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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen Banu A S.K.B.Sangeetha Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

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Multi-modal fusion and vision transformers for robust early AMD prediction

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Evolving Systems 2025年第2期16卷 1-22页

作者： Annamalai, Akila Palani, Durgadevi Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India

Age-related macular degeneration is a chronic disease affecting a central area of the retina. Accurate disease identification aids in slowing down the progression of age-related macular degeneration and preserving vision. Various traditional techniques have been developed for effective age-related macular degeneration detection. However, traditional approaches failed to detect and classify the disease accurately and it consumes more time. However, traditional approaches failed to detect and classify age-related macular degeneration accurately. This research paper proposed an efficient model named as Multi-Modal Vision transformer model for the early and accurate prediction of age-related macular degeneration. This study aims to combine information from the Color Fundus Photography and Optical Coherence Tomography streams for performing efficient age-related macular degeneration diagnosis. The input images are needed to be preprocessed to enhance the image quality and make it suitable for further processing. The proposed framework integrated a Cascaded group attention transformer block which extracts the significant features from these modalities effectively. This block has the ability to solve computational complexity issues and attention head redundancy problems. Further, the multi-modal fusion method based on self-attention is introduced for fusing the features from Color Fundus Photography and Optical Coherence Tomography images. This fusion model is trained by applying both standard backpropagation and random gradient descent algorithms. For multi-class classification tasks, the fused features are classified into different classes based on the decision score. To visualize the single-modal and multi-modal output images in a heat map we applied a Class Activation Mapping model. Furthermore, the proposed technique is conducted on the Python platform and the performance is evaluated on different datasets with significant evaluation measures. This technique achieves

关键词： Image enhancement

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © 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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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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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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Enhancing Security in Logic-Locked Systems Through UART-Based Key Authentication

Enhancing Security in Logic-Locked Systems Through UART-Base...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer N

关键词： Molecules

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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Ensuring Trust in Blockchain-Based E-commerce Using Smart Contracts 5th

Ensuring Trust in Blockchain-Based E-commerce Using Smart Co...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Surendar, P.D. Kumar, Adhithi Satish Pavani, Venkata Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (纸本)9789819773701

In the ever-changing realm of e-commerce, building trust is crucial for both customers and sellers. Reputation systems play a vital role in enabling informed decision-making by providing vendor ratings and reviews. Conventional reputation systems are typically inadequate in dealing with contemporary issues including privacy, identity theft, and feedback fraud. In order to address these challenges, we suggest implementing a decentralized reputation system that places a high value on privacy and effectively counters existing risks. Our architecture utilizes two permissioned blockchains, Hyperledger Indy for decentralized identity management through verifiable credentials and Zero Knowledge Proof (ZKP) technology, guaranteeing anonymity and authenticity, and Hyperledger Fabric for executing smart contracts, facilitating intricate business logic. The system incorporates verified vendor identities, transaction feedback tokens, and discount tokens to encourage genuine evaluations. Smart contracts implemented on the Hyperledger Fabric platform provide the security and transparency of the feedback process, hence reducing the occurrence of biased feedback and identity assaults. This technique provides a strong and reliable answer to the issues faced in e-commerce. It ensures the protection of private information and enables the verification of digital identities while maintaining privacy. The implementation of feedback and discount tokens promotes the provision of precise input, mitigating unjust practices and bolstering the dependability and confidence in the system. We demonstrate the effectiveness of our methodology using Zero Knowledge Proofs (ZKP), highlighting its importance in establishing confidence within the context of blockchain. Our system represents a notable advancement in creating a safe, transparent, and reliable e-commerce environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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