Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models

• URL: http://arxiv.org/abs/2411.06860v1
• Date: Mon, 11 Nov 2024 10:49:24 GMT
• Title: Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models
• Abstract summary: This study investigates the use of machine learning to identify phishing URLs. It emphasizes the crucial role of feature selection and model interpretability for improved performance. The study evaluated various algorithms, including CatBoost, XGBoost, and Explainable Boosting Machine.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Phishing attacks remain a persistent threat to online security, demanding robust detection methods. This study investigates the use of machine learning to identify phishing URLs, emphasizing the crucial role of feature selection and model interpretability for improved performance. Employing Recursive Feature Elimination, the research pinpointed key features like "length_url," "time_domain_activation" and "Page_rank" as strong indicators of phishing attempts. The study evaluated various algorithms, including CatBoost, XGBoost, and Explainable Boosting Machine, assessing their robustness and scalability. XGBoost emerged as highly efficient in terms of runtime, making it well-suited for large datasets. CatBoost, on the other hand, demonstrated resilience by maintaining high accuracy even with reduced features. To enhance transparency and trustworthiness, Explainable AI techniques, such as SHAP, were employed to provide insights into feature importance. The study's findings highlight that effective feature selection and model interpretability can significantly bolster phishing detection systems, paving the way for more efficient and adaptable defenses against evolving cyber threats

Related papers

• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers [0.0]
  This paper presents an empirical study of adversarial robustness and explainability drift across two cybersecurity domains.<n>We introduce a quantitative metric, the Robustness Index (RI), defined as the area under the accuracy perturbation curve.<n>Experiments on the Phishing Websites and NB15 datasets show consistent robustness trends, with adversarial training improving RI by up to 9 percent while maintaining clean-data accuracy.
  arXiv  Detail & Related papers  (2026-02-06T05:30:37Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback [74.82886755416949]
  We identify key reasoning skills essential for effective web agents.<n>We reconstruct the agent's reasoning algorithms into chain-of-thought rationales.<n>Our approach yields significant improvements across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T14:03:37Z)
• A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection [0.0]
  Existing phishing detection methods struggle to simultaneously achieve high accuracy and explainability. We propose a novel phishing URL detection system based on a first-order Takagi-Sugeno-Kang fuzzy inference model optimized through gradient-based techniques.
  arXiv  Detail & Related papers  (2025-04-25T18:31:05Z)
• Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation [0.0]
  We introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method for detecting Distributed Denial of Service (DDoS) attacks.<n>By training a GAN exclusively on attack traffic, GANFS effectively ranks feature importance without relying on full supervision.<n>Results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.
  arXiv  Detail & Related papers  (2025-04-21T20:27:33Z)
• 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)
• Enhancing web traffic attacks identification through ensemble methods and feature selection [1.3652530361013693]
  This study aims to enhance the identification of web traffic attacks by leveraging machine learning techniques. A methodology was proposed to extract relevant features from HTTP traces using the CSIC2010 v2 dataset. Ensemble methods, such as Random Forest and Extreme Gradient Boosting, were employed and compared against baseline classifiers.
  arXiv  Detail & Related papers  (2024-12-21T22:13:30Z)
• Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems [3.3150909292716477]
  Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security. statistical and machine learning (ML)-based DoS classification and detection approaches utilize a broad range of feature selection mechanisms to select a feature subset from networking traffic datasets. In this paper, we investigate the importance of feature selection in improving ML-based detection of DoS attacks.
  arXiv  Detail & Related papers  (2024-11-04T19:51:08Z)
• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [58.60915132222421]
  We introduce an approach that is both general and parameter-efficient for face forgery detection. We design a forgery-style mixture formulation that augments the diversity of forgery source domains. We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis [1.102674168371806]
  Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. We propose a 1D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data.
  arXiv  Detail & Related papers  (2024-04-27T17:13:49Z)
• 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)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Clustering Effect of (Linearized) Adversarial Robust Models [60.25668525218051]
  We propose a novel understanding of adversarial robustness and apply it on more tasks including domain adaption and robustness boosting. Experimental evaluations demonstrate the rationality and superiority of our proposed clustering strategy.
  arXiv  Detail & Related papers  (2021-11-25T05:51:03Z)
• Utilising Flow Aggregation to Classify Benign Imitating Attacks [0.0]
  In many applications, the choice of features is more important than the choice of model. A range of studies have attempted to discriminate between benign traffic and well-known cyber-attacks. We introduce new features based on a higher level of abstraction of network traffic.
  arXiv  Detail & Related papers  (2021-03-06T23:09:12Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

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.