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2020-09-28
Killer, Christian, Rodrigues, Bruno, Stiller, Burkhard.  2019.  Security Management and Visualization in a Blockchain-based Collaborative Defense. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :108–111.
A cooperative network defense is one approach to fend off large-scale Distributed Denial-of-Service (DDoS) attacks. In this regard, the Blockchain Signaling System (BloSS) is a multi-domain, blockchain-based, cooperative DDoS defense system, where each Autonomous System (AS) is taking part in the defense alliance. Each AS can exchange attack information about ongoing attacks via the Ethereum blockchain. However, the currently operational implementation of BloSS is not interactive or visualized, but the DDoS mitigation is automated. In realworld defense systems, a human cybersecurity analyst decides whether a DDoS threat should be mitigated or not. Thus, this work presents the design of a security management dashboard for BloSS, designed for interactive use by cyber security analysts.
2019-01-16
Jia, Z., Cui, X., Liu, Q., Wang, X., Liu, C..  2018.  Micro-Honeypot: Using Browser Fingerprinting to Track Attackers. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :197–204.
Web attacks have proliferated across the whole Internet in recent years. To protect websites, security vendors and researchers collect attack information using web honeypots. However, web attackers can hide themselves by using stepping stones (e.g., VPN, encrypted proxy) or anonymous networks (e.g., Tor network). Conventional web honeypots lack an effective way to gather information about an attacker's identity, which raises a big obstacle for cybercrime traceability and forensics. Traditional forensics methods are based on traffic analysis; it requires that defenders gain access to the entire network. It is not suitable for honeypots. In this paper, we present the design, implementation, and deployment of the Micro-Honeypot, which aims to use the browser fingerprinting technique to track a web attacker. Traditional honeypot lure attackers and records attacker's activity. Micro-Honeypot is deployed in a honeypot. It will run and gather identity information when an attacker visits the honeypot. Our preliminary results show that Micro-Honeypot could collect more information and track attackers although they might have used proxies or anonymous networks to hide themselves.