Biblio

Currently and particularly with remote working scenarios during COVID-19, phishing attack has become one of the most significant threats faced by internet users, organizations, and service providers. In a phishing attack, the attacker tries to steal client sensitive data (such as login, passwords, and credit card details) using spoofed emails and fake websites. Cybercriminals, hacktivists, and nation-state spy agencies have now got a fertilized ground to deploy their latest innovative phishing attacks. Timely detection of phishing attacks has become most crucial than ever. Machine learning algorithms can be used to accurately detect phishing attacks before a user is harmed. This paper presents a novel ensemble model to detect phishing attacks on the website. We select three machine learning classifiers: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Decision Tree (C4.5) to use in an ensemble method with Random Forest Classifier (RFC). This ensemble method effectively detects website phishing attacks with better accuracy than existing studies. Experimental results demonstrate that the ensemble of KNN and RFC detects phishing attacks with 97.33% accuracy.

Distributed Denial of Service (DDoS) attacks have two defense perspectives firstly, to defend your network, resources and other information assets from this disastrous attack. Secondly, to prevent your network to be the part of botnet (botforce) bondage to launch attacks on other networks and resources mainly be controlled from a control center. This work focuses on the development of a botnet prevention system for Internet of Things (IoT) that uses the benefits of both Software Defined Networking (SDN) and Distributed Blockchain (DBC). We simulate and analyze that using blockchain and SDN, how can detect and mitigate botnets and prevent our devices to play into the hands of attackers.