Building an AI/ML Based Classification Framework for Dark Web Text Data

Murty, Ch A S; Rana, Harmesh; Verma, Rachit; Pathak, Roshan; Rughani, Parag H.

doi:10.1007/978-981-19-0604-6_9

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…Communications and information about financial transactions, travel patterns, surfing activities, and publicly available information such as social media activity enable the identification of terrorists by distinguishing what characterizes the activity of a particular subgroup on these media [38]. These methods include analyzing relationships between entities or using more complex tools for an image or sound recognition [39,40]. Through advanced data analytics and ML techniques, AI can help identify potential terrorists and prevent or mitigate the risks associated with terrorist activities.…”

Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database (GTD) can influence the accuracy of the model's classification of terrorist attacks, where each part of the data can provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomy has one or more tags attached to it, referred as "related tags." We applied machine learning classifiers to classify terrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts and learns contextual features from text attributes to acquire more information from text data. The extracted contextual features are combined with the "key features" of the dataset and used to perform the final classification. The study explored different experimental setups with various classifiers to evaluate the model's performance. The experimental results show that the proposed framework outperforms the latest techniques for classifying terrorist attacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

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Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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“…The main idea is to collect enough information to evaluate any method on the dataset. We used Python3, the Wappalyzer tool 9 , Selenium and WGET requests. This crawler takes the list generated early and renders the websites on the Firefox web browser.…”

Section: ) Url Filter and Crawlermentioning

confidence: 99%

“…As a textcolorblueresult, researchers have focused on important topics related to cybersecurity. Recent relevant works include spam identification or classification [2], [3], bots detection to early response and Distributed Denial of Service (DDoS) attacks [4], [5], algorithms to classify suspicious content on the darknet [6]- [9], and even image analysis as a forensic tool to detect criminal activity on videos [10], [11].…”

Section: Introductionmentioning

confidence: 99%

PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

Castano¹,

Fidalgo²,

Aláiz-Rodríguez³

et al. 2023

IEEE Access

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Recent studies have shown that phishers are using phishing kits to deploy phishing attacks faster, easier and more massive. Detecting phishing kits in deployed websites might help to detect phishing campaigns earlier. To the best of our knowledge, there are no datasets providing a set of phishing kits that are used in websites that were attacked by phishing. In this work, we propose PhiKitA, a novel dataset that contains phishing kits and also phishing websites generated using these kits. We have applied MD5 hashes, fingerprints, and graph representation DOM algorithms to obtain baseline results in PhiKitA in three experiments: familiarity analysis of phishing kit samples, phishing website detection and identifying the source of a phishing website. In the familiarity analysis, we find evidence of different types of phishing kits and a small phishing campaign. In the binary classification problem for phishing detection, the graph representation algorithm achieved an accuracy of 92.50%, showing that the phishing kit data contain useful information to classify phishing. Finally, the MD5 hash representation obtained a 39.54% F1 score, which means that this algorithm does not extract enough information to distinguish phishing websites and their phishing kit sources properly.

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Building an AI/ML Based Classification Framework for Dark Web Text Data

Cited by 2 publications

References 28 publications

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

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