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

作者： Vardhan, Rajya Kumar, Rohan Supraja, P. SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350365269

The proliferation of the Internet of Things (loT) devices in agriculture has transformed data collecting, allowing for real-time monitoring and analysis, which is crucial for optimizing agricultural methods. However, the vulnerability of this dynamic agricultural data to cyber threats Jeopardizes the information's integrity and trustworthiness. This research study proposes a comprehensive framework for securing dynamic agricultural data generated from internet-connected loT devices. The proposed framework combines three main components: Honeycomb architecture, Intrusion Detection and Prevention Systems (IDPS), and sophisticated Al and ML principles. The Honeycomb architecture provides a strong foundation by combining distributed storage and decentralized control to reduce single points of failure and improve data resilience. The importance of this research stems from the essential role that agricultural data plays in developing future agricultural strategies and policies. A single falsified data point might lead to incorrect conclusions, affecting the agriculture sector's overall growth and sustainability. By applying the proposed cybersecurity framework, agriculture stakeholders may secure the integrity and confidentiality of their data, boosting trust in the trustworthiness of analyses and increasing the sector's resistance to cyber threats. This study contributes to the developing subject of cybersecurity, specifically addressing the unique issues given by dynamic agricultural data from loT sources. The study's findings are likely to assist stakeholders in the agriculture sector, such as farmers, researchers, and policymakers, by assuring the sustainability and resilience of agricultural data ecosystems in the face of emerging cyber threats. © 2024 IEEE.

关键词： Agriculture

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Contrastive Noise-Guided Invertible Network for Image Steganography

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 673-685页

作者： Zhang, Le Lu, Yao Lu, Guangming Harbin Institute of Technology at Shenzhen Department of Computer Science and Technology Shenzhen518057 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of Technology at Shenzhen Department of Computer Science and Technology Shenzhen518057 China

Image steganography aims to produce stego images through hiding secret images in the cover images to achieve covert communication. To simultaneously improve the invisibility and revealing quality of covert communication, this paper proposes a Contrastive Noise-Guided Invertible Network (CNGI-Net). Specifically, the Noised-Guided Invertible (NGI) mechanism is first proposed to actively and gradually generate adaptive noise in the forward hiding process using the interaction mechanism. The generated adaptive noise effectively guides and adjusts the cover-secret fusion process, as well as blurs the secret information in the stego images, which can significantly improve the transmission security. Besides, benefiting from the reversible property of NGI, high-quality revealed secret images can also be progressively decoupled from the stego images along the backward flow of NGI. To further ensure the invisibility and security of communication, Contrastive Hiding Learning (CHL) is proposed to improve the cover-stego similarity using the contrastive information. Extensive experiments demonstrate that the proposed CNGINet significantly promotes concealment security of transmission process and the quality of revealing for covert communication. Especially, the quality of stego and revealed secret images on 1, 2, 3 image hiding has been respectively promoted by 1.53, 0.43, 0.38 dB and 4.26, 3.45, 4.98 dB on ImageNet dataset, compared to SOTA steganography methods. © 1975-2011 IEEE.

关键词： Steganography Security Noise Consumer electronics Visualization Image edge detection Receivers Inverse problems Couplings Contrastive learning

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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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Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

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Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3691-3707页

作者： U.Sakthivelu C.N.S.Vinoth Kumar Department of Networking and Communications College of Engineering and Technology(CET)SRM Institute of Science and TechnologyKattankulathurChennai603203India

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s *** cyber-attacks lead to the compromise of data *** proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence *** modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update *** termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host *** anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of *** extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and *** is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s *** performance of the proposed system is compared with the state-of-the-art models.

关键词： Advanced persistent threats lateral movement detection dynamic deception remote desktop protocol Internet Protocol attack detection

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Hierarchical blockchain-based secure data storage in cloud using Merkle tree approach

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International Journal of Cloud Computing 2024年第6期13卷 605-626页

作者： Bruno, Judy Flavia Chelliah, Balika J. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multiple service providers based on the cloud to store and manage customer information. It has investigated the creation of an optimal system for shared cloud storage to facilitate flexible operations and information migration. This paper uses the Merkle tree approach to describe the experimental outcomes and designs of security systems provided by blockchain in cloud computing. We proposed that good authenticity and security be implemented with minimal significant degradation. The method was based on the position of the conscious Merkle tree that employs a three-tuple technique that has proven reliable in terms of authentication and encryption of security functions. The proposed system performance was measured by parameters such as retrieval time of information in blockchain technology, cost of storage, and average time with conventional information storage methods. The results indicate that the proposed blockchain technology solution has a response time almost 50% faster than conventional methods. Also, it claims that the storage cost would be around 20% lower. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Blockchain-based immunization against kleptographic attacks

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science China(Information sciences) 2024年第7期67卷 172-183页

作者： Changsong JIANG Chunxiang XU Jie CHEN Kefei CHEN School of Computer Science and Engineering University of Electronic Science and Technology of China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Department of Mathematics Hangzhou Normal University

Adversarial implementations of cryptographic primitives called kleptographic attacks cause the leakage of secret information. Subliminal channel attacks are one of the kleptographic attacks. In such attacks, backdoors are embedded in implementations of randomized algorithms to elaborately control randomness generation, such that the secrets will be leaked from biased outputs. To thwart subliminal channel attacks, double-splitting is a feasible solution, which splits the randomness generator of a randomized algorithm into two independent generators. In this paper, we instantiate double-splitting to propose a secure randomness generation algorithm dubbed SRG using two physically independent generators: ordinary and public randomness generators. Based on public blockchains, we construct the public randomness generator,which can be verified publicly. Hashes of a sufficient number of consecutive blocks that are newly confirmed on a blockchain are used to produce public randomness. In SRG, outputs from the two generators are taken as inputs of an immunization function. SRG accomplishes immunization against subliminal channel ***, we discuss the application strategies of SRG for symmetric and public-key encryption.

关键词： kleptographic attacks subliminal channel blockchain immunization randomized algorithm

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Predictive Salary Modelling: Leveraging Data science Skills and Machine Learning for Accurate Forecasting 9

Predictive Salary Modelling: Leveraging Data Science Skills ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Haseeb, Mohammed Ayman Viswanathan, Rishi Iyer, Kaushik Hota, Anish Raj Prathaban, Banu Priya School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798350377972

In approaching predictive salary modeling, this article identified important job positions and skills in the data science business environment. They show the need for a model that would allow employers to calculate the required starting salary while considering sectors, Python known languages, SQL, and ML. It also analyzes linear regression models, Random Forests, and neural network point of view about salary prediction. Analyzing the findings, it is proved that skill-based salary models should be further developed to meet industry requirements, advise students, and support employers. We have applied various models as follows with random forest performing the best giving out an R2 score of 0.971. The ideas for building faultless, precision, and skill-based salary estimation models are presented to the readers at the end of the article. © 2024 IEEE.

关键词： Prediction models

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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