Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework
Source abstract
Abstract
The potential of text analytics is revealed by Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Unifor... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (66)
Show source-provided references
1. A survey on differential privacy for unstructured data content
2. Text preprocessing for text mining in organizational research: Review and recommendations10.1177/1094428120971683 ↗
3. On the role of text preprocessing in BERT embedding-based DNNs for classifying informal texts
4. Text feature extraction based on stacked variational autoencoder10.1016/j.micpro.2020.103063 ↗
5. Understanding social engagements: A comparative analysis of user and text features in Twitter10.1007/s13278-022-00872-1 ↗
6. Hot topic detection based on a refined TF-IDF algorithm10.1109/ACCESS.2019.2893980 ↗
7. Analisis sentimen review film menggunakan TF-IDF dan support vector machine
8. An adaptive meta-heuristic for music plagiarism detection based on text similarity and clustering10.1007/s10618-022-00835-2 ↗
9. Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey10.1007/s11042-018-6894-4 ↗
10. Latent DIRICHLET allocation (LDA) based information modelling on BLOCKCHAIN technology: A review of trends and research patterns used in integration10.1007/s11042-022-13500-z ↗
11. Latent Dirichlet allocation
12. The impact of news media on Bitcoin prices: Modelling data driven discourses in the crypto-economy with natural language processing10.1098/rsos.220276 ↗
13. Efficient detection of phising hyperlinks using machine learning
14. Detection of Phising websites using machine learning approaches
15. Layered obfuscation: A taxonomy of software obfuscation techniques for layered security10.1186/s42400-020-00049-3 ↗
16. ObfSec: Measuring the security of obfuscations from a testing perspective10.1016/j.eswa.2022.118298 ↗
17. Cyber threat intelligence-based malicious URL detection model using ensemble learning10.3390/s22093373 ↗
18. Robust detection of malicious URLs with self-paced wide & deep learning
19. Developing a framework for detecting phishing URLs using machine learning10.46338/ijetae1121_08 ↗
20. Modeling hybrid feature-based phishing websites detection using machine learning techniques
21. A Smart model for web phishing detection based on new proposed feature selection technique10.21608/mjeer.2021.146286 ↗
22. A lightweight and proactive rule-based incremental construction approach to detect phishing scam10.1007/s10799-021-00351-7 ↗
23. Detecting Malicious URLs using machine learning techniques: Review and research directions10.1109/ACCESS.2022.3222307 ↗
24. Heuristic-based strategy for phishing prediction: A survey of URL-based approach10.1016/j.cose.2019.101613 ↗
25. Malicious domain names detection algorithm based on lexical analysis and feature quantification10.1109/ACCESS.2019.2940554 ↗
26. Efficient suspicious URL filtering based on reputation
27. A predictive model for phishing detection
28. Malicious URL protection based on attackers’ habitual behavioral analysis10.1016/j.cose.2018.01.013 ↗
29. Machine learning techniques for detecting phishing URL attacks
30. A malicious URLs detection system using optimization and machine learning classifiers
31. JsSandbox: A framework for analyzing the behavior of malicious JavaScript code using internal function hooking
32. Identifying email threats using predictive analysis
33. Towards the detection of malicious URL and domain names using machine learning
34. Automating URL blacklist generation with similarity search approach10.1587/transinf.2015ICP0027 ↗
35. Look before you leap: Detecting phishing web pages by exploiting raw URL and HTML characteristics10.1016/j.eswa.2023.121183 ↗
36. Enhancing detection of malicious urls using boosting and lexical features10.32604/iasc.2022.020229 ↗
37. Detecting malicious URL using machine learning: A survey10.22214/ijraset.2020.5447 ↗
38. QsecR: Secure QR code scanner according to a novel malicious URL detection framework10.1109/ACCESS.2023.3291811 ↗
39. A systematic approach for malware URL recognition
40. Detection of phishing websites using machine learning approach
41. Outlier detection among influencer blogs based on off-site web analytics data
42. SenseTrust: A sentiment based trust model in social network10.3390/jtaer16060114 ↗
43. Malicious URL detection based on machine learning
44. Detecting malicious URLs using lexical analysis
45. Detecting Malicious URLs using lexical analysis and network activities
46. A. Joshi, L. Lloyd, P. Westin, and S. Seethapathy, “Using lexical features for malicious URL Detection—A machine learning approach,” 2019. doi: 10.48550/arXiv.1910.06277.
47. An assessment of lexical, network, and content-based features for detecting malicious URLs using machine learning and deep learning models10.1155/2022/3241216 ↗
48. A convolution-based system for malicious URLS detection
49. Malicious and Benign webpages dataset10.1016/j.dib.2020.106304 ↗
50. Phishing URL detection using machine learning methods10.1016/j.advengsoft.2022.103288 ↗
51. Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods10.1016/j.is.2020.101494 ↗
52. Leverage temporal convolutional network for the representation learning of URLs
53. URL2Vec: URL modeling with character embeddings for fast and accurate phishing website detection
54. An enhanced deep learning-based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders10.1049/ise2.12106 ↗
55. Adaptive ensemble clustering with boosting BLS-based autoencoder10.1109/TKDE.2023.3271120 ↗
56. Stacked one-class broad learning system for intrusion detection in industry 4.010.1109/TII.2022.3157727 ↗
57. An adversarial attack analysis on malicious advertisement URL detection framework10.1109/TNSM.2022.3225217 ↗
58. An automated framework for real-time phishing URL detection
59. Detecting phishing websites using machine learning technique10.1371/journal.pone.0258361 ↗
60. A heterogeneous machine learning ensemble framework for malicious webpage detection10.3390/app122312070 ↗
61. A three-step framework for detecting malicious URLs
62. A novel approach for malicious URL detection based on the joint model
63. Is this URL Safe: Detection of malicious URLs using global vector for word representation
64. Count vectorizer model based web application vulnerability detection using artificial intelligence approach10.1080/09720529.2022.2133243 ↗
65. A Naive Bayes approach for URL classification with supervised feature selection and rejection framework10.1111/coin.12158 ↗
66. GloVe: Global vectors for word representation