Dincy R Arikkat | Searcharxiv

arXiv2025

Discerning Reliable Cyber Threat Indicators for Timely Cyber Threat Intelligence

In today's dynamic cybersecurity landscape, timely and accurate threat intelligence is essential for proactive defense. This study explores the potential of social media platforms as a valuable resource for extracting actionable Indicators of Compromise (IoCs). Utilizing a Convolutional Neural Network (CNN), we achieved an F1-score of 98.80% and a detection rate of 99.65%, filtering vast social media data to identify key IoCs, including IP addresses, URLs, file hashes, domain addresses, and CVE IDs. These indicators are critical for detecting potential threats and vulnerabilities, and their relevance was evaluated using metrics such as correctness, timeliness, and overlap. Our analysis shows that URLs emerged as the most frequently shared IoC, with 48.67% representing valid threats. To further investigate the role of automated accounts in disseminating IoCs, we applied several machine learning models, with XGBoost delivering the highest performance achieving a macro F1-score of 0.814 and a weighted F1-score of 0.925. These findings highlight the growing significance of social media as a reliable source of actionable threat intelligence, offering valuable insights for cybersecurity professionals to stay ahead of emerging threats.

cs.CR↗

arXiv2025

DroidTTP: Mapping Android Applications with TTP for Cyber Threat Intelligence

The widespread adoption of Android devices for sensitive operations like banking and communication has made them prime targets for cyber threats, particularly Advanced Persistent Threats (APT) and sophisticated malware attacks. Traditional malware detection methods rely on binary classification, failing to provide insights into adversarial Tactics, Techniques, and Procedures (TTPs). Understanding malware behavior is crucial for enhancing cybersecurity defenses. To address this gap, we introduce DroidTTP, a framework mapping Android malware behaviors to TTPs based on the MITRE ATT&CK framework. Our curated dataset explicitly links MITRE TTPs to Android applications. We developed an automated solution leveraging the Problem Transformation Approach (PTA) and Large Language Models (LLMs) to map applications to both Tactics and Techniques. Additionally, we employed Retrieval-Augmented Generation (RAG) with prompt engineering and LLM fine-tuning for TTP predictions. Our structured pipeline includes dataset creation, hyperparameter tuning, data augmentation, feature selection, model development, and SHAP-based model interpretability. Among LLMs, Llama achieved the highest performance in Tactic classification with a Jaccard Similarity of 0.9583 and Hamming Loss of 0.0182, and in Technique classification with a Jaccard Similarity of 0.9348 and Hamming Loss of 0.0127. However, the Label Powerset XGBoost model outperformed LLMs, achieving a Jaccard Similarity of 0.9893 for Tactic classification and 0.9753 for Technique classification, with a Hamming Loss of 0.0054 and 0.0050, respectively. While XGBoost showed superior performance, the narrow margin highlights the potential of LLM-based approaches in TTP classification.

cs.CR↗