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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Unveiling time utilization in resource constrained environment

Unveiling time utilization in resource constrained environme...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rahul, Ch. Kousarr, Noushheen Yadav, T. Abhishek Keerthi, Pendam Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350359688

In response to the escalating challenges of resource-constrained environments, this research endeavors to provide a comprehensive analysis of Lightweight Cryptography (LWC). With the increasing prevalence of Internet of Things (IoT) devices and the growing need for secure communication in diverse settings, understanding cryptographic algorithms' performance becomes paramount. Existing analyses of LWC algorithms often lack a holistic evaluation, particularly in terms of certain performance dimensions. This research bridges this gap by meticulously examining a curated set of algorithms, including Ascon, Gift, Elephant, Tiny Jambu, Isap, Sparkle, PHOTON-Beetle, Romulus, and Grain. The study focuses on key performance metrics such as execution time, task clock cycles, and throughput, providing a nuanced understanding of these algorithms' efficiency in real-world scenarios. Innovatively, this research proposes novel ideas for enhancing the performance of LWC algorithms, extending beyond conventional security paradigms. By unveiling and comprehensively analyzing execution time, task clock cycles, and throughput, the study aims to provide actionable insights for optimizing cryptographic protocols. This research stands as a pivotal contribution to the evolving landscape of lightweight cryptography, offering practical solutions to enhance the efficiency and applicability of cryptographic algorithms in resource-constrained environments. © 2024 IEEE.

关键词： Clocks

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Unveiling the Nexus of Intelligence: Smart Grids Meter Infrastructure - A Holistic Probe into Security and Privacy Dynamics 7

Unveiling the Nexus of Intelligence: Smart Grids Meter Infra...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Aravind, K. Sangeeta, K. Yadav, Suman Avdhesh Kansal, Lavish Garg, Monica Sirisha, N. New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Iilm University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Lovely Professional University Punjab Phagwara India Lloyd Law College Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The conventional methods of power generation, distribution, and transmission are undergoing radical transformations as smart grids take center stage as the subsequent generation of power systems. Smart grids rely on enhanced monitoring structures, or AMI. An AMI is a collection of interconnected systems and networks designed to gather and process data gathered from smart meters. By analyzing the information gathered from smart meters, AMI also efficiently controls a number of apps and services that deal with electricity. For smart grids to work properly, AMI is an absolute must. Due to its composition of systems that are susceptible to security incidents, AMI is often targeted by these types of assaults. Due to the fact that attackers might undermine smart grid networks and privacy, AMI security is vital. When it comes to handling AMI security, the Key Administration System (KMS) is both an essential and difficult issue. As a result, KMS is rooting for the prospect of becoming a research hub for AMI's potential growth. The primary security issues with automated monitoring infrastructure are outlined in this survey study, which also emphasizes the usage of important management techniques to safeguard AMI. Our first step is to define enhanced metering architecture and its relationship to the smart grid by reviewing its key components. Concerns and difficulties with AMI's security are thereafter discussed. Furthermore, we provide a classification system for all the published works that discuss the AMI secure key management system. We conclude by outlining potential avenues for further study of KMS in AMI. © 2024 IEEE.

关键词： Smart power grids

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Multitasking Agricultural Robot for Farmer Assistance using Internet of Things 14

Multitasking Agricultural Robot for Farmer Assistance using ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Saranya, S. Thilak, K. Deepa Kalaiselvi, K. Arthi, K. Kumaresan, K. Department of Electronics and Communication Engineering Easwari Engineering College India Department of Networking and communications SRM Institute of science and Technology Chennai India Department of Computer science and Engineering KSR College of Engineering India

Farming was the fundamental improvement in the climb of inert human development, by which developing of local species made food overabundances that enabled people to live in metropolitan networks. The verifiable setting of cultivating began centuries earlier. Plants were independently evolved in somewhere near 11 region of the world. Present day agribusiness considering tremendous degree monoculture in the twentieth century came to lead plant result, but around 2 billion people really depended upon asset cultivating. It is the chiefwellspring of food, grub and fuel. It is the fundamental preparation of money related new development. The foremost unbiased of this undertaking is to design a playing out numerous assignments robot that fill various jobs of Agriculture and helps in aiding farmersin the field. It is planned to restrict made by farmers despite accelerate and precision of thework and helps in higher creation and yield. The control and noticing rely upon web of things, because of the veritable of the field status can be checked from wherever in the world using web[1]. In our endeavor used in two control system i.e., field control and robotcontrol. In field control the soil sogginess sensors and ultrasound sensors resources and updates field data through PIC Microcontrollerand the field is obliged by sensible exercises ofthe information is passed on to the farmer by an application. The robot having a camera to give the field of the vision so while it plays out its fundamental assignments, we can screen everything. The whole system can be changedto mechanized mode for limitless oversight ofthe property by controlled the robot by farmer. © Grenze Scientific Society, 2023.

关键词： Cameras

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Deepfake Detection System 2

Deepfake Detection System

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

作者： Shrivas, Shaswat Rai, Aditya Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet-pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet-pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies. © 2024 IEEE.

关键词： Biometrics

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Leveraging XGBoost based GBM for Proactive Detection of Man-in-the-Middle Cyber Attacks

Leveraging XGBoost based GBM for Proactive Detection of Man-...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Manjaragi, Shivanand V. Asrani, Deepak Kumar, Arvind Sandesh, R. Maindola, Meenakshi Sai, Aarush Department of Computer Science and Engineering Hirasugar Institute of Technology Karnataka Nidasoshi591236 India Department of Computer Science and Engineering MIET Uttar Pradesh Greater Noida India SCA MRIIRS Haryana Faridabad India Department of Computer Science and Engineering ATME College of Engineering Karnataka Mysuru India Department of Computer Science Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Department of CSBS Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350374933

Man-in-the-Middle (MitM) cyber-attacks provide a substantial risk to network security, since they allow hostile individuals to intercept and alter communications between two parties. Conventional detection approaches frequently fail to recognize these complex threats, requiring the creation of more resilient and precise solutions. This study introduces a model called XGBoost-based Gradient Boosting Machine (GBM) to identify MitM attacks in advance. The system employs extensive datasets from Kaggle, namely CICIDS 2017 and UNSW-NB15, for the purpose of training and evaluating the model. By carefully preparing the data, selecting relevant features, and doing thorough cross-validation with hyperparameter optimization, the suggested model attains exceptional performance metrics. The assessment findings indicate an accuracy of 0.98, precision of 0.97, recall of 0.96, F1-Score of 0.965, and a remarkable ROC-AUC score of 0.99. Our model regularly outperforms existing technologies like as Random Forest and Traditional GBM in identifying MitM attacks, achieving higher performance metrics across the board. This research demonstrates the effectiveness of the XGBoost-based GBM model in improving cybersecurity defenses and suggests that it might serve as a standard for future advancements in cyber-attack detection systems. The suggested system's exceptional accuracy and resilience make it an invaluable tool for proactively detecting and reducing MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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Real estate price prognostication through machine learning models

Real estate price prognostication through machine learning m...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Vestly, Doddamani John Thangallapelly, Neelima Manish, Gandra Sai Hariharan, Shanmugasundaram Kukreja, Vinay Venkateswara Reddy, H. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350348460

In the dynamic realm of real estate, house prices became the crucial part to encounter the set of hurdles to make lives better. Through meticulous feature engineering, which includes advanced data cleansing and feature manipulation, coupled with robust machine learning techniques, our model offers a nuanced understanding of valuation dynamics. The optimization process involves hyper parameter tuning and cross-validation, employing cutting-edge methodologies to extract latent patterns and yield meaningful insights from the underlying data. Leveraging algorithms of supervised learning algorithms like linear regression and K-fold, chosen for their ability to discern intricate patterns within diverse datasets, our research pioneers a transformative approach to real estate valuation. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared were used to ensure a robust and accurate predictive framework which were promising © 2024 IEEE.

关键词： Algorithms data science Feature engineering Machine Learning Predictive Modeling Real Estate Valuation

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Automatic Clustering of User Behaviour Profiles for Web Recommendation System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3365-3384页

作者： S.Sadesh Osamah Ibrahim Khalaf Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin Department of Computer Science and Engineering Velalar College of Engineering and TechnologyErode638012Tamil NaduIndia Al-Nahrain Nanorenewable Energy Research Center Al-Nahrain UniversityBaghdad64074Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia School of Digital Science University Brunei DarussalamJln Tungku LinkGadong BE1410Brunei Darussalam

Web usage mining,content mining,and structure mining comprise the web mining ***-Page Recommendation(WPR)development by incor-porating data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedﬁltering *** cluster user proﬁle-based clustering process is delayed when it has a low precision *** Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Proﬁle(UBP)model group’s similar user behavior on a dynamic update of *** Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate ***’s Filtering Framework(HFF)is designed toﬁlter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accurateﬁltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining ***-GD decreases the delay time in the end-to-end workﬂow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model deﬁnes higher Classiﬁcation Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

关键词： data mining web mining process search engine web-page recommendation accuracy

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