Summary With the widespread use of encrypted data transport, network traffic encryption is becoming a standard nowadays. This presents a challenge for traffic measurement, especially for analysis and anomaly detection methods, which are dependent on the type of network traffic. In this paper, we survey existing approaches for classification and analysis of encrypted traffic. First, we describe the most widespread encryption protocols used throughout the Internet. We show that the initiation of an encrypted connection and the protocol structure give away much information for encrypted traffic classification and analysis. Then, we survey payload and feature‐based classification methods for encrypted traffic and categorize them using an established taxonomy. The advantage of some of described classification methods is the ability to recognize the encrypted application protocol in addition to the encryption protocol. Finally, we make a comprehensive comparison of the surveyed feature‐based classification methods and present their weaknesses and strengths. Copyright © 2015 John Wiley & Sons, Ltd.

The North Atlantic Treaty Organization (NATO) Research Task Group IST-152 developed a concept and a reference architecture for intelligent software agents performing active, largely autonomous cyber-defense actions on military assets. The group released a detailed report, briefly reviewed in this article, where such an agent is referred to as an Autonomous Intelligent Cyber-defense Agent (AICA). In a conflict with a technically sophisticated adversary, NATO military networks will operate in a heavily contested battlefield. Enemy malware will likely infiltrate and attack friendly networks and systems. Today’s reliance on human cyber defenders will be untenable on the future battlefield. Instead, artificially intelligent agents, such as AICAs, will be necessary to defeat the enemy malware in an environment of potentially disrupted communications where human intervention may not be possible. The IST-152 group identified specific capabilities of AICA. For example, AICA will have to be capable of autonomous planning and execution of complex multi-step activities for defeating or degrading sophisticated adversary malware, with the anticipation and minimization of resulting side effects. It will have to be capable of adversarial reasoning to battle against a thinking, adaptive malware. Crucially, AICA will have to keep itself and its actions as undetectable as possible, and will have to use deceptions and camouflage. The report identifies the key functions and components and their interactions for a potential reference architecture of such an agent, as well as a tentative roadmap toward the capabilities of AICA.

SUMMARY Network flow monitoring is currently a common practice in mid‐ and large‐size networks. Methods of flow‐based anomaly detection are subject to ongoing extensive research, because detection methods based on deep packets have reached their limits. However, there is a lack of comprehensive studies mapping the state of the art in this area. For this reason, we have conducted a thorough survey of flow‐based anomaly detection methods published on academic conferences and used by the industry. We have analyzed these methods using the perspective of similarity which is inherent to any anomaly detection method. Based on this analysis, we have proposed a new taxonomy of network anomalies and a similarity‐oriented classification of flow‐based detection methods. We have also identified four issues requiring further research: the lack of flow‐based evaluation datasets, infeasible benchmarking of proposed methods, excessive false positive rate and limited coverage of certain anomaly classes. Copyright © 2014 John Wiley & Sons, Ltd.