Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology

Cybercrime and Cybersecurity: Challenges, Emerging Trends, and AI-Driven Defense Mechanisms

Divya Dubey — 2025-04-11

In this era of growing reliance on technology, cybercrime encompasses an extensive range of malevolent actions exploiting loopholes in systems and human behaviour and has attained serious international concern. The elderly and users with less technological know-how remain particularly vulnerable groups. Effective cybersecurity requires artificial intelligence, risk mitigation strategies, user education, and ethical hacking. Situational Crime Prevention (SCP) aims to deny opportunities of commission to criminals by increasing efforts and risks while reducing the possible benefits for the criminals involved. They also support threat detection and incident response facilitated by artificial intelligence and machine learning. Effective international cooperation, flexible legislative frameworks, and continual monitoring of the evolving threatscape form the foundation for engaging in legislation. Raising awareness paired with user education becomes another crucial component in making people able to shield themselves online.

Application of Nature-Inspired Algorithms for Malicious Code Detection in Next Generation Programs: A Comprehensive Study

Atti Mangadevi — 2025-04-22

The increasing sophistication of malicious code poses a significant threat to next-generation software environments, including cloud computing and the internet of things. This review paper provides a comprehensive analysis of the application of Nature-Inspired Algorithms (NIAs) in detecting and mitigating these threats. We examine the use of genetic algorithms, ant colony optimization, and particle swarm optimization for tasks such as feature selection, anomaly detection, and malware classification. The paper also explores hybrid NIA approaches and their integration with machine learning to enhance detection accuracy. We highlight the challenges and opportunities associated with applying NIAs in resource-constrained environments like IoT and discuss future trends, including the development of emerging NIAs and the incorporation of explainable AI. The review concludes that NIAs offer a promising avenue for developing adaptive and intelligent security systems capable of defending against evolving cyber threats, but that continued research is needed.

Enhancing Transaction Security: ML-Based Credit Card Fraud Detection

M. Nikesh — 2025-04-23

The identification of credit card fraud is a crucial financial security tool that shields people and companies from illegal activities that might result in losses. The sophistication of fraudulent actions has increased over time, therefore financial institutions must use cutting-edge methods to analyze transaction patterns, spot irregularities, and stop fraud in real-time. Because of the growing number of online transactions, fraudsters are always coming up with new ways to take advantage of weaknesses, which means that fraud detection systems need to be updated and improved regularly. Thorough data collection is the first step in the fraud detection process. This includes information on transactions such as time, place, money, transaction data, and payment methods. In addition, fraud analysis requires contexts of user activity models such as cost trends, devices, IP addresses, and geographic position monitoring. To create a predictive model that distinguishes between true and fraudulent transactions, financial institutions often use past data for fraud. The next crucial stage after data collection is feature engineering, which entails transforming unprocessed data into useful features that increase the precision of fraud detection.

Blockchain for Data Integrity and Provenance

S. Uma — 2025-04-28

Ensuring data integrity and provenance is increasingly critical in today’s digital landscape, where threats such as tampering, forgery, and manipulation are prevalent. Blockchain technology offers a decentralized, immutable, and transparent solution for securing data and tracing its origin. This paper explores the core concepts of data integrity and provenance, evaluates blockchain’s contribution through features like cryptographic security, decentralization, and smart contracts, and compares it to traditional centralized security mechanisms. Use cases across supply chain, healthcare, finance, and digital governance demonstrate blockchain's real-world applicability in enhancing transparency, trust, and auditability. Furthermore, the paper addresses key challenges such as scalability, regulatory compliance, and energy consumption, along with mitigation strategies involving consensus mechanisms and Layer-2 solutions. A forward-looking view is presented through the integration of blockchain with emerging technologies like Artificial Intelligence, Internet of Things, and Cloud Computing. This convergence enables secure, autonomous, and real-time decision-making systems. By fostering trust, accountability, and innovation, blockchain-based solutions have the potential to shape a secure and sustainable digital ecosystem for the next generation of data-driven applications.

Harnessing IoT and Real-Time Analytics for Smart City Infrastructure Optimization

Raghu Ram Chowdary Velevela — 2025-04-29

The increasing urbanization of cities presents significant challenges in infrastructure management, public services, and sustainability. The growing population density, increasing energy consumption, and environmental concerns necessitate innovative solutions to improve urban living conditions. This paper introduces a real-time data analytics platform for smart city infrastructure management by integrating the Internet of Things (IoT). The platform enhances monitoring, optimizes maintenance, and fosters data-driven decision-making through continuous data collection, processing, and predictive analytics. By leveraging real-time analytics, cities can improve efficiency, reduce operational costs, and enhance citizen engagement by providing proactive solutions to urban issues. The system incorporates IoT sensors, cloud-based data storage, and AI-driven analytics to enable adaptive responses to traffic congestion, pollution levels, parking availability, and public utilities. Additionally, the system ensures seamless connectivity and real-time visualization of key metrics, providing actionable insights for urban planners and administrators. The integration of AI and machine learning further strengthens predictive capabilities, allowing for early detection of potential issues and automated decision-making processes. Furthermore, the proposed system is designed to be scalable, energy-efficient, and capable of integrating with existing smart city frameworks, thereby ensuring a holistic approach to urban sustainability. By adopting such a framework, cities can significantly improve livability, promote environmental stewardship, and foster a more connected urban ecosystem. This study presents the architecture, implementation, and advantages of the IoT-based smart city management system, demonstrating its potential to transform urban environments into more efficient, livable, and resilient spaces.