Volume-12 , Issue-5 , May -2024, E-ISSN: 2347-2693

The advent of Internet-of-Things (IoT) technology has ushered in a new era of unprecedented interconnectivity, by transforming our living space into a dynamic ecosystem. The challenging part of this is the security risk it poses on the network. Due to the vulnerabilities that are usually associated with smart devices, integrating them within the smart home ecosystem presents significant concern for the need of preserving data privacy, network traffic classification, and proper management of trusted devices. Various techniques have been employed in the development of Network Intrusion Detection System (NIDS) to safeguard the network against the evolving nature of attack deployed by cyber-criminals. This paper presents an Adaptive Hybrid Case-Based Neuro-Fuzzy System (HCBNFS) technique to the development of a robust and efficient Intrusion Detection and Prevention System (IDPS). The HCBNFS technique deploys the CBR as a detection engine to easily detect already known traffic patterns on the network, while the NFIS was deployed as a tuning factor to the reverse phase of the CBR to further investigate unknown traffic to the detection engines case-base. Five network packet features were selected as input variables to the proposed model. These features are the source IP, destination IP, source port, destination port, and network protocol. The model was trained using the CIC-IoT2022 dataset. For this study, the CIC-IoT2002 and a synthetic dataset were used for both testing and evaluating the performance of the system. The experimental results of the system using the CIC-IoT2022 dataset achieved 99% accuracy rate in intrusion detection, and recorded 99.5% for precision, recall, and F1-Score. The empirical evaluation of the proposed model validates its effectiveness and contributes towards the development of a more robust intrusion detection and prevention system. By enhancing data confidentiality, privacy, and security, the model represents a significant step forward in the safeguarding IoT-based smart home network against cyber-criminals.

Internet-of-Things, Artificial Intelligence, Neuro-Fuzzy Inference System, Case-Based Reasoning, Smart Home, Machine Learning

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Davies I.N., Taylor O.E., Anireh V.I.E., Bennett E.O., "Adaptive Hybrid Case-Based Neuro-Fuzzy Model for Intrusion Detection and Prevention for Smart Home Network," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.1-10, 2024.

Leveraging a synthesis of literature review and case studies, it illuminates how AI empowers organizations to discern intricate patterns and correlations within vast datasets. Through sophisticated algorithms and machine learning techniques, AI facilitates the nuanced understanding of the interplay between consumer demographics and purchasing behaviors, enabling targeted marketing strategies. Moreover, the study extends beyond demographics, encompassing psychographic, geographic, and behavioral factors through the amalgamation of diverse data sources. By employing predictive modeling, AI enables businesses to forecast market trends, optimize product positioning, and deliver personalized customer capabilities. Ethics around artificial intelligence-driven data analytics, incorporating consumer discretion and algorithmic fairness, are also addressed. Transparent methodologies and regulatory compliance are emphasized as crucial elements in fostering trust and mitigating risks. This paper explores the utilization of AI-driven data analytics in uncovering profound sales insights derived not only from demographics but also from diverse sources beyond traditional parameters. Machine learning deeper into consumer behavior patterns, market trends, and socio-economic indicators to gain a comprehensive understanding of their target audience. The paper discusses various methodologies employed in AI-driven data analytics, including predictive modeling, clustering techniques, and sentiment analysis, to extract valuable sales insights. Furthermore, it shows how crucial it is to incorporate data from various sources, including social media, geospatial information, and transactional records, to enrich the analytical process and enhance the accuracy of predictive models. Through real-world case studies and examples, this paper demonstrates how AI-driven data analytics can empower businesses to optimize their sales strategies, personalize marketing campaigns, and identify untapped market opportunities. By leveraging the capabilities of AI, organizations can move beyond traditional demographic segmentation and uncover nuanced insights that drive competitive advantage and foster sustainable growth.

AI-driven data analytics, sales insights, demographics, machine learning, predictive modeling, consumer behavior, market trends, socio-economic indicators, personalized marketing

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Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan, "AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.11-18, 2024.

Offering scalability and cost-effectiveness, serverless computing has become a promising paradigm for managing and deploying applications in the cloud. But as serverless architectures become more widely used, security and data protection issues have taken center stage. This study investigates techniques and approaches to improve serverless computing`s security and data protection in cloud environments. It looks at a number of serverless architecture-specific security issues, including function-level vulnerabilities, the shared responsibility paradigm, and the possibility of data disclosure. The study also looks into best practices and current security techniques, such as access control, encryption, monitoring, and compliance procedures, to help address these issues. This presentation offers an overview of the current security situation in serverless computing through an extensive examination of the literature and case studies.Organizations can take use of serverless computing`s advantages while maintaining the privacy, availability, and integrity of their data and apps by resolving these problems.

cloud computing, serverless computing, cyber- security, application security and privacy

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Sahibdeep Singh, Gurjit Singh Bhathal, "Improving Security and Data Protection of Serverless Computing in the Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.19-27, 2024.

The adoption of blockchains to effectively manage medical services is fast becoming popular for professional use and in patient-centered applications. Electronic medical records are highly sensitive with user-privacy data online with clinical services that relate to patients’ diagnosis and treatments. The features of these medical records necessitate their availability, accessibility, agility, confidentiality and security. These have been demystified with the birth of the blockchain technology that seeks to proffer platforms and application services devoted to dependability and reliability amongst other features. Thus, we propose a blockchain health information framework for healthcare facilities. Our ensemble yields a permissioned blockchain using a hyper-fabric ledger. Using this state of technology on a peer-to-peer blockchain with various actors to include patient, practitioners and other users playing the roles of the creation, retrieval and storage of medical data for a patient to aid interoperability, our ensemble produce a query response time of 0.56 secs and https response time of 0.42 secs for 2500-users, and 0.78 secs and 0.63 secs respectively for 7500-users.

Blockchain Technology, Electronic Medical Records, Data Security and Privacy, Interoperability

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David Ademola Oyemade, James Kolapo Oladele, "Secured Framework for Electronic Medical Record Protection and Exchange Using Blockchain Technology," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.28-34, 2024.

Cloud computing is a crucial element of contemporary computing structure, offering scalability, flexibility, and cost-effectiveness to organizations in various industries. This paper aims to thoroughly examine cloud computing, covering its evolution, key features, adoption trends, challenges, and future potential. Utilizing a mixed-methods approach, this study integrates literature reviews, case studies, surveys, and interviews to reveal valuable insights into how cloud computing is transforming businesses and society.

Cloud Computing, Evolution, Adoption Trends, Challenges, Future Prospects, Cloud Computing Impacts

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Deeraj Kumar Mutyala, "Exploring Cloud Computing Advancement and Future Technology Direction Impacts," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.35-41, 2024.

Variational Autoencoders (VAEs) are powerful machine learning models that can be deployed on mobile devices. However, VAEs are often deployed on resource-constrained mobile platforms, resulting in a high computational overhead. In this study, we present a novel framework, called the Miniaturizing Variations Auto Encoder (mVAE), to overcome the computational constraints associated with VAE deployment on mobile platforms. By leveraging advanced miniaturization techniques and integrating Amortized Stochastic Variational Inference (ASVI), this framework unlocks the full potential of VAE models in the mobile realm. Through extensive experiments and performance analysis, we aim to demonstrate the feasibility and efficiency of the m VAE framework in enabling the deployment of sophisticated machine learning applications on mobile systems. The findings of this study not only contribute to the advancement of mobile computing but also pave the way for a wide range of practical applications, empowering mobile users with powerful AI capabilities. Overall, this research contributes not only to theoretical foundations but also provides practical insights into implementation, addressing the need for efficient machine learning systems in mobile computing environments.

AI, Autoencoder, Amortized, Variational Inference, VAE, ASVI

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D.S. Ene, V.I.E. Anireh, D. Matthias, E.O. Bennett, "Optimization of Variational Autoencoder Model for Mobile Computing Environment Using Amortized Stochastic Variational Inference and Miniaturization Techniques," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.42-53, 2024.

Feature selection is crucial for improving the efficiency and effectiveness of machine learning models by identifying and choosing the most pertinent subset of features from the original dataset. This review article comprehensively surveys a diverse range of feature selection techniques in the context of Support Vector Machine (SVM) classification model in machine learning. This research work delves into several prominent techniques, including Mutual Information, Chi-Square, Sequential Feature Selection (SFS), Recursive Feature Elimination (RFE), LASSO, and Random Forest. The study reveals that RFE (Recursive Feature Elimination) emerges as the highly effective feature selection technique, demonstrating superior performance metrics compared to the other methods considered. Additionally, the study proposes the integration of hybrid algorithms to further enhance the performance of SVM classification models. Furthermore, this review extends its scope to encompass an evaluation of various kernel methods within the SVM classification paradigm, offering a comprehensive perspective on their efficacy and performance.

Feature Selection Technique, Chi-Square, RFE (Recursive Feature Elimination), SVM (Support Vector Machine), Kernel methods

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C. Dharmadevi, S. Thaddeus, "A Scientific Review of Feature Selection Algorithms and Kernel Methods for SVM Classification Models," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.54-58, 2024.

The research introduces a novel approach, Depth-based Extended Isolation Forest Feature Importance (DEIFFI), to enhance the interpretability of Extended Isolation Forest (EIF) algorithm in anomaly detection (AD). Anomaly detection is critical for identifying rare and significant deviations from norm in data. However, understanding the reasons behind classifying instances as anomalies poses a challenge. DEIFFI addresses this challenge by providing valuable insights, empowering users of EIF-based AD to conduct thorough root cause analysis. A noteworthy feature of DEIFFI is its capacity to improve interpretability without imposing heavy computational burdens. This is crucial for real world applications requiring efficient AD, particularly in situations demanding real-time decision-making. DEIFFI achieves remarkable results with low computational costs, making it an appealing option for practical implementations. With an accuracy of 0.914 and 0.942, precision of 0.607 and 0.64, recall of 0.773 and 0.96, and an F1 score of 0.68 and 0.768 on real and synthetic datasets, respectively. DEIFFI provides interpretable insights alongside competitive performance metrics, solidifying its suitability for real-time decision support. Importantly, DEIFFI contributes to AD by enhancing interpretability and assisting in unsupervised feature selection. This dual capability highlights practical utility of DEIFFI, improving EIF’s capabilities and extending its applicability across diverse AD scenarios.

Anomaly Detection, Explainable Artificial Intelligence, Extended Isolation Forest, Feature Selection, Interpretability, Outlier Detection

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Rahul Singh , Deepti Gupta, "Enhancing Interpretable Anomaly Detection: Depth-based Extended Isolation Forest Feature Importance (DEIFFI)," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.59-67, 2024.

Decentralized, secure platform that aids in managing loan operations is the loan management system that uses blockchain technology. Through the use of blockchain`s distributed ledger technology, security, immutability, and transparency are guaranteed. The loan lifecycle may be managed with this system in an open and effective manner, from the point of loan application to loan payback. Every stage of the loan application process is documented on the blockchain, resulting in an irreversible and permanent record of all transactions. The loan management system automates a number of loan-related tasks, including loan application, credit evaluation, approval, and disbursal, by using smart contracts. This guarantees more accuracy and transparency, gets rid of the need for middlemen, and shortens processing times.

Loan origination, Transparency, Security, Blockchain

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P.S. Gayke, Pawan Gajanan Badak, Dipak Namdev Pund, Rushikesh Dattatray Jawale, Mayur Bhausaheb Kotkar, Sarthak Shantilal Gaware, "P2P Loan Management System Using Blockchain in 6G Network System," International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.68-73, 2024.