PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT

• URL: http://arxiv.org/abs/2408.05667v3
• Date: Wed, 16 Apr 2025 23:13:25 GMT
• Title: PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT
• Authors: Sayak Saha Roy, Shirin Nilizadeh,
• Abstract summary: We introduce PhishLang, the first fully client-side anti-phishing framework built on a lightweight ensemble framework.<n> PhishLang employs a multi-modal ensemble approach, combining both the URL and Source detection models.<n>We release PhishLang as a Chromium browser extension and also open-source the framework to aid the research community.
• Score: 3.014087730099599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce PhishLang, the first fully client-side anti-phishing framework built on a lightweight ensemble framework that utilizes advanced language models to analyze the contextual features of a website's source code and URL. Unlike traditional heuristic or machine learning approaches that rely on static features and struggle to adapt to evolving threats, or deep learning models that are computationally intensive, our approach utilizes MobileBERT, a fast and memory-efficient variant of the BERT architecture, to capture nuanced features indicative of phishing attacks. To further enhance detection accuracy, PhishLang employs a multi-modal ensemble approach, combining both the URL and Source detection models. This architecture ensures robustness by allowing one model to compensate for scenarios where the other may fail, or if both models provide ambiguous inferences. As a result, PhishLang excels at detecting both regular and evasive phishing threats, including zero-day attacks, outperforming popular anti-phishing tools, while operating without relying on external blocklists and safeguarding user privacy by ensuring that browser history remains entirely local and unshared. We release PhishLang as a Chromium browser extension and also open-source the framework to aid the research community.

Related papers

• Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation [12.89058029173131]
  We propose a graph-based machine learning model for phishing URL detection. We integrate URL structure and network-level features such as IP addresses and authoritative name servers. Experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to 98.77%.
  arXiv  Detail & Related papers  (2025-01-12T19:49:00Z)
• 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)
• PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection [26.106113544525545]
  Phishing attacks are a major threat to online security, exploiting user vulnerabilities to steal sensitive information. Various methods have been developed to counteract phishing, each with varying levels of accuracy, but they also encounter notable limitations. In this study, we introduce PhishAgent, a multimodal agent that combines a wide range of tools, integrating both online and offline knowledge bases with Multimodal Large Language Models (MLLMs) This combination leads to broader brand coverage, which enhances brand recognition and recall.
  arXiv  Detail & Related papers  (2024-08-20T11:14:21Z)
• From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks [0.8050163120218178]
  Phishing attacks attempt to deceive users into stealing sensitive information. Current phishing webpage detection solutions are vulnerable to adversarial attacks. We develop a tool that generates adversarial phishing webpages by embedding diverse phishing features into legitimate webpages.
  arXiv  Detail & Related papers  (2024-07-29T18:21:34Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• PhishNet: A Phishing Website Detection Tool using XGBoost [1.777434178384403]
  PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework.
  arXiv  Detail & Related papers  (2024-06-29T21:31:13Z)
• TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models [69.37990698561299]
  TrojFM is a novel backdoor attack tailored for very large foundation models. Our approach injects backdoors by fine-tuning only a very small proportion of model parameters. We demonstrate that TrojFM can launch effective backdoor attacks against widely used large GPT-style models.
  arXiv  Detail & Related papers  (2024-05-27T03:10:57Z)
• Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection [0.32141666878560626]
  This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy compared to existing studies.
  arXiv  Detail & Related papers  (2024-01-17T03:44:27Z)
• Mitigating Bias in Machine Learning Models for Phishing Webpage Detection [0.8050163120218178]
  Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets. We propose a potential solution in the form of a tool engineered to alleviate bias in ML models.
  arXiv  Detail & Related papers  (2024-01-16T13:45:54Z)
• From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude [3.7741995290294943]
  This study explores the potential of using four popular commercially available Large Language Models to generate phishing attacks. We build a BERT-based automated detection tool that can be used for the early detection of malicious prompts. Our model is transferable across all four commercial LLMs, attaining an average accuracy of 96% for phishing website prompts and 94% for phishing email prompts.
  arXiv  Detail & Related papers  (2023-10-29T22:52:40Z)
• Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
  Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model. FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack. We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
  arXiv  Detail & Related papers  (2023-07-25T08:15:55Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification [65.33308059737506]
  Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency. Previous works have exposed privacy vulnerabilities in the FL pipeline by recovering user data from gradient updates. We introduce a new strategy that dramatically elevates existing attacks to operate on batches of arbitrarily large size.
  arXiv  Detail & Related papers  (2022-02-01T17:26:11Z)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)
• CRFL: Certifiably Robust Federated Learning against Backdoor Attacks [59.61565692464579]
  This paper provides the first general framework, Certifiably Robust Federated Learning (CRFL), to train certifiably robust FL models against backdoors. Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
  arXiv  Detail & Related papers  (2021-06-15T16:50:54Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)
• 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)

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.