arXiv:2606.04733v1 Announce Type: new Abstract: Understanding activities of Internet scanners is challenging; it often requires identifying relationships between sources, a task for which semantic annotations are scarce. This work investigates whether semantically meaningful pairwise relationships between sequences of network flow records can be estimated by contrastive learning, without pretraining and without annotations. To this end, we propose a transformer model that embeds minimally preprocessed sequences of network flow records and train it using contrastive learning. With the similarit
The proliferation of network telescope data and advancements in AI, specifically contrastive learning and transformer models, enable new approaches to cyber threat intelligence.
This research offers a novel, annotation-free method for understanding scanner activities, which is critical for cybersecurity and the integrity of global networks.
The ability to automatically identify relationships between Internet scanner sources without human-labeled data significantly enhances passive threat detection capabilities.
- · Cybersecurity firms
- · Network security operators
- · AI/ML researchers in security
- · Malicious network scanners
- · Attackers relying on stealth
More efficient and autonomous identification of internet scanning patterns.
Improved defensive measures against distributed denial-of-service (DDoS) attacks and reconnaissance activities.
Potential for AI-driven, self-healing network defenses that proactively counter emerging threats.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG