Prevention in Healthcare: An Explainable AI Approach

Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements

The Rise of Crypto Malware: Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats

Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks

A Survey on Secure Distributed Deduplication Systems for Improved Reliability

Abstract Data de-duplication is one of the most important technique used for removing the identical copies of repeating data and it is used in the clou

EFFICIENT REBROADCASTING USING TRUSTWORTHINESS OF NODE WITH NEIGHBOUR KNOWLEDGE IN MANET

Mobile Ad hoc network is an infrastructure less communication network with limited resources. To maintain virtual infrastructure for communication broadcasting mechanisms is used. Due to lack of energy efficiency in Mobile Ad hoc network, there is a need to develop an efficient broadcasting model which enhances energy efficiency. Also nodes with malicious behaviour cause an internal threat that disobeys the standard and degrades the performance of routing protocols. This paper introduced an enhanced rebroadcasting algorithm, where rebroadcasting decision for next hop is immediate or delayed on the basis of trust value and energy level of particular node. This approach helps to decrease number of rebroadcast, energy consumption and also enhances security

A SURVEY ON MULTIPATH ROUTING STRATEGY IN MULTI-HOPWIRELESS SENSOR NETWORK

There are number of routing protocols proposed for the data transmission in WSN. Initially single path routing schemes with number of variations are proposed. Still there were some drawbacks in single path routing. Single path routing was unable to provide the reliability and high throughput. Also security level was not considered while routing. Recently, to remove the drawbacks of the single path routing new routing technique is proposed called as multipath routing

Efficient Rebroadcasting Using Trustworthiness Of Node With Neighbour Knowledge In Manet

Mobile Ad hoc network is an infrastructure less communication network with limited resources. To maintain virtual infrastructure for communication broadcasting mechanisms is used. Due to lack of energy efficiency in Mobile Ad hoc network, there is a need to develop an efficient broadcasting model which enhances energy efficiency. Also nodes with Malicious behaviour cause an internal threat that disobeys the standard and degrades the performance of routing protocols. This paper introduced an enhanced rebroadcasting algorithm, where rebroadcasting decision for next hop is immediate or delayed on the basis of trust value and energy level of particular node. This approach helps to decrease number of rebroadcast, energy consumption and also enhances security