Biblio

Global cybersecurity threats have grown as a result of the evolving digital transformation. Cybercriminals have more opportunities as a result of digitization. Initially, cyberthreats take the form of phishing in order to gain confidential user credentials.As cyber-attacks get more sophisticated and sophisticated, the cybersecurity industry is faced with the problem of utilising cutting-edge technology and techniques to combat the ever-present hostile threats. Hackers use phishing to persuade customers to grant them access to a company’s digital assets and networks. As technology progressed, phishing attempts became more sophisticated, necessitating the development of tools to detect phishing.Machine learning is unsupervised one of the most powerful weapons in the fight against terrorist threats. The features used for phishing detection, as well as the approaches employed with machine learning, are discussed in this study.In this light, the study’s major goal is to propose a unique, robust ensemble machine learning model architecture that gives the highest prediction accuracy with the lowest error rate, while also recommending a few alternative robust machine learning models.Finally, the Random forest algorithm attained a maximum accuracy of 96.454 percent. But by implementing a hybrid model including the 3 classifiers- Decision Trees,Random forest, Gradient boosting classifiers, the accuracy increases to 98.4 percent.

Network on chip (NoC) is the communication infrastructure used in multicores which has been subject to a surfeit of security threats like degrading the system performance, changing the system functionality or leaking sensitive information. Because of the globalization of the advanced semiconductor industry, many third-party venders take part in the hardware design of system. As a result, a malicious circuit, called Hardware Trojans (HT) can be added anywhere into the NoC design and thus making the hardware untrusted. In this paper, a detailed study on the taxonomy of hardware trojans, its detection and prevention mechanisms are presented. Two case studies on HT-assisted Denial of service attacks and its analysis in the performance of network on Chip architecture is also presented in this paper.