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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Raj, Ritik Gupta, Siddhant Mishra, Ritik Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Customer churn, defined as customer defection is a big problem for businesses in today's competition. This paper takes an in-depth look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts;It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications. ©2024 IEEE.

关键词： Forecasting

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A Novel Technique for Detecting Concealed Malicious URLs within the Tor Network 8

A Novel Technique for Detecting Concealed Malicious URLs wit...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kanimozhi, P. Felcia Jerlin, I. Kumar, T. Ananth Ananth, Christo Mathusoothanan, K. Preethi, E. Ifet College of Engineering Computer Science and Engineering Villupuram India Grace College of Engineering Department of Computer Science and Engineering Tamilnadu Thoothukudi India Samarkand State University Uzbekistan Holycross Engineering College Department of Computer Science and Engineering Tamilnadu Thoothukudi India

ISBN: (纸本)9798350386578

Onion URLs lead to the dark web, a mysterious and secretive internet space with many websites. This paper proposes a novel content-based classification of. onion URLs. Given the concerns surrounding the dark web's anonymity, the project aims to classify these hidden services as either 'benign' or 'potentially harmful. ' The method involves scraping. onion websites for text and metadata, followed by natural language processing and machine learning analysis of word frequencies, sentiment, and topic modeling. A labeled dataset with strict criteria is utilized to train and fine-tune classification models. This project assists dark web users in identifying legitimate and risky services and contributes to discussions on content analysis and classification in anonymized online environments. With onion URLs mostly hidden and unindexed, the research advances privacy and security in this unique digital space. By automating the classification of. onion URLs, a valuable tool is provided, contributing to content analysis, online anonymity, and internet safety discussions. The problem statement seeks a Proof of Concept (PoC) to list active TOR-hosted hidden server URLs (.onion). © 2024 IEEE.

关键词： Anonymity

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Energy Aware Clustering with Medical Data Classification Model in IoT Environment

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computer Systems science & engineering 2023年第1期44卷 797-811页

作者： R.Bharathi T.Abirami Department of Computer Science and Engineering Cheran College of EngineeringK.ParamathiKarur639111India Department of Information Technology Kongu Engineering CollegePerunduraiErode638060India

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced *** to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly *** the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic *** this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT *** goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT *** proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct ***,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter *** design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the *** show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

关键词： Internet of things healthcare medical data classification energy efficiency clustering autoencoder

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Enforcing a Source-end Cooperative Multilevel Defense Mechanism to Counter Flooding Attack

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computer Systems science & engineering 2023年第1期44卷 67-79页

作者： Saraswathi Shunmuganathan Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of EngineeringKalavakkamChennaiTamil Nadu603110India

The exponential advancement in telecommunication embeds the Internet in every aspect of *** of networks all over the world impose monumental risks on the Internet.A Flooding Attack(FA)is one of the major intimidating risks on the Internet where legitimate users are prevented from accessing network *** of the protective measures incorporated in the communication infrastructure,FA still persists due to the lack of global *** of the existing mitigation is set up either at the traffic starting point or at the traffic ending *** mitigation at one or the other end may not be a complete *** insist on better protection againstflooding attacks,this work proposes a cooperative multilevel defense *** proposed cooperative multilevel defense mechanism consists of two-level of *** thefirst level,it is proposed to design a Threshold-based rate-limiting with a Spoofing Resistant Tag(TSRT),as a source end countermeasure for High-Rate Flooding Attacks(HRFA)and spoofing *** the second level,the accent is to discriminate normal traffic after Distributed Denial of Service(DDoS)traffic and drop the DDoS traffic at the destination *** Congruence-based Selective Pushback(FCSP),as a destination-initiated countermeasure for the Low Rate Flooding Attack(LRFA).The source and the destination cooperate to identify and block the attack.A key advantage of this cooperative mechanism is that it can distinguish and channel down the attack traffic nearer to the starting point of the *** presentation of the agreeable cooperative multilevel safeguard mechanism is approved through broad recreation in *** investigation and the exploratory outcomes show that the proposed plan can effectively identify and shield from the attack.

关键词： Flooding spoofing cooperative solution multilevel security filtering pushback

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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IoMT & ML Model to Detect Stress in Human 2

IoMT & ML Model to Detect Stress in Human

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Nadh Singh, Bhaludra R Ghanta, Sindhura Saishivani, Thokala Vaishnavi, Basingala Sai Sravya, Polishetty Shifa, Shaik Bhoj Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Hyderabad India

ISBN: (纸本)9798350364699

Today's fast-moving surroundings cause stress for heaps. Real-time stress administration may be troublesome, regardless of the allure of negative effects on tangible and insane fitness. ML, IoMT, and AI abilities change how we live and work and solve this question. "A Smart Biomedical Healthcare System to Detect Stress Utilizing IoT, Machine Learning and Artificial Intelligence,"intends to utilize physiological sensors like soul rate, hotness, dampness (sweat) sensors and first verbalizations to detect stress. The submitted approach can path and categories folk into "difficult"and "non-tense"circumstances. Our classification results manifest the advantage of our approach in certain-occasion stress identification. The main aim of the original search out supports a novel procedure of identifying early stress something did surroundings, knowledge atmospheres, and administrative settings to support laborers' and scholars' welfare and someday increase productivity. © 2024 IEEE.

关键词： mHealth

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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HMM-Based Secure Framework for Optical Fog Devices in the Optical Fog/Cloud Network

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Journal of Optical Communications 2023年第4期44卷 475-483页

作者： Sood, Sandeep K. Deep Singh, Kiran Department of Computer Science & Engineering Khalsa College of Engineering & Technology Amritsar India Department of Computer Science & Engineering Guru Nanak Dev University Regional Campus Punjab Gurdaspur India

In the present optical fog/cloud computing environment, optical line terminals and optical network units are used as the most promising optical fog devices (OFDs). The inherent characteristics of fog computing provide certain granted privileges to the attacker to hack devices and make them malicious. Also, existing security solutions generate false alarms that affect the performance of the underlying network. In this paper, a secure framework is proposed that not only predicts the malicious OFDs but also reduces false alarms. Hidden Markov model and intrusion detection system are used to detect the malicious one by computing the probability of shifting (POS) and then shift it to the virtual honeypot which is kept hidden by deploying it at the optical fog layer. In addition, it also reduces the generation of false alarm and logs all malicious activities for further analysis. In the experiment section, Python is used to validate the proposed framework. Further, HMM is simulated and tested in the MATLAB to reduce the false alarm rate. Results show that the proposed framework effectively reduces the false alarms and detects the malicious one and then shifts it onto the virtual honeypot efficiently. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fog

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Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第4期36卷 481-497页

作者： R.Jayaraj S.Lokesh Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032TamilnaduIndia

Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant indexing *** the outcomes,it is dif-ﬁcult to discover data that the user can be absorbed ***,in order to determine the signiﬁcance of the data,it is important to identify the contents in an informative *** annotation can be one of the greatest problematic domains in multimedia research and computer ***,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image ***,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class *** pre-processed images are sent to the segmentation approach for efﬁcient labelling technique using J-image segmentation(JSEG).Theﬁnal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classiﬁer,the unlabeled images are *** proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_*** the assistance of the pro-posed methodology,the unlabeled images are labelled.

关键词： Deep learning model J-image segmentation honey badger algorithm convolutional neural network image annotation

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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