Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation

Avoiditals: Enhanced Cyber-Attack Taxonomy In Securing Information Technology Infrastructure

An operation of an organization is currently using a digital environment which opens to potential cyber-attacks. These phenomena become worst as the cyberattack landscape is changing rapidly. The impact of cyber-attacks varies depending on the scope of the organization and the value of assets that need to be protected. It is difficult to assess the damage to an organization from cyberattacks due to a lack of understanding of tools, metrics, and knowledge on the type of attacks and their impacts. Hence, this paper aims to identify domains and sub-domains of cyber-attack taxonomy to facilitate the understanding of cyber-attacks. Four phases are carried in this research: identify existing cyber-attack taxonomy, determine and classify domains and sub-domains of cyber-attack, and construct the enhanced cyber-attack taxonomy. The existing cyber-attack taxonomies are analyzed, domains and sub-domains are selected based on the focus and objectives of the research, and the proposed taxonomy named AVOIDITALS Cyber-attack Taxonomy is constructed. AVOIDITALS consists of 8 domains, 105 sub-domains, 142 sub-sub-domains, and 90 other sub-sub-domains that act as a guideline to assist administrators in determining cyber-attacks through cyber-attacks pattern identification that commonly occurred on digital infrastructure and provide the best prevention method to minimize impact. This research can be further developed in line with the emergence of new types and categories of current cyberattacks and the future

Leveraging the Training Data Partitioning to Improve Events Characterization in Intrusion Detection Systems

The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) play a primary role since they are responsible for analyzing and classifying each network activity as legitimate or illegitimate, allowing us to take the necessary countermeasures at the appropriate time. However, these systems are not infallible due to several reasons, the most important of which are the constant evolution of the attacks (e.g., zero-day attacks) and the problem that many of the attacks have behavior similar to those of legitimate activities, and therefore they are very hard to identify. This work relies on the hypothesis that the subdivision of the training data used for the IDS classification model definition into a certain number of partitions, in terms of events and features, can improve the characterization of the network events, improving the system performance. The non-overlapping data partitions train independent classification models, classifying the event according to a majority-voting rule. A series of experiments conducted on a benchmark real-world dataset support the initial hypothesis, showing a performance improvement with respect to a canonical training approach

AVOIDIT IRS: An Issue Resolution System To Resolve Cyber Attacks

Cyber attacks have greatly increased over the years and the attackers have progressively improved in devising attacks against specific targets. Cyber attacks are considered a malicious activity launched against networks to gain unauthorized access causing modification, destruction, or even deletion of data. This dissertation highlights the need to assist defenders with identifying and defending against cyber attacks. In this dissertation an attack issue resolution system is developed called AVOIDIT IRS (AIRS). AVOIDIT IRS is based on the attack taxonomy AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). Attacks are collected by AIRS and classified into their respective category using AVOIDIT.Accordingly, an organizational cyber attack ontology was developed using feedback from security professionals to improve the communication and reusability amongst cyber security stakeholders. AIRS is developed as a semi-autonomous application that extracts unstructured external and internal attack data to classify attacks in sequential form. In doing so, we designed and implemented a frequent pattern and sequential classification algorithm associated with the five classifications in AVOIDIT. The issue resolution approach uses inference to educate the defender on the plausible cyber attacks. The AIRS can work in conjunction with an intrusion detection system (IDS) to provide a heuristic to cyber security breaches within an organization. AVOIDIT provides a framework for classifying appropriate attack information, which is fundamental in devising defense strategies against such cyber attacks. The AIRS is further used as a knowledge base in a game inspired defense architecture to promote game model selection upon attack identification. Future work will incorporate honeypot attack information to improve attack identification, classification, and defense propagation.In this dissertation, 1,025 common vulnerabilities and exposures (CVEs) and over 5,000 lines of log files instances were captured in the AIRS for analysis. Security experts were consulted to create rules to extract pertinent information and algorithms to correlate identified data for notification. The AIRS was developed using the Codeigniter [74] framework to provide a seamless visualization tool for data mining regarding potential cyber attacks relative to web applications. Testing of the AVOIDIT IRS revealed a recall of 88%, precision of 93%, and a 66% correlation metric

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture, and the effectiveness of self-organization and entangled hierarchies

Deteção de ataques de negação de serviços distribuídos na origem

From year to year new records of the amount of traffic in an attack are established, which demonstrate not only the constant presence of distributed denialof-service attacks, but also its evolution, demarcating itself from the other network threats. The increasing importance of resource availability alongside the security debate on network devices and infrastructures is continuous, given the preponderant role in both the home and corporate domains. In the face of the constant threat, the latest network security systems have been applying pattern recognition techniques to infer, detect, and react more quickly and assertively. This dissertation proposes methodologies to infer network activities patterns, based on their traffic: follows a behavior previously defined as normal, or if there are deviations that raise suspicions about the normality of the action in the network. It seems that the future of network defense systems continues in this direction, not only by increasing amount of traffic, but also by the diversity of actions, services and entities that reflect different patterns, thus contributing to the detection of anomalous activities on the network. The methodologies propose the collection of metadata, up to the transport layer of the osi model, which will then be processed by the machien learning algorithms in order to classify the underlying action. Intending to contribute beyond denial-of-service attacks and the network domain, the methodologies were described in a generic way, in order to be applied in other scenarios of greater or less complexity. The third chapter presents a proof of concept with attack vectors that marked the history and a few evaluation metrics that allows to compare the different classifiers as to their success rate, given the various activities in the network and inherent dynamics. The various tests show flexibility, speed and accuracy of the various classification algorithms, setting the bar between 90 and 99 percent.De ano para ano são estabelecidos novos recordes de quantidade de tráfego num ataque, que demonstram não só a presença constante de ataques de negação de serviço distribuídos, como também a sua evolução, demarcando-se das outras ameaças de rede. A crescente importância da disponibilidade de recursos a par do debate sobre a segurança nos dispositivos e infraestruturas de rede é contínuo, dado o papel preponderante tanto no dominio doméstico como no corporativo. Face à constante ameaça, os sistemas de segurança de rede mais recentes têm vindo a aplicar técnicas de reconhecimento de padrões para inferir, detetar e reagir de forma mais rápida e assertiva. Esta dissertação propõe metodologias para inferir padrões de atividades na rede, tendo por base o seu tráfego: se segue um comportamento previamente definido como normal, ou se existem desvios que levantam suspeitas sobre normalidade da ação na rede. Tudo indica que o futuro dos sistemas de defesa de rede continuará neste sentido, servindo-se não só do crescente aumento da quantidade de tráfego, como também da diversidade de ações, serviços e entidades que refletem padrões distintos contribuindo assim para a deteção de atividades anómalas na rede. As metodologias propõem a recolha de metadados, até á camada de transporte, que seguidamente serão processados pelos algoritmos de aprendizagem automática com o objectivo de classificar a ação subjacente. Pretendendo que o contributo fosse além dos ataques de negação de serviço e do dominio de rede, as metodologias foram descritas de forma tendencialmente genérica, de forma a serem aplicadas noutros cenários de maior ou menos complexidade. No quarto capítulo é apresentada uma prova de conceito com vetores de ataques que marcaram a história e, algumas métricas de avaliação que permitem comparar os diferentes classificadores quanto à sua taxa de sucesso, face às várias atividades na rede e inerentes dinâmicas. Os vários testes mostram flexibilidade, rapidez e precisão dos vários algoritmos de classificação, estabelecendo a fasquia entre os 90 e os 99 por cento.Mestrado em Engenharia de Computadores e Telemátic