TY - GEN
T1 - BustedURL
T2 - 35th Australasian Database Conference, ADC 2024
AU - Sundarraj, Jayaprakash Nariyambut
AU - Zhang, Yan
AU - Itharaju, Santosh Kapil Dev
AU - Saleh, Ahmed
AU - Ahmed, Saad
AU - Azam, Sami
PY - 2024/12/13
Y1 - 2024/12/13
N2 - Phishing is a major security issue in cyberspace, and time-stamping the detection of malicious URLs is crucial to safeguarding users and systems. Current approaches like URLNet and URLTran are reasonable, but the methods generally cannot adapt to quick changes and cannot be easily scaled as a standalone agent. In this paper, we present BustedURL, a sustainable framework designed to address these limitations. Leveraging a distributed, collaborative multi-agent architecture, BustedURL incorporates advanced methodologies, including transformers, ensemble learning, stacking, big-data aggregation, and sophisticated learning pipelines. These innovations enhance the framework’s adaptability across diverse environments. We empirically evaluate the proposed architecture using real-time datasets from OpenPhish and various other sources, demonstrating that BustedURL outperforms current state-of-the-art solutions across various performance metrics in dynamic phishing contexts, owing to its distributed, scalable, and adaptive characteristics. Specifically, the scalability experiments demonstrated an approximately 50-fold improvement compared to the competitive baseline models.
AB - Phishing is a major security issue in cyberspace, and time-stamping the detection of malicious URLs is crucial to safeguarding users and systems. Current approaches like URLNet and URLTran are reasonable, but the methods generally cannot adapt to quick changes and cannot be easily scaled as a standalone agent. In this paper, we present BustedURL, a sustainable framework designed to address these limitations. Leveraging a distributed, collaborative multi-agent architecture, BustedURL incorporates advanced methodologies, including transformers, ensemble learning, stacking, big-data aggregation, and sophisticated learning pipelines. These innovations enhance the framework’s adaptability across diverse environments. We empirically evaluate the proposed architecture using real-time datasets from OpenPhish and various other sources, demonstrating that BustedURL outperforms current state-of-the-art solutions across various performance metrics in dynamic phishing contexts, owing to its distributed, scalable, and adaptive characteristics. Specifically, the scalability experiments demonstrated an approximately 50-fold improvement compared to the competitive baseline models.
KW - Malicious URL detection
KW - multi-agent system
KW - phishing detection
KW - real-time detection
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85213354224&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-1242-0_34
DO - 10.1007/978-981-96-1242-0_34
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85213354224
SN - 9789819612413
VL - 15449
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 463
EP - 476
BT - Databases Theory and Applications - 35th Australasian Database Conference, ADC 2024, Proceedings
A2 - Chen, Tong
A2 - Cao, Yang
A2 - Nguyen, Quoc Viet Hung
A2 - Nguyen, Thanh Tam
PB - Springer Singapore
CY - Singapore
Y2 - 16 December 2024 through 18 December 2024
ER -
