Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors

• URL: http://arxiv.org/abs/2005.08454v3
• Date: Thu, 24 Nov 2022 09:51:12 GMT
• Title: Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors
• Authors: Bushra Sabir (University of Adelaide, CREST - The Centre for Research on Engineering Software Technologies, CSIROs Data61) and M. Ali Babar (University of Adelaide, CREST - The Centre for Research on Engineering Software Technologies), Raj Gaire (CSIROs Data61) and Alsharif Abuadbba (CSIROs DATA61)
• Abstract summary: ML-based Phishing URL (MLPU) detectors serve as the first level of defence to protect users and organisations from being victims of phishing attacks. We have proposed a methodology to investigate the reliability and robustness of 50 representative state-of-the-artU models. We analyzed their robustness and reliability using box plots and heat maps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: ML-based Phishing URL (MLPU) detectors serve as the first level of defence to protect users and organisations from being victims of phishing attacks. Lately, few studies have launched successful adversarial attacks against specific MLPU detectors raising questions about their practical reliability and usage. Nevertheless, the robustness of these systems has not been extensively investigated. Therefore, the security vulnerabilities of these systems, in general, remain primarily unknown which calls for testing the robustness of these systems. In this article, we have proposed a methodology to investigate the reliability and robustness of 50 representative state-of-the-art MLPU models. Firstly, we have proposed a cost-effective Adversarial URL generator URLBUG that created an Adversarial URL dataset. Subsequently, we reproduced 50 MLPU (traditional ML and Deep learning) systems and recorded their baseline performance. Lastly, we tested the considered MLPU systems on Adversarial Dataset and analyzed their robustness and reliability using box plots and heat maps. Our results showed that the generated adversarial URLs have valid syntax and can be registered at a median annual price of \$11.99. Out of 13\% of the already registered adversarial URLs, 63.94\% were used for malicious purposes. Moreover, the considered MLPU models Matthew Correlation Coefficient (MCC) dropped from a median 0.92 to 0.02 when tested against $Adv_\mathrm{data}$, indicating that the baseline MLPU models are unreliable in their current form. Further, our findings identified several security vulnerabilities of these systems and provided future directions for researchers to design dependable and secure MLPU systems.

Related papers

• Benchmarking Large Language Models for Zero-shot and Few-shot Phishing URL Detection [0.0]
  Deceptive URLs have reached unprecedented sophistication due to the widespread use of generative AI by cybercriminals.<n> phishing volume has escalated over 4,000% since 2022, with nearly 50% more attacks evading detection.<n>We present a benchmark of LLMs under a unified zero-shot and few-shot prompting framework.
  arXiv  Detail & Related papers  (2026-02-02T18:56:06Z)
• CLASP: Cost-Optimized LLM-based Agentic System for Phishing Detection [0.8737375836744933]
  We present CLASP, a novel system that effectively identifies phishing websites by leveraging multiple intelligent agents.<n>The system processes URLs or QR codes, employing specialized LLM-based agents that evaluate the URL structure, webpage screenshot, and HTML content.<n>CLASP surpasses leading previous solutions, achieving over 40% higher recall and a 20% improvement in F1 score for phishing detection on the collected dataset.
  arXiv  Detail & Related papers  (2025-10-21T12:38:52Z)
• ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection [43.41293570032631]
  ParaVul is a retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection.<n>We develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning.<n>We construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system.
  arXiv  Detail & Related papers  (2025-10-20T03:23:41Z)
• One Token to Fool LLM-as-a-Judge [52.45386385722788]
  Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models.<n>We uncover a critical vulnerability even in this reference-based paradigm: generative reward models are systematically susceptible to reward hacking.
  arXiv  Detail & Related papers  (2025-07-11T17:55:22Z)
• LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge [44.6358611761225]
  Large Language Models (LLMs) have demonstrated remarkable intelligence across various tasks.<n>These systems are susceptible to adversarial attacks that can manipulate evaluation outcomes.<n>Existing evaluation methods adopted by LLM-based judges are often piecemeal and lack a unified framework for comprehensive assessment.
  arXiv  Detail & Related papers  (2025-06-11T06:48:57Z)
• Phishing URL Detection using Bi-LSTM [0.0]
  This paper proposes a deep learning-based approach to classify URLs into four categories: benign, phishing, defacement, and malware. Experimental results on a dataset comprising over 650,000 URLs demonstrate the model's effectiveness, achieving 97% accuracy and significant improvements over traditional techniques.
  arXiv  Detail & Related papers  (2025-04-29T00:55:01Z)
• Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities [0.6144680854063939]
  AGC systems' reliance on communicated measurements exposes them to false data injection attacks (FDIAs) This paper proposes a machine learning (ML)-based detection framework that identifies FDIAs and determines the compromised measurements. Our results demonstrate the efficacy of the proposed method in detecting FDIAs while maintaining a low false alarm rate, with an F1-score of up to 99.98%, outperforming existing approaches.
  arXiv  Detail & Related papers  (2025-04-12T23:06:59Z)
• EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability [44.2907457629342]
  EXPLICATE is a framework that enhances phishing detection through a three-component architecture. It is on par with existing deep learning techniques but has better explainability. It addresses the critical divide between automated AI and user trust in phishing detection systems.
  arXiv  Detail & Related papers  (2025-03-22T23:37:35Z)
• Jailbreaking as a Reward Misspecification Problem [80.52431374743998]
  We propose a novel perspective that attributes this vulnerability to reward misspecification during the alignment process. We introduce a metric ReGap to quantify the extent of reward misspecification and demonstrate its effectiveness. We present ReMiss, a system for automated red teaming that generates adversarial prompts in a reward-misspecified space.
  arXiv  Detail & Related papers  (2024-06-20T15:12:27Z)
• 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)
• Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review [51.31851488650698]
  Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. adversarial distortion injected into the power signal will greatly affect the system's normal control and operation. It is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems.
  arXiv  Detail & Related papers  (2023-08-30T03:29:26Z)
• Detecting Phishing Sites Using ChatGPT [2.3999111269325266]
  We propose a novel system called ChatPhishDetector that utilizes Large Language Models (LLMs) to detect phishing sites. Our system involves leveraging a web crawler to gather information from websites, generating prompts for LLMs based on the crawled data, and then retrieving the detection results from the responses generated by the LLMs. The experimental results using GPT-4V demonstrated outstanding performance, with a precision of 98.7% and a recall of 99.6%, outperforming the detection results of other LLMs and existing systems.
  arXiv  Detail & Related papers  (2023-06-09T11:30:08Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• 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)
• Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems [0.7829352305480285]
  A growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems. This study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.
  arXiv  Detail & Related papers  (2023-03-12T14:01:00Z)
• Free Lunch for Generating Effective Outlier Supervision [46.37464572099351]
  We propose an ultra-effective method to generate near-realistic outlier supervision. Our proposed textttBayesAug significantly reduces the false positive rate over 12.50% compared with the previous schemes.
  arXiv  Detail & Related papers  (2023-01-17T01:46:45Z)
• Learned-Database Systems Security [46.898983878921484]
  We develop a framework for identifying vulnerabilities that stem from the use of machine learning (ML)<n>We show that the use of ML cause leakage of past queries in a database, enable a poisoning attack that causes exponential memory blowup and crashes it in seconds.<n>We find that adversarial ML is an universal threat against learned components in database systems.
  arXiv  Detail & Related papers  (2022-12-20T15:09:30Z)
• PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning [10.445311342905118]
  We propose a classification system using Machine Learning (ML) to accurately identify the origin of noisy memory derived (DRAM) PUF responses. We achieve up to 98% classification accuracy using a modified deep convolutional neural network (CNN) for feature extraction.
  arXiv  Detail & Related papers  (2022-07-11T08:13:08Z)
• Threat Assessment in Machine Learning based Systems [12.031113181911627]
  We conduct an empirical study of threats reported against Machine Learning-based systems. The study is based on 89 real-world ML attack scenarios from the MITRE's ATLAS database, the AI Incident Database, and the literature. Results show that convolutional neural networks were one of the most targeted models among the attack scenarios.
  arXiv  Detail & Related papers  (2022-06-30T20:19:50Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• A Critical Evaluation of Open-World Machine Learning [46.88273149649151]
  Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors. We show that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance. We show that OOD inputs with 22 unintentional corruptions or adversarial perturbations render open-world ML systems unusable with false positive rates of up to $100%$.
  arXiv  Detail & Related papers  (2020-07-08T19:40:07Z)
• Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking [83.48804199140758]
  We propose a learning-to-mis-rank formulation to perturb the ranking of the system output. We also perform a back-box attack by developing a novel multi-stage network architecture. Our method can control the number of malicious pixels by using differentiable multi-shot sampling.
  arXiv  Detail & Related papers  (2020-04-08T18:48:29Z)

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.