Next Generation of Phishing Attacks using AI powered Browsers

• URL: http://arxiv.org/abs/2406.12547v1
• Date: Tue, 18 Jun 2024 12:24:36 GMT
• Title: Next Generation of Phishing Attacks using AI powered Browsers
• Authors: Akshaya Arun, Nasr Abosata,
• Abstract summary: The model had an accuracy of 98.32%, precision of 98.62%, recall of 97.86%, and an F1-score of 98.24%. The zero-day phishing attack detection testing over a 15-day period revealed the model's capability to identify previously unseen threats. The model had successfully detected phishing URLs that evaded detection by Google safe browsing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The increase in the number of phishing demands innovative solutions to safeguard users from phishing attacks. This study explores the development and utilization of a real-time browser extension integrated with machine learning model to improve the detection of phishing websites. The results showed that the model had an accuracy of 98.32%, precision of 98.62%, recall of 97.86%, and an F1-score of 98.24%. When compared to other algorithms like Support Vector Machine, Na\"ive Bayes, Decision Tree, XGBoost, and K Nearest Neighbor, the Random Forest algorithm stood out for its effectiveness in detecting phishing attacks. The zero-day phishing attack detection testing over a 15-day period revealed the model's capability to identify previously unseen threats and thus achieving an overall accuracy rate of 99.11%. Furthermore, the model showed better performance when compared to conventional security measures like Google Safe Browsing. The model had successfully detected phishing URLs that evaded detection by Google safe browsing. This research shows how using machine learning in real-time browser extensions can defend against phishing attacks. It gives useful information about cybersecurity and helps make the internet safer for everyone.

Related papers

• PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection [0.0]
  Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. This paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy.
  arXiv  Detail & Related papers  (2024-09-29T23:15:57Z)
• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats [52.94388672185062]
  We propose an efficient defense mechanism against backdoor threats using a concept known as machine unlearning. This entails strategically creating a small set of poisoned samples to aid the model's rapid unlearning of backdoor vulnerabilities. In the backdoor unlearning process, we present a novel token-based portion unlearning training regime.
  arXiv  Detail & Related papers  (2024-09-29T02:55:38Z)
• NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques [0.0]
  "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. The performance results show that Random Forest delivers the best precision.
  arXiv  Detail & Related papers  (2024-09-01T18:59:14Z)
• Position Paper: Think Globally, React Locally -- Bringing Real-time Reference-based Website Phishing Detection on macOS [0.4962561299282114]
  The recent surge in phishing attacks keeps undermining the effectiveness of the traditional anti-phishing blacklist approaches. On-device anti-phishing solutions are gaining popularity as they offer faster phishing detection locally. We propose a phishing detection solution that uses a combination of computer vision and on-device machine learning models to analyze websites in real time.
  arXiv  Detail & Related papers  (2024-05-28T14:46:03Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Towards Web Phishing Detection Limitations and Mitigation [21.738240693843295]
  We show how phishing sites bypass Machine Learning-based detection. Experiments with 100K phishing/benign sites show promising accuracy (98.8%) We propose Anti-SubtlePhish, a more resilient model based on logistic regression.
  arXiv  Detail & Related papers  (2022-04-03T04:26:04Z)
• Adversarially robust deepfake media detection using fused convolutional neural network predictions [79.00202519223662]
  Current deepfake detection systems struggle against unseen data. We employ three different deep Convolutional Neural Network (CNN) models to classify fake and real images extracted from videos. The proposed technique outperforms state-of-the-art models with 96.5% accuracy.
  arXiv  Detail & Related papers  (2021-02-11T11:28:00Z)
• Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers [47.828297621738265]
  We show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.
  arXiv  Detail & Related papers  (2020-10-30T15:27:44Z)
• Phishing and Spear Phishing: examples in Cyber Espionage and techniques to protect against them [91.3755431537592]
  Phishing attacks have become the most used technique in the online scams, initiating more than 91% of cyberattacks, from 2012 onwards. This study reviews how Phishing and Spear Phishing attacks are carried out by the phishers, through 5 steps which magnify the outcome.
  arXiv  Detail & Related papers  (2020-05-31T18:10:09Z)
• High Accuracy Phishing Detection Based on Convolutional Neural Networks [0.0]
  We present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites.
  arXiv  Detail & Related papers  (2020-04-08T12:20:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.