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The master thesis focuses on the selection of a suitable algorithm for the classification of selected network traffic services and its implementation. The theoretical part describes the available classification approaches together with commonly used algorithms and selected network services. The practical part focuses on the preparation and preprocessing of the dataset, selection and optimization of the classification algorithm and verifying the classification capabilities of the algorithm in the various scenarios of the dataset.

Le sujet de la classification de trafic r´eseau est d’une grande importance pourla planification de r´eseau efficace, la gestion de trafic `a base de r`egles, la gestionde priorit´e d’applications et le contrˆole de s´ecurit´e. Bien qu’il ait re¸cu une atten-tion consid´erable dans le milieu de la recherche, ce th`eme laisse encore de nom-breuses questions en suspens comme, par exemple, les m´ethodes de classificationdes flux de trafics chiffr´es. Cette th`ese est compos´ee de quatre parties. La premi`erepr´esente quelques aspects th´eoriques li´es `a la classification de trafic et `a la d´etec-tion d’intrusion. Les trois parties suivantes traitent des probl`emes sp´ecifiques declassification et proposent des solutions pr´ecises.Dans la deuxi`eme partie, nous proposons une m´ethode d’´echantillonnage pr´ecisepour d´etecter les attaques de type ”SYN flooding”et ”portscan”. Le syst`eme examineles segments TCP pour trouver au moins un des multiples segments ACK provenantdu serveur. La m´ethode est simple et ´evolutive, car elle permet d’obtenir unebonne d´etection avec un taux de faux positif proche de z´ero, mˆeme pour des tauxd’´echantillonnage tr`es faibles. Nos simulations bas´ees sur des traces montrent quel’efficacit´e du syst`eme propos´e repose uniquement sur le taux d’´echantillonnage,ind´ependamment de la m´ethode d’´echantillonnage.Dans la troisi`eme partie, nous consid´erons le probl`eme de la d´etection et de laclassification du trafic de Skype et de ses flux de services tels que les appels vocaux,SkypeOut, les vid´eo-conf´erences, les messages instantan´es ou le t´el´echargement defichiers. Nous proposons une m´ethode de classification pour le trafic Skype chiffr´ebas´e sur le protocole d’identification statistique (SPID) qui analyse les valeurs statis-tiques de certains attributs du trafic r´eseau. Nous avons ´evalu´e notre m´ethode surun ensemble de donn´ees montrant d’excellentes performances en termes de pr´eci-sion et de rappel. La derni`ere partie d´efinit un cadre fond´e sur deux m´ethodescompl´ementaires pour la classification des flux applicatifs chiffr´es avec TLS/SSL.La premi`ere mod´elise des ´etats de session TLS/SSL par une chaˆıne de Markov ho-mog`ene d’ordre 1. Les param`etres du mod`ele de Markov pour chaque applicationconsid´er´ee diff`erent beaucoup, ce qui est le fondement de la discrimination entreles applications. La seconde m´ethode de classification estime l’´ecart d’horodatagedu message Server Hello du protocole TLS/SSL et l’instant d’arriv´ee du paquet.Elle am´eliore la pr´ecision de classification des applications et permet l’identificationviiefficace des flux Skype. Nous combinons les m´ethodes en utilisant une ClassificationNaive Bay´esienne (NBC). Nous validons la proposition avec des exp´erimentationssur trois s´eries de donn´ees r´ecentes. Nous appliquons nos m´ethodes `a la classificationde sept applications populaires utilisant TLS/SSL pour la s´ecurit´e. Les r´esultatsmontrent une tr`es bonne performance. / The subject of traffic classification is of great importance for effective networkplanning, policy-based traffic management, application prioritization, and securitycontrol. Although it has received substantial attention in the research communitythere are still many unresolved issues, for example how to classify encrypted trafficflows. This thesis is composed of four parts. The first part presents some theoreticalaspects related to traffic classification and intrusion detection, while in the followingthree parts we tackle specific classification problems and propose accurate solutions.In the second part, we propose an accurate sampling scheme for detecting SYNflooding attacks as well as TCP portscan activity. The scheme examines TCPsegments to find at least one of multiple ACK segments coming from the server.The method is simple and scalable, because it achieves a good detection with aFalse Positive Rate close to zero even for very low sampling rates. Our trace-basedsimulations show that the effectiveness of the proposed scheme only relies on thesampling rate regardless of the sampling method.In the third part, we consider the problem of detecting Skype traffic and classi-fying Skype service flows such as voice calls, skypeOut, video conferences, chat, fileupload and download. We propose a classification method for Skype encrypted traf-fic based on the Statistical Protocol IDentification (SPID) that analyzes statisticalvalues of some traffic attributes. We have evaluated our method on a representativedataset to show excellent performance in terms of Precision and Recall.The last part defines a framework based on two complementary methods for clas-sifying application flows encrypted with TLS/SSL. The first one models TLS/SSLsession states as a first-order homogeneous Markov chain. The parameters of theMarkov models for each considered application differ a lot, which is the basis foraccurate discrimination between applications. The second classifier considers thedeviation between the timestamp in the TLS/SSL Server Hello message and thepacket arrival time. It improves the accuracy of application classification and al-lows efficient identification of Skype flows. We combine the methods using a NaiveBayes Classifier (NBC).We validate the framework with experiments on three recentdatasets—we apply our methods to the classification of seven popular applicationsthat use TLS/SSL for security. The results show a very good performance.

This thesis focuses on description of the current state of research in the detection of network attacks and subsequently on the improvement of detection capabilities of specific attacks by establishing a formal definition of network metrics. These metrics approximate the progress of network connection and create a signature, based on behavioral characteristics of the analyzed connection. The aim of this work is not the prevention of ongoing attacks, or the response to these attacks. The emphasis is on the analysis of connections to maximize information obtained and definition of the basis of detection system that can minimize the size of data collected from the network, leaving the most important information for subsequent analysis. The main goal of this work is to create the concept of the detection system by using defined metrics for reduction of the network traffic to signatures with an emphasis on the behavioral aspects of the communication. Another goal is to increase the autonomy of the detection system by developing an expert knowledge of honeypot system, with the condition of independence to the technological aspects of analyzed data (e.g. encryption, protocols used, technology and environment). Defining the concept of honeypot system's expert knowledge in the role of the teacher of classification algorithms creates autonomy of the~system for the detection of unknown attacks. This concept also provides the possibility of independent learning (with no human intervention) based on the knowledge collected from attacks on these systems. The thesis describes the process of creating laboratory environment and experiments with the defined network connection signature using collected data and downloaded test database. The results are compared with the state of the art of the network detection systems and the benefits of the proposed approximation methods are highlighted.

This thesis is focused on network behavior analysis (NBA) designed to detect network attacks. The goal of the thesis is to increase detection accuracy of obfuscated network attacks. Methods and techniques used to detect network attacks and network traffic classification were presented first. Intrusion detection systems (IDS) in terms of their functionality and possible attacks on them are described next. This work also describes principles of selected attacks against IDS. Further, obfuscation methods which can be used to overcome NBA are suggested. The tool for automatic exploitation, attack obfuscation and collection of this network communication was designed and implemented. This tool was used for execution of network attacks. A dataset for experiments was obtained from collected network communications. Finally, achieved results emphasized requirement of training NBA models by obfuscated malicious network traffic.