A NOVEL DEEP BELIEF-BASED MODEL FOR THE INTRUSION DETECTION OF NETWORK TRAFFIC

ABSTRACT The fast expansion of networked systems, combined with the pervasive reliance on the internet, has raised worries about network security, needing new defense methods. Intrusion Detection Systems (IDS) use a variety of ways to distinguish between legitimate and malicious network traffic, including rule-based, signature-based, anomaly detection, and machine learning approaches. While signature-based IDS excel

A Long Short-Term Memory-based Phishing Detection on URL

ABSTRACT The rapid evolution of cyberspace and the advent of new technologies have greatly impacted our daily lives. However, these advancements also present vulnerabilities that malicious actors exploit. Phishing, a common cyber threat, involves creating deceptive traps that can lead to severe consequences like financial loss and blackmail. Traditional detection methods, such as blacklisting, whitelisting,

A Critical Review on Finger Print Recognition Techniques

ABSTRACT Fingerprint recognition techniques have become a prominent area of study and application in the field of biometrics and security. These techniques aim to accurately identify individuals based on their unique fingerprint patterns. The primary focus of this review is a comprehensive assessment of the major categories of fingerprint recognition methods, including minutiae-based, deep learning-based,

IMPROVED ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHMS

ABSTRACT The recent popularity of Elliptic Curve Digital Signature Algorithm (ECDSA) has been attributed to its capability of providing good security using shorter key size. Although ECDSA is secure based on the hardness of the Elliptic Curve Discrete Logarithm Problem (ECDLP) and it is being used to ensure users’ authentication, transactions non-repudiation, and data integrity,

USE OF TRANSFER LEARNING AND MACHINE LEARNING FOR DISEASE PRONE DETECTION IN WATERMELON FRUIT

ABSTRACT Given that consumption has increased as a result of medical advice, watermelon crop disease detection is essential for maintaining its health. The Jupyter IDE, libraries, Kaggle dataset, preprocessing, constructing CNN model, training, and testing steps of a deep learning-based technique were suggested. The pre-selected box setting method used by the CNN model was enhanced

TOWARDS A BLOCKCHAIN-BASED PARTIAL COMPUTATION OFFLOADING FOR THE METAVERSE IN IN-NETWORK COMPUTING

ABSTRACT The Metaverse is anticipated to offer an immersive experience to many simultaneous users. Therefore, it is critical to optimally allocate computing resources to meet the massive users’ demand. Meanwhile, the In-network computing (COIN) paradigm has emerged to reduce delay and meet quality of experience (QoE) by using unused network resources for performing some tasks.