A Survey of Methods for Encrypted Traffic Classification and Analysis

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 a lot of 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.Šifrování síťového provozu se v dnešní době stalo standardem. To přináší vysoké nároky na monitorování síťového provozu, zejména pak na analýzu provozu a detekci anomálií, které jsou závislé na znalosti typu síťového provozu. V tomto článku přinášíme přehled existujících způsobů klasifikace a analýzy šifrovaného provozu. Nejprve popisujeme nejrozšířenější šifrovací protokoly, a ukazujeme, jakým způsobem lze získat informace pro analýzu a klasifikaci šifrovaného provozu. Následně se zabýváme klasifikačními metodami založenými na obsahu paketů a vlastnostech síťového provozu. Tyto metody klasifikujeme pomocí zavedené taxonomie. Výhodou některých popsaných klasifikačních metod je schopnost rozeznat nejen šifrovací protokol, ale také šifrovaný aplikační protokol. Na závěr porovnáváme silné a slabé stránky všech popsaných klasifikačních metod

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy