Detecção de intrusão em redes de computadores utilizando Floresta de Caminhos Ótimos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Kappa-PSO-ARTMAP Fuzzy: uma metodologia para detecção de intrusos baseado em seleção de atributos e otimização de parâmetros numa rede neural ARTMAP Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

IPSFlow: Um framework para Sistema de Prevenção de Intrusão baseado em Redes Definidas por Software

Os Sistemas de Detecção e Prevenção de Intrusão (Intrusion Detection Systems – IDS e Intrusion Prevention Systems - IPS) são ferramentas bastante conhecidas e bem consagradas no mundo da segurança da informação. Porém, a falta de integração com os equipamentos de rede como switches e roteadores acaba limitando a atuação destas ferramentas e exige um bom dimensionamento de recursos de hardware como processamento, memória e interfaces de rede de alta velocidade, utilizados para implementá-las. Diante de diversas limitações deparadas por pesquisadores e administradores de redes, surgiu o conceito de Rede Definida por Software (Software Defined Network – SDN), que ao separar os planos de controle e de dados, permite adaptar o funcionamento da rede de acordo com as necessidades de cada um. Desta forma, devido à padronização e flexibilidade propostas pelas SDNs, e das limitações apresentadas dos IPSs, esta dissertação de mestrado propõe o IPSFlow, um framework que utiliza uma rede baseada na arquitetura SDN e o protocolo OpenFlow para a criação de um IPS com ampla cobertura e que permite bloquear um tráfego caracterizado pelos IDS(s) como malicioso no equipamento mais próximo da origem. Para validar o framework, experimentos no ambiente virtual Mininet foram realizados utilizando-se o Snort como IDS para analisar tráfego de varredura (scan) gerado pelo Nmap de um host ao outro. Os resultados coletados apresentam que o IPSFlow funcionou conforme planejado ao efetuar o bloqueio de 85% do tráfego de varredura.

Segurança em redes sem fio: estudo sobre o desenvolvimento de conjuntos de dados para comparação de IDS

Pós-graduação em Engenharia Elétrica - FEIS

Correlação e visualização de alertas de segurança em redes de computadores

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de anomalias e redução de falsos positivos em sistemas de detecção de intrusão baseados em rede utilizando métodos de agrupamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Contribución a la automatización de sistemas de respuesta frente a intrusiones mediante ontologías

La seguridad en redes informáticas es un área que ha sido ampliamente estudiada y objeto de una extensa investigación en los últimos años. Debido al continuo incremento en la complejidad y sofisticación de los ataques informáticos, el aumento de su velocidad de difusión, y la lentitud de reacción frente a las intrusiones existente en la actualidad, se hace patente la necesidad de mecanismos de detección y respuesta a intrusiones, que detecten y además sean capaces de bloquear el ataque, y mitiguen su impacto en la medida de lo posible. Los Sistemas de Detección de Intrusiones o IDSs son tecnologías bastante maduras cuyo objetivo es detectar cualquier comportamiento malicioso que ocurra en las redes. Estos sistemas han evolucionado rápidamente en los últimos años convirtiéndose en herramientas muy maduras basadas en diferentes paradigmas, que mejoran su capacidad de detección y le otorgan un alto nivel de fiabilidad. Por otra parte, un Sistema de Respuesta a Intrusiones (IRS) es un componente de seguridad que puede estar presente en la arquitectura de una red informática, capaz de reaccionar frente a los incidentes detectados por un Sistema de Detección de Intrusiones (IDS). Por desgracia, esta tecnología no ha evolucionado al mismo ritmo que los IDSs, y la reacción contra los ataques detectados es lenta y básica, y los sistemas presentan problemas para ejecutar respuestas de forma automática. Esta tesis doctoral trata de hacer frente al problema existente en la reacción automática frente a intrusiones, mediante el uso de ontologías, lenguajes formales de especificación de comportamiento y razonadores semánticos como base de la arquitectura del sistema de un sistema de respuesta automática frente a intrusiones o AIRS. El objetivo de la aproximación es aprovechar las ventajas de las ontologías en entornos heterogéneos, además de su capacidad para especificar comportamiento sobre los objetos que representan los elementos del dominio modelado. Esta capacidad para especificar comportamiento será de gran utilidad para que el AIRS infiera la respuesta óptima frente a una intrusión en el menor tiempo posible. Abstract Security in networks is an area that has been widely studied and has been the focus of extensive research over the past few years. The number of security events is increasing, and they are each time more sophisticated, and quickly spread, and slow reaction against intrusions, there is a need for intrusion detection and response systems to dynamically adapt so as to better detect and respond to attacks in order to mitigate them or reduce their impact. Intrusion Detection Systems (IDSs) are mature technologies whose aim is detecting malicious behavior in the networks. These systems have quickly evolved and there are now very mature tools based on different paradigms (statistic anomaly-based, signature-based and hybrids) with a high level of reliability. On the other hand, Intrusion Response System (IRS) is a security technology able to react against the intrusions detected by IDS. Unfortunately, the state of the art in IRSs is not as mature as with IDSs. The reaction against intrusions is slow and simple, and these systems have difficulty detecting intrusions in real time and triggering automated responses. This dissertation is to address the existing problem in automated reactions against intrusions using ontologies, formal behaviour languages and semantic reasoners as the basis of the architecture of an automated intrusion response systems or AIRS. The aim is to take advantage of ontologies in heterogeneous environments, in addition to its ability to specify behavior of objects representing the elements of the modeling domain. This ability to specify behavior will be useful for the AIRS in the inference process of the optimum response against an intrusion, as quickly as possible.

Análisis de ataques avanzados y sus técnicas de detección

Los ataques a redes de información son cada vez más sofisticados y exigen una constante evolución y mejora de las técnicas de detección. Para ello, en este proyecto se ha diseñado e implementado una plataforma cooperativa para la detección de intrusiones basada en red. En primer lugar, se ha realizado un estudio teórico previo del marco tecnológico relacionado con este ámbito, en el que se describe y caracteriza el software que se utiliza para realizar ataques a sistemas (malware) así como los métodos que se utilizan para llegar a transmitir ese software (vectores de ataque). En el documento también se describen los llamados APT, que son ataques dirigidos con una gran inversión económica y temporal. Estos pueden englobar todos los malware y vectores de ataque existentes. Para poder evitar estos ataques, se estudiarán los sistemas de detección y prevención de intrusiones, describiendo brevemente los algoritmos que se tienden a utilizar en la actualidad. En segundo lugar, se ha planteado y desarrollado una plataforma en red dedicada al análisis de paquetes y conexiones para detectar posibles intrusiones. Este sistema está orientado a sistemas SCADA (Supervisory Control And Data Adquisition) aunque funciona sobre cualquier red IPv4/IPv6, para ello se definirá previamente lo que es un sistema SCADA, así como sus partes principales. Para implementar el sistema se han utilizado dispositivos de bajo consumo llamados Raspberry PI, estos se ubican entre la red y el equipo final que se quiera analizar. En ellos se ejecutan 2 aplicaciones desarrolladas de tipo cliente-servidor (la Raspberry central ejecutará la aplicación servidora y las esclavas la aplicación cliente) que funcionan de forma cooperativa utilizando la tecnología distribuida de Hadoop, la cual se explica previamente. Mediante esta tecnología se consigue desarrollar un sistema completamente escalable. La aplicación servidora muestra una interfaz gráfica que permite administrar la plataforma de análisis de forma centralizada, pudiendo ver así las alarmas de cada dispositivo y calificando cada paquete según su peligrosidad. El algoritmo desarrollado en la aplicación calcula el ratio de paquetes/tiempo que entran/salen del equipo final, procesando los paquetes y analizándolos teniendo en cuenta la información de señalización, creando diferentes bases de datos que irán mejorando la robustez del sistema, reduciendo así la posibilidad de ataques externos. Para concluir, el proyecto inicial incluía el procesamiento en la nube de la aplicación principal, pudiendo administrar así varias infraestructuras concurrentemente, aunque debido al trabajo extra necesario se ha dejado preparado el sistema para poder implementar esta funcionalidad. En el caso experimental actual el procesamiento de la aplicación servidora se realiza en la Raspberry principal, creando un sistema escalable, rápido y tolerante a fallos. ABSTRACT. The attacks to networks of information are increasingly sophisticated and demand a constant evolution and improvement of the technologies of detection. For this project it is developed and implemented a cooperative platform for detect intrusions based on networking. First, there has been a previous theoretical study of technological framework related to this area, which describes the software used for attacks on systems (malware) as well as the methods used in order to transmit this software (attack vectors). In this document it is described the APT, which are attacks directed with a big economic and time inversion. These can contain all existing malware and attack vectors. To prevent these attacks, intrusion detection systems and prevention intrusion systems will be discussed, describing previously the algorithms tend to use today. Secondly, a platform for analyzing network packets has been proposed and developed to detect possible intrusions in SCADA (Supervisory Control And Data Adquisition) systems. This platform is designed for SCADA systems (Supervisory Control And Data Acquisition) but works on any IPv4 / IPv6 network. Previously, it is defined what a SCADA system is and the main parts of it. To implement it, we used low-power devices called Raspberry PI, these are located between the network and the final device to analyze it. In these Raspberry run two applications client-server developed (the central Raspberry runs the server application and the slaves the client application) that work cooperatively using Hadoop distributed technology, which is previously explained. Using this technology is achieved develop a fully scalable system. The server application displays a graphical interface to manage analytics platform centrally, thereby we can see each device alarms and qualifying each packet by dangerousness. The algorithm developed in the application calculates the ratio of packets/time entering/leaving the terminal device, processing the packets and analyzing the signaling information of each packet, reating different databases that will improve the system, thereby reducing the possibility of external attacks. In conclusion, the initial project included cloud computing of the main application, being able to manage multiple concurrent infrastructure, but due to the extra work required has been made ready the system to implement this funcionality. In the current test case the server application processing is made on the main Raspberry, creating a scalable, fast and fault-tolerant system.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Introduction to Security Onion

Security Onion is a Network Security Manager (NSM) platform that provides multiple Intrusion Detection Systems (IDS) including Host IDS (HIDS) and Network IDS (NIDS). Many types of data can be acquired using Security Onion for analysis. This includes data related to: Host, Network, Session, Asset, Alert and Protocols. Security Onion can be implemented as a standalone deployment with server and sensor included or with a master server and multiple sensors allowing for the system to be scaled as required. Many interfaces and tools are available for management of the system and analysis of data such as Sguil, Snorby, Squert and Enterprise Log Search and Archive (ELSA). These interfaces can be used for analysis of alerts and captured events and then can be further exported for analysis in Network Forensic Analysis Tools (NFAT) such as NetworkMiner, CapME or Xplico. The Security Onion platform also provides various methods of management such as Secure SHell (SSH) for management of server and sensors and Web client remote access. All of this with the ability to replay and analyse example malicious traffic makes the Security Onion a suitable low cost alternative for Network Security Management. In this paper, we have a feature and functionality review for the Security Onion in terms of: types of data, configuration, interface, tools and system management.

A multiagent based strategy for detecting attacks in databases in a distributed mode

This paper presents a distributed hierarchical multiagent architecture for detecting SQL injection attacks against databases. It uses a novel strategy, which is supported by a Case-Based Reasoning mechanism, which provides to the classifier agents with a great capacity of learning and adaptation to face this type of attack. The architecture combines strategies of intrusion detection systems such as misuse detection and anomaly detection. It has been tested and the results are presented in this paper.

Global Optimization Algorithms and their Application to Distributed Systems

In this report, we discuss the application of global optimization and Evolutionary Computation to distributed systems. We therefore selected and classified many publications, giving an insight into the wide variety of optimization problems which arise in distributed systems. Some interesting approaches from different areas will be discussed in greater detail with the use of illustrative examples.

UWB radar sensor networks: Detection algorithms design and experimental analysis

In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.

Conceptual Modelling of Complex Network Management Systems

Society, as we know it today, is completely dependent on computer networks, Internet and distributed systems, which place at our disposal the necessary services to perform our daily tasks. Moreover, and unconsciously, all services and distributed systems require network management systems. These systems allow us to, in general, maintain, manage, configure, scale, adapt, modify, edit, protect or improve the main distributed systems. Their role is secondary and is unknown and transparent to the users. They provide the necessary support to maintain the distributed systems whose services we use every day. If we don’t consider network management systems during the development stage of main distributed systems, then there could be serious consequences or even total failures in the development of the distributed systems. It is necessary, therefore, to consider the management of the systems within the design of distributed systems and systematize their conception to minimize the impact of the management of networks within the project of distributed systems. In this paper, we present a formalization method of the conceptual modelling for design of a network management system through the use of formal modelling tools, thus allowing from the definition of processes to identify those responsible for these. Finally we will propose a use case to design a conceptual model intrusion detection system in network.

Early warning systems for cyber defence

Cybercriminals ramp up their efforts with sophisticated techniques while defenders gradually update their typical security measures. Attackers often have a long-term interest in their targets. Due to a number of factors such as scale, architecture and nonproductive traffic however it makes difficult to detect them using typical intrusion detection techniques. Cyber early warning systems (CEWS) aim at alerting such attempts in their nascent stages using preliminary indicators. Design and implementation of such systems involves numerous research challenges such as generic set of indicators, intelligence gathering, uncertainty reasoning and information fusion. This paper discusses such challenges and presents the reader with compelling motivation. A carefully deployed empirical analysis using a real world attack scenario and a real network traffic capture is also presented.