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Abstract The Internet has become a vital part of our daily lives, serving as a hub for global connectivity and a facilitator for seamless communication and information exchange. However, the rise of malicious domains presents a serious challenge, undermining the reliability of the Internet and posing risks to user safety. These malicious activities exploit the Domain Name System (DNS) to deceive users, leading to harmful activities such as spreading drive-by-download malware, operating botnets, creating phishing sites, and sending spam. In response to this growing threat, the application of Machine Learning (ML) techniques has proven to be highly effective. These methods excel in quickly and accurately detecting, classifying, and analyzing such threats. This paper explores the latest developments in using transfer learning for the classification of malicious domains, with a focus on image visualization as a key methodological approach. Our proposed solution has achieved a remarkable testing accuracy rate of 98.67%, demonstrating its effectiveness in detecting and classifying malicious domains.
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Thriving on chaos: Proactive detection of command and control domains in internet of things‐scale botnets using DRIFT
Abstract In this paper, we introduce DRIFT, a system for detecting command and control (C2) domain names in Internet of Things–scale botnets. Using an intrinsic feature of malicious domain name queries prior to their registration (perhaps due to clock drift), we devise a difference‐based lightweight feature for malicious C2 domain name detection. Using NXDomain query and response of a popular malware, we establish the effectiveness of our detector with 99% accuracy and as early as more than 48 hours before they are registered. Our technique serves as a tool of detection where other techniques relying on entropy or domain generating algorithms reversing are impractical.
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- Award ID(s):
- 1809000
- PAR ID:
- 10073645
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Transactions on Emerging Telecommunications Technologies
- Volume:
- 30
- Issue:
- 4
- ISSN:
- 2161-3915
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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