Impact of Artificial Intelligence on Cybersecurity: A Case of Internet of Things
Adaptive Chimp Optimization Algorithm Based Secure Workload Control Strategy in Real Time Database Management Systems
Nerve GABA Neurotransmitter Health Level Detection System
Cyber Vaccinator for Image Tamper Resilient and Recovery using Invertible Neural Network
Phishing Attack Detection using Gradient Boosting
Nerve GABA Neurotransmitter Health Level Detection System
Impact of Artificial Intelligence on Cybersecurity: A Case of Internet of Things
Cyber Vaccinator for Image Tamper Resilient and Recovery using Invertible Neural Network
Enhancing Cyber Security in Health Care Industry by using ISO 27001 Accreditation
An Extensive Overview on Dark Web
The increasing adoption of electronic emails as a means of communication, both at the commercial, government, and individual levels, serves as an impetus for attackers to compromise communication. Consequently, numerous machine learning techniques have been developed for identifying unwanted emails, commonly known as spam. Despite the significant progress reported in existing literature, most studies do not integrate the detection of both textual and image-based spam. In this paper, two deep learning techniques that detect both textual and image-based spam were evaluated. First, the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) is studied, training them on various textimage features to explore their effectiveness on an improved dataset. Subsequently, in an effort to outsmart current spam detection techniques, a bi-modal architecture capable of detecting textual spam, image spam, and mixed spam is designed. The experimental results in conjunction with existing transfer learning for effective spam detection is provided.
Credit card fraud is a significant problem for financial institutions and cardholders and can result in significant financial losses and damage to reputation. Detecting and preventing fraud in credit card transactions is critical to minimizing losses and maintaining customer trust. The main objective of this project is to build a model (a web-based system) that can effectively identify fraudulent transactions. This involves collecting and processing large datasets of credit card transactions, including both legitimate and fraudulent transactions, and using machine learning algorithms like decision trees and random forests to train a model to recognize patterns and anomalies. In addition, the system will have strong user authentication protocols that must be in place to prevent unauthorized access to the system.
The Oversight Crypto-Virtual Storage application is designed as a virtual storage solution aimed at curtailing crimes involving data, such as data theft and tampering. The application enables users to privately store files by creating an encrypted box. Data storage is available for a predetermined amount of time. Once this period ends, the user's Box of files will prompt the user to decide whether to extend the storage duration. If the user chooses not to renew their service, the box becomes accessible to everyone on the platform.
Healthcare Industry plays a pivotal role in every one's life and with rapid advancements in cyber-attack vectors, threat actors and their strategies it has in-need created a necessity and a challenge to the numerous organizations and to the Governments as well, to stand guard and secure the institutes and the data stored with them. To design a secure healthcare system involves several considerations to protect sensitive patient data and ensure the confidentiality, integrity, and availability of information. Lot of work has been published on cyber security along with importance of protecting the Personally Identifiable Information (PII) and patient health records stored in hospitals, and also comparisons were made between paid or licensed tools and open source; however, implementation of the tools in real time was not in place where the financial limitations are a real concern and security is a need. This paper has given insight into important parameters such as risk assessment and security policies etc.
The dark web has become an infamous cybercrime platform, enticing criminals with the promise of privacy and encryption. This review paper will delve into the dark web's history and evolution, along with the characteristics that make it an attractive arena for cybercriminals. It will explore various types of crimes occurring on the dark web, such as cyberattacks, data breaches, and online fraud, examining their societal impacts. Additionally, this paper will scrutinize law enforcement activities, cybersecurity technologies, and public education programs designed to thwart crimes on the dark web. It will propose suggestions for future research and policy actions aimed at mitigating the adverse effects of cybercrime on the dark web. This study aims to foster a deeper understanding of the dark web and the societal risks posed by cybercrime, influencing the development of effective strategies to prevent such occurrences.
