Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
Emerging Trends in Cybersecurity
Secure Data Transmission using Cryptographic and Steganographic Technique
A Comprehensive Study on YouTube Spam Comments Recognition using Python, AI and ML
Enhancing Source Location Privacy in Networked Systems: A Review
Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
Emerging Trends in Cybersecurity
A Comprehensive Study on YouTube Spam Comments Recognition using Python, AI and ML
Enhancing Source Location Privacy in Networked Systems: A Review
Secure Data Transmission using Cryptographic and Steganographic Technique
Enhancing Source Location Privacy in Networked Systems: A Review
Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
Secure Data Transmission using Cryptographic and Steganographic Technique
Emerging Trends in Cybersecurity
A Comprehensive Study on YouTube Spam Comments Recognition using Python, AI and ML
Bitcoin was once seen mainly as a risky investment or a tradeable item. But now, many countries are starting to treat it like real money, with proper rules and regulations. This change affects how people and businesses use Bitcoin and how it fits into regular financial systems. This study looks at how the rules around Bitcoin are changing and what it means for its future as a currency. It also explores the benefits and challenges of this shift for users, companies, and governments. It also examines Bitcoin's price changes, market behavior, and whether it can truly work as everyday money. Overall, it offers insights into how Bitcoin is becoming a regulated currency and what that means for the future of cryptocurrencies.
Cybersecurity has become a critical priority in the digital era due to the exponential rise in cyber threats, attacks, and vulnerabilities. The rapid adoption of cloud computing, the Internet of Things (IoT), artificial intelligence (AI), and mobile technologies has expanded the threat landscape, exposing individuals and organizations to increasingly sophisticated cyber-attacks. This paper explores emerging trends in cybersecurity, including the integration of AI and machine learning (ML), Zero Trust Architecture (ZTA), blockchain-based security solutions, and post-quantum cryptography. It also examines the merits and limitations of recent technologies, identifies key challenges, and emphasizes the need for a holistic, collaborative, and forward-thinking approach to ensure robust protection. The findings of this study highlight that while technological advancement brings significant potential for defense, strong governance, education, and international cooperation must accompany it to effectively combat cybercrime.
Secure data transmission is increasingly vital amid rising cyber threats. This paper proposes a hybrid system combining cryptographic and steganographic techniques, implemented through a user interface for practical use. Users upload multipart documents, optionally compressed through zip, then encrypted with AES-256 for confidentiality. The AES key is encrypted using RSA for enhanced security. Both encrypted data and keys are embedded into a cover image using Discrete Wavelet Transform (DWT) steganography, chosen for its robustness. At the receiver end, the stego-image is processed to extract and decrypt the data, recovering the original files. Preliminary results show strong resistance to statistical attacks and high image quality (PSNR > 38 dB). This approach integrates symmetric and asymmetric cryptography with advanced steganography, offering a scalable solution for secure document sharing in sensitive applications, such as corporate and governmental communication.
The proliferation of spam comments on platforms like YouTube poses a significant challenge, degrading user experience and potentially spreading malicious content or misinformation. This paper provides a comprehensive overview of developing a YouTube spam comment recognition system utilizing Python, Artificial Intelligence (AI), and Machine Learning (ML) techniques. It details the entire pipeline, from data acquisition and preprocessing of textual comment data to feature extraction methodologies suitable for text, selection and training of appropriate ML and AI models, and robust evaluation strategies. The paper also discusses key Python libraries and frameworks instrumental in implementing such systems, alongside an exploration of common challenges like evolving spam tactics and dataset imbalances, and outlines potential future research directions in this domain. The goal is to equip researchers and practitioners with a foundational understanding for building effective automated spam detection systems for online comment sections.
Source location privacy has become a critical issue in networked systems. At present, many applications are utilizing networked systems because of the popularity of smartphone users. Protecting the privacy of the source location is important to keep private user data safe from people who want to use location data for criminal purposes. This article gives a comprehensive examination of all the current research initiatives, procedures, and strategies of implementation designed to improve source location privacy in networked systems. The methods were classified as per their basic ideas, and ratings were given on the basis of their effectiveness, usability, and shortcomings. Furthermore, this study identifies open research challenges and suggests potential opportunities for future studies to enhance the state-of-the-art in source location privacy protection.
