Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Scalable Detection and Isolation of Phishing

This paper presents a proposal for scalable detection and isolation of phishing. The main ideas are to move the protection from end users towards the network provider and to employ the novel bad neighborhood concept, in order to detect and isolate both phishing e-mail senders and phishing web servers. In addition, we propose to develop a self-management architecture that enables ISPs to protect their users against phishing attacks, and explain how this architecture could be evaluated. This proposal is the result of half a year of research work at the University of Twente (UT), and it is aimed at a Ph.D. thesis in 2012

Phishing Detection using Base Classifier and Ensemble Technique

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats

A maximum entropy classification scheme for phishing detection using parsimonious features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM)

Develop a Hybrid Classification using an Ensemble Model for Phishing Website Detection

Solutions to threats posed by technical and social vulnerabilities must be found to secure the web interface. Social engineering attacks frequently use phishing as one of their vectors. The importance is promptly detecting phishing attacks has increased. The classifier model was constructed using publicly accessible data from trustworthy and phishing websites. A variety of methods were used to extract relevant features to build the model. Before a user experiences any harm, Machine Learning algorithms can reliably identify phishing attacks. To identify phishing attacks on the website, this study presents a novel ensemble model. In this paper, the Artificial Neural Network (ANN) and the Random Forest Classifier (RFC) are used in an ensemble method along with the Support Vector Machine (SVM). Compared to previous studies, this ensemble method more accurately and efficiently detects website phishing attacks. According to experimental findings, the proposed system detects phishing attacks 97.3% of the time