Volume 16, Issue 1, February 2025

Smart Home Energy Management Algorithm Considering Renewables Energies with Storage System and Electric Vehicles

Halim Halimi, Gazmend Xhaferi

Department of IT, Faculty of Natural Sciences and Mathematics, University of Tetova, Tetova, R. N. Macedonia

Abstract - The efficient use of the incorporation of photovoltaic generation (PV) an electric vehicle (EV) and solar panel with the home energy management system (HEMS) can play a significant role in improving grid stability and economic benefit of the consumers. To reduce the peak load and electricity bill, was proposed a smart appliances control algorithm for the smart home energy management system (SHEMS) with integration of the renewable energy sources (RES), electric vehicles (EV) and energy storage system (ESS). The proposed algorithm decreases the peak load and electricity bill by shifting starting times of shifted appliances from peak to off-peak periods. Therefore, an energy storage system (ESS) and backup battery storage system (BBSS) are also considered for stable and reliable power system operation. The aim of this is to reduce energy usage and monetary cost with an efficient home energy management scheme (HEMS). In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances operating time.

Index Terms - Smart Home, HEMS, RESs, PV, Electric Vehicle (EV) and ESS

A Comprehensive Review of Cyber Threat Modeling and Phishing Resilience in Microsoft 365 Cloud Ecosystem

Pages: 7-9 (3) | [Full Text] PDF (308K)

Rana Ans Shahzad, M. J. Arshad

Department of Computer Science, University of Engineering and Technology, Lahore, Punjab 54890, Pakistan

Abstract - Microsoft 365’s widespread adoption has introduced critical security challenges, including AI-driven phishing campaigns (up 45% in 2023), unpatched legacy vulnerabilities (responsible for 22% of breaches), and insufficient threat modeling automation. This paper synthesizes 2022–2024 research to evaluate advancements in AI-augmented threat intelligence, multi-layered phishing defenses, and temporal vulnerability management. Through a systematic analysis of 10 key studies, we identify persistent gaps in adaptive attack detection (e.g., multi-channel phishing), user awareness (only 34% of employees pass phishing simulations), and hybrid-cloud governance. Our findings propose an integrated framework combining predictive threat modeling, automated email security protocols (e.g., DMARC adoption reducing spoofing by 85%), and legacy system modernization strategies, demonstrating a 40% reduction in manual effort and 60% fewer outages in pilot implementations.

Index Terms - Cyber Threat, Cloud Ecosystem, Phishing Defense and AI Augmented Threat

Fraud Detection in Credit Cards Using Machine Learning

Pages: 10-14 (5) | [Full Text] PDF (314K)

Tehreem Zahid

Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan

Abstract - The rapid growth of electronic transactions in the modern financial landscape has led to an increased prevalence of fraudulent activities, particularly in the realm of credit and debit cards. This research paper explores the application of machine learning algorithms for the detection and prevention of fraud in card transactions. By leveraging the power of artificial intelligence and data analytics, financial institutions can significantly enhance their capabilities to identify and mitigate fraudulent activities, thereby safeguarding the interests of both consumers and businesses.

Index Terms - Machine Learning, Data Science, Credit Card Fraud Detection and Algorithms

Enhancing IoT Devices Security by Integrating Zero Trust Model Using Machine Learning Techniques in a Public Cloud

Pages: 15-23 (9) | [Full Text] PDF (597K)

F. Zubair, Talha Majeed

Computer Science Department, University of Engineering and Technology Lahore, Pakistan

Abstract - The rapid growth of the Internet of Things (IoT) has introduced significant security vulnerabilities, necessitating advanced methods for protecting IoT ecosystems. This thesis presents a novel approach to enhancing IoT security by integrating the Zero Trust model with machine learning techniques. Specifically, a CNN-BiLSTM-based Intrusion Detection System (IDS) is proposed to detect and mitigate various cyber threats, including Distributed Denial of Service (DDoS), spoofing, and Man-in-the-Middle (MitM) attacks, within a Zero Trust framework. The CICIDS2017 dataset was employed for model training and evaluation, ensuring comprehensive coverage of IoT-related attack vectors. A comparative analysis was conducted using three machine learning models—CNN-BiLSTM, Support Vector Machines (SVM), and Linear Regression—evaluating their performance based on accuracy, loss, and the computed trust values of IoT devices. The CNN-BiLSTM model outperformed the other models, achieving 100% test accuracy and generating the highest trust scores for IoT devices. These trust values were dynamically computed as part of the Zero Trust policy enforcement, ensuring that only authenticated and verified devices could access the system. This research also introduces a feedback loop, where continuous monitoring of device behavior and machine learning predictions feed back into the system to adjust policies dynamically, further strengthening the security posture. The results demonstrate the effectiveness of combining Zero Trust principles with machine learning for robust, real-time IoT security, offering an innovative solution for mitigating advanced cyber threats.

Index Terms - ML, Zero Trust Model (ZTM), IoT, CNN and Cyber Threats

Volume 16, Issue 1, February 2025

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