Performance Assessment of some Phishing predictive models based on Minimal Feature corpus

Phishing is currently one of the severest cybersecurity challenges facing the emerging online community. With damages running into millions of dollars in financial and brand losses, the sad tale of phishing activities continues unabated. This led to an arms race between the con artists and online security community which demand a constant investigation to win the cyberwar. In this paper, a new approach to phishing is investigated based on the concept of minimal feature set on some selected remarkable machine learning algorithms. The goal of this is to select and determine the most efficient machine learning methodology without undue high computational requirement usually occasioned by non-minimal feature corpus. Using the frequency analysis approach, a 13-dimensional feature set consisting of 85% URL-based feature category and 15% non-URL-based feature category was generated. This is because the URL-based features are observed to be more regularly exploited by phishers in most zero-day attacks. The proposed minimal feature set is then trained on a number of classifiers consisting of Random Tree, Decision Tree, Artificial Neural Network, Support Vector Machine and Naïve Bayes. Using 10 fold-cross validation, the approach was experimented and evaluated with a dataset consisting of 10000 phishing instances. The results indicate that Random Tree outperforms other classifiers with significant accuracy of 96.1% and a Receiver’s Operating Curve (ROC) value of 98.7%. Thus, the approach provides the performance metrics of various state of art machine learning approaches popular with phishing detection which can stimulate further deeper research work in the evaluation of other ML techniques with the minimal feature set approach

World-wide cloaking phishing websites detection

Most known anti-phishing tools are based in “black-list” system and http headers, but some phishing sites have been used web cloaking technique to avoid possible detection. These kinds pf phishing websites have an officially and trustful web content at ordinary times but triggered by some specific keyword on search engines. Contrapose this phenomenon, a new method based on anonymous, distributed and active probing-based for detecting cloaking fast-flux phishing websites is presented. This research works on 5 of top 10 world Search engines, which are Bing, Ask, Aol, Lycos and Search. We have two models to detect phishing website. Model A based on local dictionary, search random keywords through all search engines to detect suspicious website; Model B will determine specific URLs whether suspicious or not by our detection system

Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics

Cybercriminals resort to phishing as a simple and cost-effective medium to perpetrate cyber-attacks on today's Internet. Recent studies in phishing detection are increasingly adopting automated feature selection over traditional manually engineered features. This transition is due to the inability of existing traditional methods to extrapolate their learning to new data. To this end, in this paper, we propose WebPhish, a deep learning technique using automatic feature selection extracted from the raw URL and HTML of a web page. This approach is the first of its kind, which uses the concatenation of URL and HTML embedding feature vectors as input into a Convolutional Neural Network model to detect phishing attacks on web pages. Extensive experiments on a real-world dataset yielded an accuracy of 98 percent, outperforming other state-of-the-art techniques. Also, WebPhish is a client-side strategy that is completely language-independent and can conduct lightweight phishing detection regardless of the web page's textual language