Publication

Abstract : Spamming and Phishing attacks are the most common security challenges we face in today's cyber world. The existing methods for the Spam and Phishing detection are based on blacklisting and heuristics technique. These methods require human intervention to update if any new Spam and Phishing activity occurs. Moreover, these are completely inefficient in detecting new Spam and Phishing activities. These techniques can detect malicious activity only after the attack has occurred. Machine learning has the capability to detect new Spam and Phishing activities. This requires extensive domain knowledge for feature learning and feature representation. Deep learning is a method of machine learning which has the capability to extract optimal feature representation from various samples of benign, Spam and Phishing activities by itself. To leverage, this work uses various deep learning architectures for both Spam and Phishing detection with electronic mail (Email) and uniform resource locator (URL) data sources. Because in recent years both Email and URL resources are the most commonly used by the attackers to spread malware. Various datasets are used for conducting experiments with deep learning architectures. For comparative study, classical machine learning algorithms are used. These datasets are collected using public and private data sources. All experiments are run till 1,000 epochs with varied learning rate 0.01–0.5. For comparative study various classical machine learning classifiers are used with domain level feature extraction. For deep learning architectures and classical machine learning algorithms to convert text data into numeric representation various natural language processing text representation methods are used. As far as anyone is concerned, this is the first attempt, a framework that can examine and connect the occasions of Spam and Phishing activities from Email and URL sources at scale to give cyber threat situational awareness. The created framework is exceptionally versatile and fit for distinguishing the malicious activities in close constant. In addition, the framework can be effectively reached out to deal with vast volume of other cyber security events by including extra resources. These qualities have made the proposed framework emerge from some other arrangement of comparative kind.