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The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques have been developed to identify the authentic web pages that users visit and warn them when the webpage is phishing. In this paper, we have developed an extension for Chrome the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by the Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision., Comment: 21 pages, 13 figures, 5 listings, 1 table

Parallel to our physical activities our virtual presence also leaves behind our unique digital fingerprints, while navigating on the Internet. These digital fingerprints have the potential to unveil users' activities encompassing browsing history, utilized applications, and even devices employed during these engagements. Many Internet users tend to use web browsers that provide the highest privacy protection and anonymization such as Tor. The success of such privacy protection depends on the Tor feature to anonymize end-user IP addresses and other metadata that constructs the website fingerprint. In this paper, we show that using the newest machine learning algorithms an attacker can deanonymize Tor traffic by applying such techniques. In our experimental framework, we establish a baseline and comparative reference point using a publicly available dataset from Universidad Del Cauca, Colombia. We capture network packets across 11 days, while users navigate specific web pages, recording data in .pcapng format through the Wireshark network capture tool. Excluding extraneous packets, we employ various machine learning algorithms in our analysis. The results show that the Gradient Boosting Machine algorithm delivers the best outcomes in binary classification, achieving an accuracy of 0.8363. In the realm of multi-class classification, the Random Forest algorithm attains an accuracy of 0.6297., Comment: 14 pages, 5 tables, 2 figures, 4 equations

The rapid evolution of the Internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralization of corporations' power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this is still a topic of interest. This paper proposes a novel conceptual approach for implementing decentralized collaborative intrusion detection networks (CIDN) through a proof-of-concept. The study employs an analytical and comparative methodology, examining the synergy between cutting-edge Web3 technologies and information security. The proposed model incorporates blockchain concepts, cyber non-fungible token (cyberNFT) rewards, machine learning algorithms, and publish/subscribe architectures. Finally, the paper discusses the strengths and limitations of the proposed system, offering insights into the potential of decentralized cybersecurity models., Comment: 9 pages, 6 figures, 1 table, 1 algorithm, 1 listing, journal article

Biometric authentication methods, representing the "something you are" scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learning techniques demand a serious systematic reevaluation of biometric authentication. This paper analyzes and presents the Fast Gradient Sign Method (FGSM) attack using face recognition for biometric authentication. Machine Learning techniques have been used to train and test the model, which can classify and identify different people's faces and which will be used as a target for carrying out the attack. Furthermore, the case study will analyze the implementation of the FGSM and the level of performance reduction that the model will have by applying this method in attacking. The test results were performed with the change of parameters both in terms of training and attacking the model, thus showing the efficiency of applying the FGSM., Comment: This is a journal article that contains 8 pages, 4 figueres, and 3 tables. Furthermore, this is an Accepted Manuscript of an article published by Taylor & Francis in Applied Artificial Intelligence on September 16, 2021. Available online at https://www.tandfonline.com/doi/full/10.1080/08839514.2021.1978149