arXiv:2606.28917v1 Announce Type: new Abstract: Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have read access to all AD data through LDAP, making it a common initial tool for reconnaissance when a threat actor first compromises an identity. To capture threat actors early in the reconnaissance phase, we developed two machine learning frameworks to detect LDAP reconnaissance: an ML classifier to predict malicious LDAP queries and an ML-based data-mining method to extract malicious query signatur
The increasing sophistication of threat actors and the pervasive use of LDAP for identity management necessitate advanced detection methods for reconnaissance activities, pushing ML into cybersecurity defenses.
This development enhances early detection capabilities against threat actors, allowing organizations to neutralize threats before they escalate, thereby reducing potential damage and improving cybersecurity posture.
The ability to proactively identify and block malicious LDAP queries using ML will make initial reconnaissance phases significantly harder for attackers, shifting the advantage towards defenders.
- · Cybersecurity companies
- · Organizations with robust ML security infrastructure
- · Active Directory users
- · Threat actors
- · Legacy security systems
- · Organizations with limited security budgets
Reduced success rate of initial reconnaissance phases for cyber attackers.
Increased investment in ML-driven security solutions and potential shift in red-teaming tactics to evade such defenses.
Enhanced overall digital trust and reduced economic impact from successful cyberattacks, fostering broader adoption of advanced ML in security.
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Read at arXiv cs.LG