Cyberattacker utgör ett stort hot för dagens företag och organisationer, med engenomsnittlig kostnad för ett intrång på ca 3,86 miljoner USD. För att minimera kostnaden av ett intrång är det viktigt att detektera intrånget i ett så tidigt stadium som möjligt. Avancerande långvariga hot (APT) är en sofistikerad cyberattack som har en lång närvaro i offrets nätverk. Efter attackerarens första intrång kommer fokuset av attacken skifta till att få kontroll över så många enheter som möjligt på nätverket. Detta steg kallas för lateral rörelse och är ett av de mest kritiska stegen i en APT. Syftet med denna uppsats är att undersöka hur och hur väl lateral rörelse kan upptäckas med hjälp av en maskininlärningsmetod. I undersökningen jämförs och utvärderas fem maskininlärningsalgoritmer med upprepad korsvalidering följt av statistisk testning för att bestämma vilken av algoritmerna som är bäst. Undersökningen konkluderar även vilka attributer i det undersökta datasetet som är väsentliga för att detektera laterala rörelser. Datasetet kommer från en Active Directory domänkontrollant där datasetets attributer är skapade av korrelerade loggar med hjälp av datornamn, IP-adress och användarnamn. Datasetet består av en syntetisk, samt, en verklig del vilket skapar ett semi-syntetiskt dataset som innehåller ett multiklass klassifierings problem. Experimentet konkluderar att all fem algoritmer klassificerar rätt med en pricksäkerhet (accuracy) på 0.998. Algoritmen RF presterar med den högsta f-measure (0.88) samt recall (0.858), SVM är bäst gällande precision (0.972) och DT har denlägsta inlärningstiden (1237ms). Baserat på resultaten indikerar undersökningenatt algoritmerna RF, SVM och DT presterar bäst i olika scenarier. Till exempel kan SVM användas om en låg mängd falsk positiva larm är viktigt. Om en balanserad prestation av de olika prestanda mätningarna är viktigast ska RF användas. Undersökningen konkluderar även att en stor mängd utav de undersökta attributerna av datasetet kan bortses i framtida experiment, då det inte påverkade prestandan på någon av algoritmerna. / Cyber attacks raise a high threat for companies and organisations worldwide. With the cost of a data breach reaching $3.86million on average, the demand is high fora rapid solution to detect cyber attacks as early as possible. Advanced persistent threats (APT) are sophisticated cyber attacks which have long persistence inside the network. During an APT, the attacker will spread its foothold over the network. This stage, which is one of the most critical steps in an APT, is called lateral movement. The purpose of the thesis is to investigate lateral movement detection with a machine learning approach. Five machine learning algorithms are compared using repeated cross-validation followed statistical testing to determine the best performing algorithm and feature importance. Features used for learning the classifiers are extracted from Active Directory log entries that relate to each other, with a similar workstation, IP, or account name. These features are the basis of a semi-synthetic dataset, which consists of a multiclass classification problem. The experiment concludes that all five algorithms perform with an accuracy of 0.998. RF displays the highest f1-score (0.88) and recall (0.858), SVM performs the best with the performance metric precision (0.972), and DT has the lowest computational cost (1237ms). Based on these results, the thesis concludes that the algorithms RF, SVM, and DT perform best in different scenarios. For instance, SVM should be used if a low amount of false positives is favoured. If the general and balanced performance of multiple metrics is preferred, then RF will perform best. The results also conclude that a significant amount of the examined features can be disregarded in future experiments, as they do not impact the performance of either classifier.

Infrastructure as a Service (IaaS) is a cloud service provisioning model which largely focuses on data centre provisioning of computing and storage facilities. The networking aspects of IaaS beyond the data centre are a limiting factor preventing communication services that are sensitive to network characteristics from adopting this approach. Cloud networking is a new technology which integrates network provisioning with the existing cloud service provisioning models thereby completing the cloud computing picture by addressing the networking aspects. In cloud networking, shared network resources are virtualized, and provisioned to customers and end-users on-demand in an elastic fashion. This technology allows various kinds of optimization, e.g., reducing latency and network load. Further, this allows service providers to provision network performance guarantees as a part of their service offering. However, this new approach introduces new security challenges. Many of these security challenges are addressed in the CloNe security architecture. This thesis presents a set of potential techniques for securing different resource in a cloud network environment which are not addressed in the existing CloNe security architecture. The thesis begins with a holistic view of the Cloud networking, as described in the Scalable and Adaptive Internet Solutions (SAIL) project, along with its proposed architecture and security goals. This is followed by an overview of the problems that need to be solved and some of the different methods that can be applied to solve parts of the overall problem, specifically a comprehensive, tightly integrated, and multi-level security architecture, a key management algorithm to support the access control mechanism, and an intrusion detection mechanism. For each method or set of methods, the respective state of the art is presented. Additionally, experiments to understand the performance of these mechanisms are evaluated on a simple cloud network test bed. The proposed key management scheme uses a hierarchical key management approach that provides fast and secure key update when member join and member leave operations are carried out. Experiments show that the proposed key management scheme enhances the security and increases the availability and integrity. A newly proposed genetic algorithm based feature selection technique has been employed for effective feature selection. Fuzzy SVM has been used on the data set for effective classification. Experiments have shown that the proposed genetic based feature selection algorithm reduces the number of features and hence decreases the classification time, while improving detection accuracy of the fuzzy SVM classifier by minimizing the conflicting rules that may confuse the classifier. The main advantages of this intrusion detection system are the reduction in false positives and increased security. / Infrastructure as a Service (IaaS) är en Cloudtjänstmodell som huvudsakligen är inriktat på att tillhandahålla ett datacenter för behandling och lagring av data. Nätverksaspekterna av en cloudbaserad infrastruktur som en tjänst utanför datacentret utgör en begränsande faktor som förhindrar känsliga kommunikationstjänster från att anamma denna teknik. Cloudnätverk är en ny teknik som integrerar nätverkstillgång med befintliga cloudtjänstmodeller och därmed fullbordar föreställningen av cloud data genom att ta itu med nätverkaspekten.  I cloudnätverk virtualiseras delade nätverksresurser, de avsätts till kunder och slutanvändare vid efterfrågan på ett flexibelt sätt. Denna teknik tillåter olika typer av möjligheter, t.ex. att minska latens och belastningen på nätet. Vidare ger detta tjänsteleverantörer ett sätt att tillhandahålla garantier för nätverksprestandan som en del av deras tjänsteutbud. Men denna nya strategi introducerar nya säkerhetsutmaningar, exempelvis VM migration genom offentligt nätverk. Många av dessa säkerhetsutmaningar behandlas i CloNe’s Security Architecture. Denna rapport presenterar en rad av potentiella tekniker för att säkra olika resurser i en cloudbaserad nätverksmiljö som inte behandlas i den redan existerande CloNe Security Architecture. Rapporten inleds med en helhetssyn på cloudbaserad nätverk som beskrivs i Scalable and Adaptive Internet Solutions (SAIL)-projektet, tillsammans med dess föreslagna arkitektur och säkerhetsmål. Detta följs av en översikt över de problem som måste lösas och några av de olika metoder som kan tillämpas för att lösa delar av det övergripande problemet. Speciellt behandlas en omfattande och tätt integrerad multi-säkerhetsarkitektur, en nyckelhanteringsalgoritm som stödjer mekanismens åtkomstkontroll och en mekanism för intrångsdetektering. För varje metod eller för varje uppsättning av metoder, presenteras ståndpunkten för respektive teknik. Dessutom har experimenten för att förstå prestandan av dessa mekanismer utvärderats på testbädd av ett enkelt cloudnätverk. Den föreslagna nyckelhantering system använder en hierarkisk nyckelhantering strategi som ger snabb och säker viktig uppdatering när medlemmar ansluta sig till och medlemmarna lämnar utförs. Försöksresultat visar att den föreslagna nyckelhantering system ökar säkerheten och ökar tillgänglighet och integritet. En nyligen föreslagna genetisk algoritm baserad funktion valet teknik har använts för effektiv funktion val. Fuzzy SVM har använts på de uppgifter som för effektiv klassificering. Försök har visat att den föreslagna genetiska baserad funktion selekteringsalgoritmen minskar antalet funktioner och därmed minskar klassificering tiden, och samtidigt förbättra upptäckt noggrannhet fuzzy SVM klassificeraren genom att minimera de motstående regler som kan förvirra klassificeraren. De främsta fördelarna med detta intrångsdetekteringssystem är den minskning av falska positiva och ökad säkerhet.

Yes / The broadcast nature of wireless local area networks has made them prone to several types of wireless injection attacks, such as Man-in-the-Middle (MitM) at the physical layer, deauthentication, and rogue access point attacks. The implementation of novel intrusion detection systems (IDSs) is fundamental to provide stronger protection against these wireless injection attacks. Since most attacks manifest themselves through different metrics, current IDSs should leverage a cross-layer approach to help toward improving the detection accuracy. The data fusion technique based on the Dempster–Shafer (D-S) theory has been proven to be an efficient technique to implement the cross-layer metric approach. However, the dynamic generation of the basic probability assignment (BPA) values used by D-S is still an open research problem. In this paper, we propose a novel unsupervised methodology to dynamically generate the BPA values, based on both the Gaussian and exponential probability density functions, the categorical probability mass function, and the local reachability density. Then, D-S is used to fuse the BPA values to classify whether the Wi-Fi frame is normal (i.e., non-malicious) or malicious. The proposed methodology provides 100% true positive rate (TPR) and 4.23% false positive rate (FPR) for the MitM attack and 100% TPR and 2.44% FPR for the deauthentication attack, which confirm the efficiency of the dynamic BPA generation methodology. / Gulf Science, Innovation and Knowledge Economy Programme of the U.K. Government under UK-Gulf Institutional Link Grant IL 279339985 and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/R006385/1.

No description available.

Utförda hacker-attacker på moderna fordon belyser ett behov av snabb detektering av hot inom denna miljö, särskilt när det förekommer en trend inom denna industri där moderna fordon idag kan klassas som IoT-enheter. Det förekommer kända fall av attacker där en angripare förmår stoppa fordon i drift, eller ta bromsar ur funktion, och detta har påvisats ske fjärrstyrt. Denna studie undersöker detektion av utförda attacker, på en riktig bil, genom studie av CAN bus meddelanden. De två modellerna CUSUM, från området Change Point Detection, och Random Forests, från området maskininlärning, tillämpas på riktig datamängd, för att sedan jämföras på simulerad data sinsemellan. En ny hypotesdefinition introduceras vilket möjliggör att evalueringsmetoden Conditional expected delay kan nyttjas för fallet Random Forests, där resultat förmås jämföras med evalueringsresultat från CUSUM. Conditional expected delay har inte tidigare studerats för metod av maskininlärning. De båda metoderna evalueras också genom ROC-kurva. Sammantaget förmås de båda metoderna jämföras sinsemellan, med varandras etablerade evalueringsmetoder. Denna studie påvisar metod och hypotes för att brygga de två områdena change point detection och maskininlärning, för att evaluera de två enligt gemensamt motiverade parametervärden. / There are known hacker attacks which have been conducted on modern vehicles. These attacks illustrates a need for early threat detection in this environment. Development of security systems in this environment is of special interest due to the increasing interconnection of vehicles and their newfound classification as IoT devices. Known attacks, that have even been carried out remotely on modern vehicles, include attacks which allow a perpetrator to stop vehicles, or to disable brake mechanisms. This study examines the detection of attacks carried out on a real vehicle, by studying CAN bus messages. The two methods CUSUM, from the field of Change Point Detection, and Random Forests, from the field of Machine Learning, are both applied to real data, and then later comparably evaluated on simulated data. A new hypothesis defintion is introduced which allows for the evaluation method Conditional expected delay to be used in the case of Random Forests, where results may be compared to evaluation results from CUSUM. Conditional expected delay has not been studied in the machinelarning case before. Both methods are also evaluated by method of ROC curve. The combined hypothesis definition for the two separate fields, allow for a comparison between the two models, in regard to each other's established evaluation methods. This study present a method and hypothesis to bridge the two separate fields of study, change point detection, and machinelearning, to achieve a comparable evaluation between the two.

Nearly all present-day commercial intrusion detection systems are based on a hierarchical architecture. In such an architecture, the root node is responsible for detecting intrusions and for issuing responses. However, an intrusion detection system (IDS) based on a hierarchical architecture has many single points of failure. For example, by disabling the root node, the intrusion-detection function of the IDS will also be disabled. To solve this problem, an IDS inspired by the human immune system is proposed. The proposed IDS has no single component that is responsible for detecting intrusions. Instead, the intrusion-detection function is divided and placed within mobile agents. Mobile agents act similarly to white blood cells of the human immune system and travel from host to host in the network to detect intrusions. The IDS is fault-tolerant because it can continue to detect intrusions even when most of its components have been disabled. / Computer Science (School of Computing) / M. Sc. (Computer Science)

O protocolo International Electrotechnical Commission (IEC)-61850 tornou possível integrar os equipamentos das subestações de energia elétrica, através de uma rede de comunicação de dados Ethernet de alta velocidade. A utilização deste protocolo tem como objetivo principal a interligação dos Intelligent Electronic Devices (IEDs) para a automatização dos processos no sistema elétrico. As contribuições deste protocolo para a integração do controle e supervisão do sistema elétrico são diversas, porém, o fato de utilizar uma rede de comunicação de dados Ethernet integrada expõe o sistema elétrico à ataques cibernéticos. A norma IEC-62351 estabelece uma série de recomendações para prover segurança à rede de comunicação do sistema elétrico, dentre elas, o gerenciamento da rede de comunicação, a análise dos campos da mensagem Generic Object Oriented Substation Event (GOOSE) e a utilização de sistemas de detecção de intrusão. O presente trabalho descreve o desenvolvimento de um Intrusion Detection System (IDS) que atende os requisitos de segurança propostos pelo protocolo IEC-62351, para a identificação de ataques à comunicação realizada por mensagens GOOSE do protocolo IEC-61850, e entre equipamentos do sistema elétrico. Para o desenvolvimento desta aplicação, foram identificados os campos que compõem as mensagens GOOSE, de forma a reconhecer os valores esperados em diferentes situações de operação do sistema elétrico. Determinaram-se padrões de comportamento a serem utilizados para discernir mensagens falsas na rede de comunicação. Instalou-se e configurou-se um sistema operacional de tempo real embarcado na plataforma de desenvolvimento Zynq Board (ZYBO), juntamente com o controlador Open-Mul, para gerenciar a rede de comunicação da subestação, através do protocolo OpenFlow, realizando otimizações para o tráfego multicast. Foi desenvolvido um sistema de detecção e bloqueio de mensagens GOOSE falsas que utiliza o protocolo OpenFlow para controle da rede de comunicação do Sistema Elétrico. Desenvolveu-se ainda um sistema inteligente, utilizando-se uma Rede Neural Artificial (RNA) Nonlinear Autoregressive Model with Exogenous Input (NARX), para predição do tráfego realizado por mensagens GOOSE e detecção de ataques Distributed Deny of Service (DDOS). Os resultados obtidos demonstraram que o protocolo OpenFlow pode ser uma ferramenta interessante para controle da rede, porém, os fabricantes necessitam amadurecer sua implementação nos switches, para que sejam utilizados em produção nas redes de comunicação das subestações. O sistema de predição do tráfego gerado por mensagens GOOSE apresentou benefícios interessantes para a segurança da rede de comunicação, demonstrando potencial para compor um sistema de detecção de ataques DDOS realizado por mensagens GOOSE, na rede de comunicação das subestações de energia elétrica. / The IEC-61850 made it possible to integrate equipments of electric power system substations to a high-speed Ethernet data communication network. Its main goal is the interconnection of IEDs for the automation of processes in an electrical system. The contributions of this protocol for the integration of the control and supervision of the electrical system are diverse, although an Ethernet network exposes the electrical system for cyber attacks. The IEC-62351 states a series of recommendations to provide security to the communication network of the electrical system, such as the communication network management, the analysis of GOOSE messages and the use of intrusion detection systems. This study describes the development of an IDS that meets the security requirements proposed by the IEC-62351 standard to identify attacks on communication between GOOSE messages exchanged by electrical equipment using IEC-61850. For the development of this application, fields of the GOOSE messages were identified, in order to recognize the expected values in different power system operating conditions. Behaviour patterns were determined to detect false messages on the communication network. A real-time embedded operating system on ZYBO was installed and configured, as well as the OpenMul controller to manage the communication network of the substation through the OpenFlow protocol, performing optimizations for multicast traffic. A detection system and block tamper GOOSE messages, using the OpenFlow protocol for control of the electrical system communication network, were developed. In addition, an intelligent system using an Artificial Neural Network (ANN) Nonlinear Autoregressive Model with Exogenous Input (NARX) for predicting of the GOOSE messages traffic and the detection of Distributed Deny of Service attack (DDOS) were also developed. The results obtained show that the OpenFlow protocol may be a valuable tool for network control, however, manufacturers should maturely carry on with its implementation in the switches, so that it be used in substation communication networks. The traffic prediction system of the GOOSE messages presented interesting benefits for the security of the communication network, demonstrating potential to built a DDOS attack detection system performed by GOOSE messages on the communication network of electric power substations.

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model's accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold.

O protocolo International Electrotechnical Commission (IEC)-61850 tornou possível integrar os equipamentos das subestações de energia elétrica, através de uma rede de comunicação de dados Ethernet de alta velocidade. A utilização deste protocolo tem como objetivo principal a interligação dos Intelligent Electronic Devices (IEDs) para a automatização dos processos no sistema elétrico. As contribuições deste protocolo para a integração do controle e supervisão do sistema elétrico são diversas, porém, o fato de utilizar uma rede de comunicação de dados Ethernet integrada expõe o sistema elétrico à ataques cibernéticos. A norma IEC-62351 estabelece uma série de recomendações para prover segurança à rede de comunicação do sistema elétrico, dentre elas, o gerenciamento da rede de comunicação, a análise dos campos da mensagem Generic Object Oriented Substation Event (GOOSE) e a utilização de sistemas de detecção de intrusão. O presente trabalho descreve o desenvolvimento de um Intrusion Detection System (IDS) que atende os requisitos de segurança propostos pelo protocolo IEC-62351, para a identificação de ataques à comunicação realizada por mensagens GOOSE do protocolo IEC-61850, e entre equipamentos do sistema elétrico. Para o desenvolvimento desta aplicação, foram identificados os campos que compõem as mensagens GOOSE, de forma a reconhecer os valores esperados em diferentes situações de operação do sistema elétrico. Determinaram-se padrões de comportamento a serem utilizados para discernir mensagens falsas na rede de comunicação. Instalou-se e configurou-se um sistema operacional de tempo real embarcado na plataforma de desenvolvimento Zynq Board (ZYBO), juntamente com o controlador Open-Mul, para gerenciar a rede de comunicação da subestação, através do protocolo OpenFlow, realizando otimizações para o tráfego multicast. Foi desenvolvido um sistema de detecção e bloqueio de mensagens GOOSE falsas que utiliza o protocolo OpenFlow para controle da rede de comunicação do Sistema Elétrico. Desenvolveu-se ainda um sistema inteligente, utilizando-se uma Rede Neural Artificial (RNA) Nonlinear Autoregressive Model with Exogenous Input (NARX), para predição do tráfego realizado por mensagens GOOSE e detecção de ataques Distributed Deny of Service (DDOS). Os resultados obtidos demonstraram que o protocolo OpenFlow pode ser uma ferramenta interessante para controle da rede, porém, os fabricantes necessitam amadurecer sua implementação nos switches, para que sejam utilizados em produção nas redes de comunicação das subestações. O sistema de predição do tráfego gerado por mensagens GOOSE apresentou benefícios interessantes para a segurança da rede de comunicação, demonstrando potencial para compor um sistema de detecção de ataques DDOS realizado por mensagens GOOSE, na rede de comunicação das subestações de energia elétrica. / The IEC-61850 made it possible to integrate equipments of electric power system substations to a high-speed Ethernet data communication network. Its main goal is the interconnection of IEDs for the automation of processes in an electrical system. The contributions of this protocol for the integration of the control and supervision of the electrical system are diverse, although an Ethernet network exposes the electrical system for cyber attacks. The IEC-62351 states a series of recommendations to provide security to the communication network of the electrical system, such as the communication network management, the analysis of GOOSE messages and the use of intrusion detection systems. This study describes the development of an IDS that meets the security requirements proposed by the IEC-62351 standard to identify attacks on communication between GOOSE messages exchanged by electrical equipment using IEC-61850. For the development of this application, fields of the GOOSE messages were identified, in order to recognize the expected values in different power system operating conditions. Behaviour patterns were determined to detect false messages on the communication network. A real-time embedded operating system on ZYBO was installed and configured, as well as the OpenMul controller to manage the communication network of the substation through the OpenFlow protocol, performing optimizations for multicast traffic. A detection system and block tamper GOOSE messages, using the OpenFlow protocol for control of the electrical system communication network, were developed. In addition, an intelligent system using an Artificial Neural Network (ANN) Nonlinear Autoregressive Model with Exogenous Input (NARX) for predicting of the GOOSE messages traffic and the detection of Distributed Deny of Service attack (DDOS) were also developed. The results obtained show that the OpenFlow protocol may be a valuable tool for network control, however, manufacturers should maturely carry on with its implementation in the switches, so that it be used in substation communication networks. The traffic prediction system of the GOOSE messages presented interesting benefits for the security of the communication network, demonstrating potential to built a DDOS attack detection system performed by GOOSE messages on the communication network of electric power substations.

The problems of incidents arising in computer networks and the computer system security level evaluation are considered in the thesis. The main research objects are incidents arising in computer networks, intrusion detection systems and network scanning types. The aim of the thesis is the investigation of the incidents in the computer networks and computer system security level evaluation. The following main tasks are solved in the work: classification of attacks and numerical evaluation of the attack severity level evaluation; quantitative evaluation of the computer system security level; investigation of the dependence of the computer system performance and availability on the attacks affecting the system and defense mechanisms used in it; development of the model simulating the computer network horizontal and vertical scanning. The thesis consists of general characteristic of the research, five chapters and general conclusions. General characteristic of the thesis is dedicated to an introduction of the problem and its topicality. The aims and tasks of the work are also formulated; the used methods and novelty of solutions are described; the author‘s publications and structure of the thesis are presented. Chapter 1 covers the analysis of existing publications related to the problems of the thesis. The survey of the intrusion detection systems is presented and methods of the intrusion detection are analyzed. The currently existing techniques of the attack classification are... [to full text] / Disertacijoje nagrinėjamos incidentų kompiuterių tinkluose ir kompiuterių sistemų saugumo lygio įvertinimo problemos. Pagrindiniai tyrimo objektai yra incidentai kompiuterių tinkluose, atakų atpažinimo sistemos ir kompiuterių tinklo žvalgos būdai. Disertacijos tikslas – incidentų kompiuterių tinkluose tyrimas ir kompiuterių sistemų saugumo lygio įvertinimas. Darbe sprendžiami šie pagrindiniai uždaviniai: atakų klasifikavimas ir jų sunkumo lygio skaitinis įvertinimas; kompiuterių sistemos saugumo lygio kiekybinis įvertinimas; kompiuterių sistemos našumo ir pasiekiamumo priklausomybės nuo sistemą veikiančių atakų ir joje naudojamų apsaugos mechanizmų tyrimas; modelio, imituojančio kompiuterių tinklo horizontalią ir vertikalią žvalgą kūrimas. Disertaciją sudaro įvadas, penki skyriai ir bendrosios išvados. Įvadiniame skyriuje nagrinėjamas problemos aktualumas, formuluojamas darbo tikslas bei uždaviniai, aprašomas mokslinis darbo naujumas, pristatomi autoriaus pranešimai ir publikacijos, disertacijos struktūra. Pirmasis skyrius skirtas literatūros apžvalgai. Jame apžvelgiamos atakų atpažinimo sistemos, analizuojami atakų atpažinimo metodai. Nagrinėjami atakų klasifikavimo būdai. Didelis dėmesys skiriamas kompiuterių sistemos saugumo lygio įvertinimo metodams, kompiuterių prievadų žvalgos būdams ir žvalgos atpažinimo metodams. Skyriaus pabaigoje formuluojamos išvados ir konkretizuojami disertacijos uždaviniai. Antrajame skyriuje pateikta sudaryta atakų nukreiptų į kompiuterių... [toliau žr. visą tekstą]