Navigating the Digital Frontier: A Comprehensive Analysis of Cybersecurity and Information Security in the Modern Era
Exploring Natural Language Processing Chatbots and Phishing Website Detection: A Literature Perspective
Survey on ATM Security Enhancement using Encryption Technology
A Growth of Artificial Intelligence in Crime Detection usages in Law Enforcement
Comparative Analysis of Deep Learning Models for Financial Fraud Detection
Exploring Natural Language Processing Chatbots and Phishing Website Detection: A Literature Perspective
Impact of Artificial Intelligence on Cybersecurity: A Case of Internet of Things
A Growth of Artificial Intelligence in Crime Detection usages in Law Enforcement
Comparative Analysis of Deep Learning Models for Financial Fraud Detection
An Extensive Overview on Dark Web
In today's rapidly evolving digital landscape, cybersecurity and information security are crucial for safeguarding critical assets across organizations and individuals. Cybersecurity focuses on protecting internet-connected systems, including hardware, software, and data, from cyber-attacks. In contrast, information security ensures the confidentiality, integrity, and availability of information, regardless of its digital or physical form. Although these terms are sometimes used interchangeably, they represent distinct concepts and methodologies, addressing specific threats and risks within the digital ecosystem. This review examines the differences and overlaps between cybersecurity and information security, highlighting their intersection in creating comprehensive protection strategies. While cybersecurity addresses external threats such as malware, phishing, and hacking, information security focuses on policies and procedures to prevent breaches—whether accidental or intentional. Recent trends, regulatory requirements, and the increasing complexity of cyber threats underscore the importance of adopting robust security measures. This article also explores the impact of cyber-attacks, including financial losses, reputational damage, and legal consequences stemming from noncompliance with data protection regulations. Additionally, the role of emerging technologies, such as artificial intelligence, machine learning, and blockchain, in enhancing the security landscape is discussed. The conclusion emphasizes the importance of a unified approach integrating cybersecurity and information security to mitigate risks effectively and ensure the long-term protection of sensitive data.
The paper proposes a novel approach that integrates machine learning and NLP in order to identify phishing sites and create multilingual interaction by providing better user engagement through the chatbot. It will utilize the XG-Boost algorithm in order to show the phishing detection system with more than 90% accuracy rate in the identification and classification of websites as legitimate or phishing for a set of 10,000 websites. A major contribution of this work is the embedding of a multilingual chatbot, developed on Dialog- flow, with support for English, Hindi, and Marathi, thereby broadening the possible community of users for the system. This paper describes the architecture of the system at its different layers- feature extraction, model training, and its integration with the chatbot. The proposed work fills the gaps in earlier literature as it provides a user interface accompanied by robust detection. This system will also be extended by the provision of language support by adding more languages in the near future and also by increasing the detection accuracy using deep learning models. The results demonstrate that combining machine learning with user-centric design may improve the detection of phishing sites considerably and enhance user engagement.
This paper introduces a novel ATM security system that integrates biometric authentication and advanced encryption techniques to enhance safety. The system employs facial recognition and additional biometric methods to strengthen user identification and prevent fraud. It also includes features like helmet or mask detection and one-time password (OTP) authentication to ensure secure transactions. The security framework leverages technologies such as Convolutional Neural Networks (CNNs) and FaceNet for accurate facial recognition. The proposed ATM design includes two levels of security: biometric authentication using a facial image combined with a 4-digit password. Additionally, encryption is reinforced with a wavelet- based Advanced Encryption Standard (AES) algorithm, providing further protection for sensitive information. Future work may explore refining these methods and incorporating additional layers of security to keep up with evolving threats.
Artificial intelligence (AI) is increasingly becoming a valuable tool for detecting illegal activities and holding offenders accountable. Among the most advanced AI technologies, facial recognition is widely used by law enforcement agencies for crime detection and prevention. This study aims to (i) analyze the use of artificial intelligence in law enforcement and (ii) identify the drawbacks of AI. A total of 75 respondents were selected from Kanyakumari and Tirunelveli districts, using simple random sampling due to time constraints. Among the respondents, 38 were from Tirunelveli and 37 from Kanyakumari. The Garrett ranking method was employed to analyze the data. The study found that facial recognition ranked first with a Garrett score of 62.1, followed by language processing with a score of 58.97. The study concluded that facial recognition technology significantly improves the accuracy and efficiency of crime profiling, while language processing aids in overcoming language barriers during investigations.
The increasing convenience of e-commerce and online payment systems has contributed to a rise in financial fraud incidents. This development has prompted significant research aimed at identifying effective techniques for detecting and preventing such fraud. Conventional approaches, such as rule-based systems or statistical models, face challenges due to imbalances in datasets and the constantly evolving tactics of fraudsters, which they struggle to manage. In contrast, sophisticated AI models, particularly deep learning methods, offer practical solutions to these issues. This paper compares various leading AI models for detecting financial fraud, assessing their advantages, disadvantages, and performance on standard datasets. The evaluation emphasizes critical factors such as accuracy, efficiency, and scalability, demonstrating the potential of these models to significantly impact the field of financial fraud detection. Additionally, this paper addresses the evolving dynamics of fraud and the need for models that can adapt in real time, highlighting future research directions for further advancements.
