-tuned by training them on lettuce images. While testing, a higher accuracy of 95.6258% was achieved. Hence, this research work signifies that the proposed model would be appropriate for lettuce disease classification in controlled environment agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 376-387页

作者： Maojiao Ye Danhu Li Qing-Long Han Lei Ding IEEE the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial ***,reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with firstorder and second-order players,*** the developed algorithms,the observed disturbance values are included in control signals to eliminate the influence of disturbances,based on which a gradient-like optimization method is implemented for each ***,a signum function based distributed algorithm is proposed to attenuate disturbances for games with secondorder integrator-type *** be more specific,a signum function is involved in the proposed seeking strategy to dominate disturbances,based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players'objective *** Lyapunov stability analysis,it is proven that the players'actions can approach a small region around the Nash equilibrium by utilizing disturbance observerbased strategies with appropriate control ***,exponential(asymptotic)convergence can be achieved when the signum function based control strategy(with an adaptive control gain)is *** performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform(V-REP)and MATLAB.

关键词： Distributed networks disturbance rejection Nash equilibrium networked games

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Securing IoT Devices Against Emerging Security Threats: Challenges and Mitigation Techniques

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Journal of Cyber Security technology 2023年第4期7卷 199-223页

作者： Aziz Al Kabir, Mohammed Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering UEL London United Kingdom

The increasing prevalence of IoT devices has brought about numerous security challenges due to their relatively simple internal architecture and low-powered hardware warranted by their small footprint requirement. As there are billions of IoT devices in use today, the sheer number of such devices pose a great security challenge as they are often constrained by a number of hardware and software limitations in addition to being designed with a focus on convenience, ease of use, mass production, and low cost, rather than security. The seemingly exponentially increasing number of such devices make it harder to keep track of–and patch–insecure IoT devices. This paper explores the common security threats, attacks, and vulnerabilities relating to IoT devices and highlights the challenges associated with securing them against emerging security threats and cyberattacks. Due to their role as gateways to connected devices and susceptibility to forming botnets or facilitating man-in-the-middle attacks, IoT devices are a lucrative target for cybercriminals. The paper discusses various remediation and mitigation techniques that can be implemented to better secure IoT devices, including access control mechanisms, secure communication protocols, and regular updates and patches. By better understanding the security challenges associated with IoT devices and implementing effective mitigation techniques, individuals and businesses can ensure the safety, security, and privacy of their connected devices and networks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cybersecurity

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Fast feature selection algorithm of EEG data based on convolutional neural network 6

Fast feature selection algorithm of EEG data based on convol...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Liu, Bin Cao, Liming Fang, Tianke Xiamen University of Technology School of Software Engineering Xiamen China Mapua University School of Information Technology Manila Philippines Xiamen University of Technology School of Computing and Information Engineering Xiamen China

ISBN: (纸本)9798331516550

Electroencephalography (EEG) data is invaluable for investigating EEG physiology, neuroscience and clinical medicine. However, EEG signals are characterized by temporal nature, high dimensionality, and noise, which make it difficult for traditional feature selection methods for EEG data to extract useful information effectively with high computational effort. To solve this problem, this paper proposed a fast feature selection algorithm for multichannel EEG data based on a convolutional neural network (FFSEEG-GNN). Specifically, the algorithm pre-processes the multichannel EEG data, then extracts the waveform features and builds a CUDA architecture using GPUs to convolutional neural network operations. Finally, the most representative subset of features is selected in order of feature importance and feature extraction is performed. In addition, the simulation comparison experiment proves that the algorithm achieves 92.60% in terms of precision rate. Therefore, the program has accurate feature selection capability, which is expected to play an essential role in clinical medicine. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Nutrient Deficiency Prediction System for Groundnut Leaf

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Intelligent Automation & Soft computing 2023年第5期36卷 1845-1862页

作者： Janani Malaisamy JebakumarRethnaraj Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattuTamil Nadu603203India

Prediction of the nutrient deﬁciency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identiﬁcation also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deﬁciency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deﬁciency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sﬁrst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areﬂat-tened and given to the dense layer(DL)***,the Maximum Margin clas-siﬁer(MMC)is deployed and takes the input from DL for the classiﬁcation process toﬁnd *** dataset used in this work has no visible symptoms of a deﬁciency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deﬁciency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

关键词： CNN chlorophyll groundnut crop deep learning nitrogen deﬁciency

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