Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology

The Intersection of Quantum-Resistant Technologies and Dark Web Privacy: A Comprehensive Review

P. Devi Sravanthi, Manas Kumar Yogi — Sat, 30 Nov 2024 00:00:00 +0000

The rise of quantum computing presents both opportunities and significant threats to current cryptographic systems, particularly those employed on the dark web to ensure anonymity and privacy. As quantum algorithms, such as Shor's, potentially render widely used encryption methods like RSA and ECC obsolete, there is an urgent need to explore quantum-resistant technologies. This review examines the intersection of quantum-resistant cryptographic protocols and dark web privacy, focusing on how post-quantum encryption techniques, such as lattice-based, hash-based, and multivariate cryptography, can safeguard the integrity of communication and transactions in this covert digital landscape. Additionally, the review discusses the challenges of integrating these technologies with existing privacy-preserving tools, such as Tor and I2P, which rely heavily on traditional encryption. The potential implications of quantum computing on dark web anonymity, the preservation of user privacy, and the ethical concerns surrounding enhanced cryptographic resistance are critically analyzed. This comprehensive review highlights critical advancements in quantum-resistant cryptography and their role in ensuring the continued security of privacy-centric platforms in a post-quantum world.

A Machine Learning Framework for Identifying and Preventing DDoS Attacks in Real-Time

Fri, 13 Dec 2024 00:00:00 +0000

Distributed Denial of Service (DDoS) attacks are a significant threat to network security, disrupting services by overwhelming systems with malicious traffic. Traditional methods of mitigating these attacks are often ineffective due to their reliance on static rules or manual intervention, which can be slow and limited in adaptability. To address this, we developed an automated, real-time DDoS detection system using machine learning to enhance efficiency and reliability.

Our system utilizes a combination of Random Forest, Neural Networks, and Logistic Regression models to analyze network traffic and detect DDoS attacks with high accuracy. Implemented with Python in a Flask-based application, this solution leverages machine learning algorithms to identify complex patterns associated with malicious activity. The result is a robust, scalable system capable of rapidly distinguishing between normal and attack traffic, helping to secure networks against evolving threats.

Identifying DDoS Attacks Using Machine Learning Approaches

Mon, 23 Dec 2024 00:00:00 +0000

Cloud computing offers users access to various cloud services, enabling efficient data storage and computational resources with minimal data overhead. However, this convenience comes with a significant risk: Distributed Denial of Service (DDoS) attacks. These attacks leverage multiple compromised computers to target network resources and servers, overwhelming them with messages, malformed packets, and connection requests, ultimately disrupting service for legitimate users. To address this challenge, this project proposes the design of an advanced algorithm that integrates multiple machine-learning techniques. The goal is to develop a model capable of detecting DDoS attacks more accurately. This approach aims to enhance the precision and reliability of DDoS detection, providing a robust defense mechanism for cloud computing environments, analyzing data and its parameters to check any redundancy in data values that may affect prediction results, to remove all the empty or uncertain values and use different types of classifiers to compare the model accuracy of detecting the DDoS attacks and lastly to build a model which aims to provide better model accuracy when compared to that of other models.

Enhancing Academic Excellence through Autonomy: A Data- Driven Analysis of NIRF Rankings in South Indian Higher Educational Institutions

Shiva Sumanth Reddy, Anil Kumar B, Jahnavi S, Manjunath D R, Girish N, Nandini C — Mon, 23 Dec 2024 00:00:00 +0000

Published as part of the research "Impact on NIRF Rankings", this study examines how colleges in Andhra Pradesh, Tamil Nadu, Kerala, and Telangana are performing regarding their autonomous status under the National Institutional Ranking Framework (NIRF). Using a detailed dataset from 2016–2023, the study employs advanced statistical techniques, including descriptive statistics, paired t-tests, Wilcoxon signed-rank tests, and multiple regression models to uncover differences in rankings of NIRF before and after autonomy.

Time series analysis with trend lines and moving averages can identify significant trends or rank changes across the years. Using machine learning models, they predict the rankings in the future and determine which factors have been most influential for improving ranking based on some of the commonly used machine learnings like Random Forest or Support Vector Machines (SVM). Statistical data analysis revealed a direct co-variation witnessed in autonomous status with an uplifted NIRF ranking (Teaching, Learning & Resources - TLR and Research and Professional Practice — RPC).

The study also indicates that Autonomous colleges could perform better in NIRF rankings as they can have a more excellent hold on innovative initiatives, academic optimization, and higher-performance research outputs. Collectively, these insights are helpful to researchers in educational data mining and higher education management as they might inform policymakers or institutional leaders interested in using autonomy to enhance academic quality. This is an exploratory study, and future research should adopt a longitudinal perspective and expand it to investigate other performance indicators, such as student satisfaction or employability.

The study highlights the importance of autonomy in improving India's ranking in higher education through a strong methodological framework and sophisticated data analysis techniques adopted by this research work.

Enhancing Bug Prediction with Machine Learning Algorithms

T. Bhaskar, Avanti Joshi, Kiran Nikam, Pagar Pratibha, Nale Divyani — Mon, 23 Dec 2024 00:00:00 +0000

Software Bug Prediction (SBP) enhances software quality by identifying defects early and reducing maintenance efforts and costs. Accurate bug prediction models help improve the overall software development process, but building an effective model remains challenging due to the complex nature of software systems.

This research explores the performance of five machine learning algorithms K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, Decision Tree, and Naïve Bayes in predicting software bugs. Our results indicate that Random Forest outperforms the others, achieving an accuracy of 93.7%, precision of 91.5%, recall of 92.1%, and an F1 score of 91.8%. KNN follows closely with an accuracy of 90.8%, while Naïve Bayes delivers the lowest results with 66.1% accuracy.

These findings confirm that machine learning techniques, particularly Random Forest, offer significant promise for improving software defect prediction, providing a reliable and scalable method for enhancing the software development lifecycle.