Web applications are growing rapidly and as the amount of web sites globallyincreases so do security threats. Complex applications often interact with thirdparty services and databases to fetch information and often interactions requireuser input. Intruders are targeting web applications specifically and they are ahuge security threat to organizations and a way to combat this is to haveintrusion detection systems. Most common web attack methods are wellresearched and documented however due to time constraints developers oftenwrite applications fast and may not implement the best security practices. Thisreport describes one way to implement a intrusion detection system thatspecifically detects web based attacks.

Computer security is to protect the data inside the computer, relay the information, expose the information, or reduce the level of security to some extent. The communication contents are the main target of any malicious intent to interrupt one or more of the three aspects of the information security triad (confidentiality, integrity, and availability). This thesis aims to provide a comprehensive idea of network traffic analysis, various anomaly or intrusion detection systems, the tools used for it, and finally, a comparison of two Network Traffic Analysis (NTA) tools available in the market: Splunk and Security Onion and comparing their finding to analyse their feasibility and efficiency on Anomaly detection. Splunk and Security Onion were found to be different in the method of monitoring, User Interface (UI), and the observations noted. Further scope for future works is also suggested from the conclusions made.

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

Dans cette thèse, nous présentons les risques posés par les Menaces Persistentes Avancées (APTs) et proposons une approche en deux temps pour distinguer les attaques qui en font partie. Ce travail fait partie d'Akheros, un Système de Détection d'Intrusion (IDS) autonome développé par trois doctorants. L'idée est d'utiliser l'apprentissage machine pour détecté des évènements inattendus et vérifier s'ils posent un risque de sécurité. La dernière étape, et le sujet de cette thèse, est de mettre en évidence les APT. Les campagnes d'APT sont particulièrement dangereuses car les attaquants sont compétents et ont un but précis ainsi que du temps et de l'argent. Nous partons des résultats des parties précédentes d'Akheros: une liste d'évènements traduisible en flux d'information et qui indique quand des attaques sont détectées. Nous faisons ressortir les liens entre attaques en utilisant le Suivi de Flux d'Information: nous ajoutons une nouvelle teinte pour chaque attaque. Lors de la propagation, si une teinte se trouve en amont d'un flux qui fait partie d'une attaque, alors les deux attaques sont liés. Certaines attaques se trouvent liées par erreur car les évènements que nous utilisons ne sont pas assez précis, d'où l'approche en deux temps. Dans le cas où certaines attaques ne sont pas détectées, la teinte de cette attaque n'est pas créée, cependant, les autres teintes sont propagées normalement, et l'attaque précédent l'attaque non détectée sera liée à l'attaque lui faisant suite. Le deuxième temps de l'approche est de retirer les liens erronés. Nous utilisons un Modèle de Markov Caché pour représenter les APTs et retirons les campagnes qui ne suivent pas le modèle. Ceci fonctionne car les APTs, quoique toutes différentes, passent par les mêmes phases. Ces phases sont les états cachés du modèle. Les observations sont les types d'attaques effectuées pendant ces phases. De plus, les actions futures des attaquants dépendent des résultats de l'action en cours, ce qui satisfait l'hypothèse de Markov. Le score utilisé pour classer les campagnes potentielles de la plus proche d'une APT à la plus éloigné est basé sur un algorithme de Viterbi modifié pour prendre en compte les attaques non détectées potentielles. / In this thesis, we present the risks posed by Advanced Persitent Threats (APTs) and propose a two-step approach for recognising when detected attacks are part of one. This is part of the Akheros solution, a fully autonomous Intrusion Detection System (IDS) being developed in collaboration by three PhD students. The idea is to use machine learning to detect unexpected events and check if they present a security risk. The last part, and the subject of this thesis, is the highlighting of APT. APTs campaigns are particularly dangerous because they are performed by skilled attackers with a precise goal and time and money on their side.We start with the results from the previous part of the Akheros IDS: a list of events, which can be translated to flows of information, with an indication for events found to be attacks. We find links between attacks using Information Flow Tracking. To do so, we create a new taint for each detected attack and propagate it. Whenever a taint is on the input of an event that is part of another attack, then the two attacks are linked. However, the links are only potential because the events used are not precise enough, which leads to erroneously propagated taints. In the case of an undetected attack, no taint is created for that attack, but the other taints are still propagated as normal so that previous attack is still linked to the next attack, only skipping the undetected one. The second step of the approach is to filter out the erroneous links. To do so, we use a Hidden Markov Model to represent APTs and remove potential attack campaign that do not fit the model. This is possible because, while each APT is different, they all go through the same phases, which form the hidden states of our model. The visible observations are the kind of attacks performed during these phases. In addition, the results in one phase dictate what the attackers do next, which fits the Markov hypothesis. The score used to rank potential attack campaign from most likely an APT to least likely so is based on a customised Viterbi algorithm in order to take into account potentially undetected attacks.

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

Made available in DSpace on 2016-08-17T14:53:24Z (GMT). No. of bitstreams: 1 Dissertacao Josenilson.pdf: 3842701 bytes, checksum: 33761f8b37e7f3c354f33c31fcb658cf (MD5) Previous issue date: 2013-06-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The elasticity and large consumption of computational resources are becoming attractive for intruders to exploit the cloud vulnerabilities to launch attacks or have access of private and privileged data of cloud users. In order to effectively protect the cloud and its users, the IDS must have the capability to quickly scale up and down the quantity of sensors according to the resources provisioned, besides of isolating the access to the system levels and infrastructure. The protection against internal cloud threats must be planned because of the non-adequate threatening identification system in most protection systems. For this, the proposed solution uses virtual machines features as fast recovery, start, stop, migration to other hosts and cross-platform execution in IDS based VM, to monitor the internal environment of the cloud virtual machines by inserting data capture sensors at the local network of the VM users, this way, it can detect suspicious user behaviors. / A elasticidade e abundante disponibilidade de recursos computacionais são atrativos para intrusos explorarem vulnerabilidades da nuvem, podendo assim lançar ataques contra usuários legítimos para terem acesso a dados privados e privilegiados. Para proteger efetivamente os usuários da nuvem, um Sistema de Detecção de Intrusão ou IDS deve ter a capacidade de expandir-se, aumentado ou diminuindo rapidamente a quantidade de sensores, de acordo com o provisionamento de recursos, além de isolar o acesso aos níveis de sistema e infraestrutura. A proteção contra ameaças internas na nuvem deve ser planejada, pois a maioria dos sistemas de proteção não identifica adequadamente ameaças internas ao sistema. Para isso, a solução proposta utiliza as características de máquinas virtuais como rápida inicialização, rápida recuperação, parada, migração entre diferentes hosts e execução em múltiplas plataformas na construção de um IDS que visa monitorar o ambiente interno de máquinas virtuais da nuvem, inserindo sensores de captura de dados na rede local das VMs dos usuários, podendo assim detectar comportamentos suspeitos dos usuários.

V této práci se zabýváme vylepšením systémů pro odhalení síťových průniků. Konkrétně se zaměřujeme na behaviorální analýzu, která využívá data extrahovaná z jednotlivých síťových spojení. Tyto informace využívá popsaný framework k obfuskaci cílených síťových útoků, které zneužívají zranitelností v sadě soudobých zranitelných služeb. Z Národní databáze zranitelností od NIST vybíráme zranitelné služby, přičemž se omezujeme jen na roky 2018 a 2019. Ve výsledku vytváříme nový dataset, který sestává z přímých a obfuskovaných útoků, provedených proti vybraným zranitelným službám, a také z jejich protějšků ve formě legitimního provozu. Nový dataset vyhodnocujeme za použití několika klasifikačních technik, a demonstrujeme, jak důležité je trénovat tyto klasifikátory na obfuskovaných útocích, aby se zabránilo jejich průniku bez povšimnutí. Nakonec provádíme křížové vyhodnocení datasetů pomocí nejmodernějšího datasetu ASNM-NPBO a našeho datasetu. Výsledky ukazují důležitost opětovného trénování klasifikátorů na nových zranitelnostech při zachování dobrých schopností detekovat útoky na staré zranitelnosti.

This thesis deals with the principles of network traffic obfuscation, in order to avoid its detection by the Intrusion Detection System installed in the network. At the beginning of the work, reader is familiarized with the fundamental principle of the basic types of IDS and introduced into the matter of obfuscation techniques, that serve as stepping stone in order to create our own library, whose design is described in the last part of the work. The outcome of the work is represented by a library, that provides all the implemented techniques for further use. The library can be well utilized in penetration testing of the new systems or used by the attacker.

This Thesis details the research on Machine Learning techniques that are central in performing Anomaly and Masquerade attack detection. The main focus is put on Web Applications because of their immense popularity and ubiquity. This popularity has led to an increase in attacks, making them the most targeted entry point to violate a system. Specifically, a group of attacks that range from identity theft using social engineering to cross site scripting attacks, aim at exploiting and masquerading users. Masquerading attacks are even harder to detect due to their resemblance with normal sessions, thus posing an additional burden. Concerning prevention, the diversity and complexity of those systems makes it harder to define reliable protection mechanisms. Additionally, new and emerging attack patterns make manually configured and Signature based systems less effective with the need to continuously update them with new rules and signatures. This leads to a situation where they eventually become obsolete if left unmanaged. Finally the huge amount of traffic makes manual inspection of attacks and False alarms an impossible task. To tackle those issues, Anomaly Detection systems are proposed using powerful and proven Machine Learning algorithms. Gravitating around the context of Anomaly Detection and Machine Learning, this Thesis initially defines several basic definitions such as user behavior, normality and normal and anomalous behavior. Those definitions aim at setting the context in which the proposed method is targeted and at defining the theoretical premises. To ease the transition into the implementation phase, the underlying methodology is also explained in detail. Naturally, the implementation is also presented, where, starting from server logs, a method is described on how to pre-process the data into a form suitable for classification. This preprocessing phase was constructed from several statistical analyses and normalization methods (Univariate Selection, ANOVA) to clear and transform the given logs and perform feature selection. Furthermore, given that the proposed detection method is based on the source and1request URLs, a method of aggregation is proposed to limit the user privacy and classifier over-fitting issues. Subsequently, two popular classification algorithms (Multinomial Naive Bayes and Support Vector Machines) have been tested and compared to define which one performs better in our given situations. Each of the implementation steps (pre-processing and classification) requires a number of different parameters to be set and thus a method called Hyper-parameter optimization is defined. This method searches for the parameters that improve the classification results. Moreover, the training and testing methodology is also outlined alongside the experimental setup. The Hyper-parameter optimization and the training phases are the most computationally intensive steps, especially given a large number of samples/users. To overcome this obstacle, a scaling methodology is also defined and evaluated to demonstrate its ability to handle larger data sets. To complete this framework, several other options have been also evaluated and compared to each other to challenge the method and implementation decisions. An example of this, is the "Transitions-vs-Pages" dilemma, the block restriction effect, the DR usefulness and the classification parameters optimization. Moreover, a Survivability Analysis is performed to demonstrate how the produced alarms could be correlated affecting the resulting detection rates and interval times. The implementation of the proposed detection method and outlined experimental setup lead to interesting results. Even so, the data-set that has been used to produce this evaluation is also provided online to promote further investigation and research on this field. / Det här arbetet behandlar forskningen på maskininlärningstekniker som är centrala i utförandet av detektion av anomali- och maskeradattacker. Huvud-fokus läggs på webbapplikationer på grund av deras enorma popularitet och att de är så vanligt förekommande. Denna popularitet har lett till en ökning av attacker och har gjort dem till den mest utsatta punkten för att bryta sig in i ett system. Mer specifikt så syftar en grupp attacker som sträcker sig från identitetsstölder genom social ingenjörskonst, till cross-site scripting-attacker, på att exploatera och maskera sig som olika användare. Maskeradattacker är ännu svårare att upptäcka på grund av deras likhet med vanliga sessioner, vilket utgör en ytterligare börda. Vad gäller förebyggande, gör mångfalden och komplexiteten av dessa system det svårare att definiera pålitliga skyddsmekanismer. Dessutom gör nya och framväxande attackmönster manuellt konfigurerade och signaturbaserade system mindre effektiva på grund av behovet att kontinuerligt uppdatera dem med nya regler och signaturer. Detta leder till en situation där de så småningom blir obsoleta om de inte sköts om. Slutligen gör den enorma mängden trafik manuell inspektion av attacker och falska alarm ett omöjligt uppdrag. För att ta itu med de här problemen, föreslås anomalidetektionssystem som använder kraftfulla och beprövade maskininlärningsalgoritmer. Graviterande kring kontexten av anomalidetektion och maskininlärning, definierar det här arbetet först flera enkla definitioner såsom användarbeteende, normalitet, och normalt och anomalt beteende. De här definitionerna syftar på att fastställa sammanhanget i vilket den föreslagna metoden är måltavla och på att definiera de teoretiska premisserna. För att under-lätta övergången till implementeringsfasen, förklaras även den bakomliggande metodologin i detalj. Naturligtvis presenteras även implementeringen, där, med avstamp i server-loggar, en metod för hur man kan för-bearbeta datan till en form som är lämplig för klassificering beskrivs. Den här för´-bearbetningsfasen konstruerades från flera statistiska analyser och normaliseringsmetoder (univariate se-lection, ANOVA) för att rensa och transformera de givna loggarna och utföra feature selection. Dessutom, givet att en föreslagen detektionsmetod är baserad på käll- och request-URLs, föreslås en metod för aggregation för att begränsa problem med överanpassning relaterade till användarsekretess och klassificerare. Efter det så testas och jämförs två populära klassificeringsalgoritmer (Multinomialnaive bayes och Support vector machines) för att definiera vilken som fungerar bäst i våra givna situationer. Varje implementeringssteg (för-bearbetning och klassificering) kräver att ett antal olika parametrar ställs in och således definieras en metod som kallas Hyper-parameter optimization. Den här metoden söker efter parametrar som förbättrar klassificeringsresultaten. Dessutom så beskrivs tränings- och test-ningsmetodologin kortfattat vid sidan av experimentuppställningen. Hyper-parameter optimization och träningsfaserna är de mest beräkningsintensiva stegen, särskilt givet ett stort urval/stort antal användare. För att övervinna detta hinder så definieras och utvärderas även en skalningsmetodologi baserat på dess förmåga att hantera stora datauppsättningar. För att slutföra detta ramverk, utvärderas och jämförs även flera andra alternativ med varandra för att utmana metod- och implementeringsbesluten. Ett exempel på det är ”Transitions-vs-Pages”-dilemmat, block restriction-effekten, DR-användbarheten och optimeringen av klassificeringsparametrarna. Dessu-tom så utförs en survivability analysis för att demonstrera hur de producerade alarmen kan korreleras för att påverka den resulterande detektionsträ˙säker-heten och intervalltiderna. Implementeringen av den föreslagna detektionsmetoden och beskrivna experimentuppsättningen leder till intressanta resultat. Icke desto mindre är datauppsättningen som använts för att producera den här utvärderingen också tillgänglig online för att främja vidare utredning och forskning på området.