The encryption of network traffic complicates legitimate network monitoring, traffic analysis, and network forensics. In this paper, we present real-time lightweight identification of HTTPS clients based on network monitoring and SSL/TLS fingerprinting. Our experiment shows that it is possible to estimate the User-Agent of a client in HTTPS communication via the analysis of the SSL/TLS handshake. The fingerprints of SSL/TLS handshakes, including a list of supported cipher suites, differ among clients and correlate to User-Agent values from a HTTP header. We built up a dictionary of SSL/TLS cipher suite lists and HTTP User-Agents and assigned the User-Agents to the observed SSL/TLS connections to identify communicating clients. The dictionary was used to classify live HTTPS network traffic. We were able to retrieve client types from 95.4 % of HTTPS network traffic. Further, we discussed host-based and network-based methods of dictionary retrieval and estimated the quality of the data.

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Abstract.In this paper we analyze HTTP protocol parsers that provide a web traffic visibility to IP flow. Despite extensive work, flow meters generally fall short of performance goals due to extracting application layer data. Constructing effective protocol parser for in-depth analysis is a challenging and error-prone affair. We designed and evaluated several HTTP protocol parsers representing current state-of-the-art approaches used in today's flow meters. We show the packet rates achieved by respective parsers, including the throughput decrease (performance implications of application parser) which is of the utmost importance for high-speed deployments. We believe that these results provide researchers and network operators with important insight into application visibility and IP flow.

Abstract-The growing share of encrypted network traffic complicates network traffic analysis and network forensics. In this paper, we present real-time lightweight identification of HTTPS clients based on network monitoring and SSL/TLS fingerprinting. Our experiment shows that it is possible to estimate the UserAgent of a client in HTTPS communication via the analysis of the SSL/TLS handshake. The fingerprints of SSL/TLS handshakes, including a list of supported cipher suites, differ among clients and correlate to User-Agent values from a HTTP header. We built up a dictionary of SSL/TLS cipher suite lists and HTTP UserAgents and assigned the User-Agents to the observed SSL/TLS connections to identify communicating clients. We discuss hostbased and network-based methods of dictionary retrieval and estimate the quality of the data. The usability of the proposed method is demonstrated on two case studies of network forensics.

Abstract-Cyber attacks have become ubiquitous and in order to face current threats it is important to understand them. Studying attacks in a real environment however, is not viable and therefore it is necessary to find other methods how to examine the nature of attacks. Gaining detailed knowledge about them facilitates designing of new detection methods as well as understanding their impact. In this paper we present a testbed framework to simulate attacks that enables to study a wide range of security scenarios. The framework provides a notion of realworld arrangements, yet it retains full control over all the activities performed within the simulated infrastructures. Utilizing the sandbox environment, it is possible to simulate various security attacks and evaluate their impacts on real infrastructures. The design of the framework benefits from IaaS clouds. Therefore its deployment does not require dedicated facilities and the testbed can be deployed over miscellaneous contemporary clouds. The viability of the testbed has been verified by a simulation of particular DDoS attack.

Abstract-Performing research on live network traffic requires the traffic to be well documented and described. The results of such research are heavily dependent on the particular network. This paper presents a study of network characteristics, which can be used to describe the behaviour of a network. We propose a number of characteristics that can be collected from the networks and evaluate them on five different networks of Masaryk University. The proposed characteristics cover IP, transport and application layers of the network traffic. Moreover, they reflect strong day-night and weekday patterns that are present in most of the networks. Variation in the characteristics between the networks indicates that they can be used for the description and differentiation of the networks. Furthermore, a weak correlation between the chosen characteristics implies their independence and contribution to network description.

In today's complex computer networks, we are constantly facing a risk of data loss, system compromise, or intellectual property theft. The complexity of the networks hinders their effective defense. A Network-wide Cyber Situational Awareness (NwCSA) has been introduced to assist a network security administrator with network security. The concept, however, faces several challenges that hinder an efficient application of the NwCSA in a real-world environment. The challenges include the overload of raw data, low speed of reaction, and a lack of context and unified view on a network. In this paper, we present a novel framework that faces above mentioned challenges. The framework leverages a distributed data stream processing system and methods for real-time big data processing. The framework is evaluated with respect to stated requirements on systems for NwCSA. Moreover, we present a prototype framework implementation and provide lessons learned from its real-world deployment.

Abstract. Many vulnerabilities are operating system specific. Information about the OS of all hosts in a network represents a valuable asset for network administrators. While OS detection in small networks is an easy task, expanding the same process on a large scale becomes a challenge. The weak performance, high speed traffic and large amount of hosts for OS detection are issues to overcome. In this paper we propose a flow based framework for large scale OS detection. Furthermore, we describe the framework implementation into a flow probe, provide performance comparison and share remarks on deployment in a real world network.