Data-Driven Anomaly Detection in Industrial Networks

Since the conception of the first Programmable Logic Controllers (PLCs) in the 1960s, Industrial Control Systems (ICSs) have evolved vastly. From the primitive isolated setups, ICSs have become increasingly interconnected, slowly forming the complex networked environments, collectively known as Industrial Networks (INs), that we know today. Since ICSs are responsible for a wide range of physical processes, including those belonging to Critical Infrastructures (CIs), securing INs is vital for the well-being of modern societies. Out of the many research advances on the field, Anomaly Detection Systems (ADSs) play a prominent role. These systems monitor IN and/or ICS behavior to detect abnormal events, known or unknown. However, as the complexity of INs has increased, monitoring them in the search of anomalous trends has effectively become a Big Data problem. In other words, IN data has become too complex to process it by traditional means, due to its large scale, diversity and generation speeds. Nevertheless, ADSs designed for INs have not evolved at the same pace, and recent proposals are not designed to handle this data complexity, as they do not scale well or do not leverage the majority of the data types created in INs. This thesis aims to fill that gap, by presenting two main contributions: (i) a visual flow monitoring system and (ii) a multivariate ADS that is able to tackle data heterogeneity and to scale efficiently. For the flow monitor, we propose a system that, based on current flow data, builds security visualizations depicting network behavior while highlighting anomalies. For the multivariate ADS, we analyze the performance of Multivariate Statistical Process Control (MSPC) for detecting and diagnosing anomalies, and later we present a Big Data, MSPCinspired ADS that monitors field and network data to detect anomalies. The approaches are experimentally validated by building INs in test environments and analyzing the data created by them. Based on this necessity for conducting IN security research in a rigorous and reproducible environment, we also propose the design of a testbed that serves this purpose

Anomaly detection in SCADA systems: a network based approach

Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols. However, modern deployments commonly make use of commercial off-the-shelf devices and standard communication protocols, such as TCP/IP. Furthermore, these networks are becoming increasingly interconnected, allowing communication with corporate networks and even the Internet. As a result, SCADA networks become vulnerable to cyber attacks, being exposed to the same threats that plague traditional IT systems.\ud \ud In our view, measurements play an essential role in validating results in network research; therefore, our first objective is to understand how SCADA networks are utilized in practice. To this end, we provide the first comprehensive analysis of real-world SCADA traffic. We analyze five network packet traces collected at four different critical infrastructures: two water treatment facilities, one gas utility, and one electricity and gas utility. We show, for instance, that exiting network traffic models developed for traditional IT networks cannot be directly applied to SCADA network traffic. \ud \ud We also confirm two SCADA traffic characteristics: the stable connection matrix and the traffic periodicity, and propose two intrusion detection approaches that exploit them. In order to exploit the stable connection matrix, we investigate the use of whitelists at the flow level. We show that flow whitelists have a manageable size, considering the number of hosts in the network, and that it is possible to overcome the main sources of instability in the whitelists. In order to exploit the traffic periodicity, we focus our attention to connections used to retrieve data from devices in the field network. We propose PeriodAnalyzer, an approach that uses deep packet inspection to automatically identify the different messages and the frequency at which they are issued. Once such normal behavior is learned, PeriodAnalyzer can be used to detect data injection and Denial of Service attacks

Anomaly detection based on zone partition for security protection of industrial cyber-physical systems

A developing trend of traditional industrial systems is the integration of the cyber and physical domain to improve flexibility and the efficiency of supervision, management and control. But, the deep integration of these Industrial Cyber-Physical Systems (ICPSs), increases the potential for security threats. Attack detection, which forms initial protective barrier, plays an important role in overall security protection. However, most traditional methods focused on cyber information and ignored any limitations that might arise from the characteristics of the physical domain. In this paper, an anomaly detection approach based on zone partition is designed for ICPSs. In detail, initially an automated zone partition method ensuring crucial system states can be observed in more than one zone is designed. Then, methods of building zone function model which do not require any prior knowledge of the physical system are presented before analyzing the anomaly based on zone information. Finally, an experimental rig is constructed to verify the effectiveness of the proposed approach. The results demonstrate that the approach presents a high accuracy solution which also performs effectively in realtime

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community.While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of BigData frameworks such asHadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security,mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing INbased ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development

Resilient event collection in SIEM systems

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013A importância da Segurança da Informação tem crescido rapidamente nos últimos anos, com uma maior consciencialização da sociedade civil e das empresas para o problema. As notícias recorrentes de ataques direcionados e roubo de informação em larga escala que resultam em grandes prejuízos financeiros, por vezes tendo como consequência o encerramento das organizações envolvidas, justificam o investimento em mecanismos de proteção da informação. No âmago da capacidade para monitorização da segurança em tempo-real está o Security Operations Center (SOC), o conjunto de pessoas, processos e sistemas onde se concentram as capacidades de análise e resposta a incidentes de Segurança da Informação. A base tecnológica do SOC é construída sobre o sistema de Gestão de Informação e Eventos de Segurança, vulgo SIEM. Este sistema permite recolher eventos de segurança de diversas fontes e encontrar padrões de ataque analisando relações entre eles. No entanto, tal como acontece com todos os sistemas informáticos, um atacante que tenha conhecimento da sua existência irá procurar ultrapassar as proteções implementadas, prevenindo que a equipa do SOC seja alertada para o ataque em curso. A relevância dos sistemas SIEM tem vindo a aumentar no contexto da maior importância atribuída a questões de segurança da informação. Considerando um número cada vez mais elevado de eventos e as múltiplas origens onde estes são gerados, as equipas de monitorização estão cada vez mais dependentes de consolas únicas onde a informação é centralizada e processada. Como consequência existe também uma maior dependência dos sistemas centrais, tornando-os pontos únicos de falha. Os sistemas SIEM são intrinsecamente complexos devido à necessidade de recolha de eventos de segurança a partir de fontes com tecnologias muito diversas, com localizações dispersas. O facto de desempenharem diversas funções aumenta esta complexidade, necessitando de módulos para recolha, consolidação, processamento e armazenamento de eventos. Para além destes módulos, que podem ou não traduzir-se em componentes fisicamente distintos, os sistemas SIEM estão fortemente dependentes dos sensores colocados junto às fontes de eventos, bem como da rede de comunicações que permite o envio desses eventos entre os diversos componentes, até à consola central. A inexistência de investigação diretamente focada no aumento da resiliência dos sistemas SIEM resulta na implementação de soluções pouco adaptadas aos riscos e desafios associados a infraestruturas de segurança. Estando maioritariamente focada na proteção de segurança ao nível da rede, muitos dos desenvolvimentos recentes centram-se na capacidade de identificar padrões de tráfego maliciosos. Esta abordagem reflete-se em publicações direcionadas aos sistemas de detecção e prevenção de intrusões (IDS/IPS), com menos enfoque na implementação resiliente de sistemas SIEM. A nossa percepção, corroborada por uma pesquisa alargada de trabalhos desenvolvidos nesta área, aponta para um elevado número de implementações padrão, assumindo cenários teóricos e sem tomar em linha de conta o efeito de ataques contra o próprio sistema SIEM. Neste trabalho começamos por efetuar uma análise às falhas de segurança que podem afectar o desempenho do processo de recolha de eventos de segurança, incluindo falhas acidentais mas também possíveis ataques deliberados ao sistema SIEM que possibilitem a uma entidade maliciosa ultrapassar os mecanismos de segurança implementados. Com base nessa análise endereçamos os problemas de fiabilidade que afetam qualquer sistema informático, apontando soluções que permitam lidar com falhas acidentais e, dessa forma, aumentar a disponibilidade do sistema. Ao reduzir a probabilidade de falhas que impeçam a recolha de eventos de segurança, estamos a contribuir diretamente para diminuir a janela de oportunidade disponível para que ataques à infraestrutura não sejam detectados. Focando o risco de falhas maliciosas, propomos soluções que impeçam os atacantes de explorar com sucesso vulnerabilidades no processo de recolha de eventos de segurança. Este processo envolve sistemas heterogéneos, desde a fonte dos eventos até à consola central, passando pela rede de comunicação responsável por interligar toda a infraestrutura. Consideramos fundamental atingir um nível de robustez elevado, mesmo na presença de infraestrutura parcialmente comprometida. O principal objectivo deste trabalho passa por definir um método sistemático de recolha e correlação resiliente de eventos de segurança num sistema SIEM, mesmo na presença de componentes maliciosos sob controlo de atacantes. Para atingir este objectivo centramo-nos na robustez das regras de correlação, desde a sua concepção e desenho até à implementação final no sistema SIEM. Os sistemas SIEM contêm um conjunto alargado de regras padrão que, como demonstramos, partem de premissas demasiado optimistas relativamente ao processo de recolha de eventos. Descrevemos, ao longo do trabalho, de que forma estas regras padrão podem ser melhoradas para lidar com as diversas possibilidades de falhas e ataques maliciosos, aumentando desta forma a resiliência total do sistema SIEM e o nível de confiança que a equipa do SOC pode depositar nesta ferramenta essencial. Utilizando casos de uso reais, demonstramos a metodologia proposta para aumentar a resiliência das regras de correlação. Tendo como ponto de partida uma regra base, aplicamos passo a passo a metodologia, detalhando e avaliando cada evolução da regra, até ser atingido um nível de robustez elevado. Com o propósito de sistematizar a metodologia proposta para o aumento de qualidade das regras de correlação, desenvolvemos uma aplicação denominada AutoRule. Esta ferramenta recebe como entrada uma ou mais regras de correlação e efetua uma análise automática, detectando possíveis lacunas e sugerindo correções. Apesar de não suprir a necessidade de análise com base na experiência prática na definição de regras de correlação, a aplicação AutoRule permite à equipa de configuração do sistema SIEM atuar de forma precisa e direcionada, corrigindo as regras de correlação e, dessa forma, tornando-as mais resilientes. Finalmente, para demonstrar e medir a eficácia da nossa proposta, foi posta em prática a metodologia através de uma implementação em cenário real, recorrendo ao sistema SIEM utilizado para monitorizar os eventos de segurança na rede corporativa da EDP – Energias de Portugal, S.A. Tratando-se de um grupo multinacional com mais de 12000 colaboradores ativos, a rede informática monitorizada por este sistema SIEM fornece a possibilidade de analisar em larga escala os efeitos das melhorias propostas. A metodologia proposta para aumentar a resiliência das regras de correlação traduziu-se num acréscimo da eficácia das mesmas, resultando num sistema mais fiável. A consequência mais direta é uma melhoria operacional do SOC, que passa a dispor de informação mais precisa e mais adequada ao seu contexto de operação. Para além da proposta teórica, a implementação permitiu também validar a operação num cenário real da aplicação AutoRule, desenvolvida para automatizar a análise das regras de correlação. As melhorias introduzidas nas regras de correlação desenvolvidas no contexto da operação do SOC EDP, seguindo os passos da metodologia, foram sendo testadas com recurso à aplicação. Os resultados demonstram que a eficácia medida das regras correspondeu também a um melhor resultado obtido através da análise automática, existindo por isso motivos para confiar nesta análise. A aplicação AutoRule possibilitou ainda uma comparação entre as regras predefinidas, instaladas de forma automática com a solução ArcSight, e as regras que seguiram o processo de melhoria preconizado pela metodologia proposta. As avaliações finais que fazemos da implementação num cenário real são francamente positivas, ratificando a nossa proposta teórica e conferindo-lhe um elevado grau de confiança quanto à possibilidade de aplicação em larga escala, de forma independente da tecnologia de sistema SIEM escolhida.Information Security has become a relevant subject in recent years, with greater awareness to the topic from major companies and general public. The frequent news regarding targeted attacks and large-scale information thefts resulting in major financial losses, sometimes even resulting in company bankruptcy, justify investments in protection mechanisms. At the heart of real-time security monitoring is the Security Information and Event Management system, commonly known as SIEM. These systems allow for security event collection and pattern discovery, by analyzing relationships between those events in real-time. However, as with all computer systems, an attacker who is aware of its existence will seek to overcome the protection mechanisms in place, preventing the security experts from being alerted to the ongoing attacks. We present an analysis of possible attacks to a SIEM system and seek solutions to prevent successful exploitation of those attacks, even if the attackers are able to take control over part of the infrastructure. Instead of suggesting massive changes throughout the multiple systems and network components, we propose an approach based on the capabilities of the SIEM system to collect and correlate security events from multiple sources. We advocate that it is possible to detect faults, malicious or accidental, though real time analysis of the collected events using carefully crafted and resilient correlation rules. Our goal is to define a systematic method to resiliently collect and correlate security events in a SIEM system, despite the presence of components already under the control of attackers. The effectiveness of the proposed methodology is evaluated in a real production environment, simulating attacks and accidental failures and observing their effects in the capability of the SIEM system to identify abnormal behavior. We also develop and demonstrate an application capable of automatically analyzing correlation rules, identifying vulnerabilities and proposing improvements to increase heir overall resilience

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

SCADA Intrusion Detection System Test Framework

Master's thesis Information- and communication technology IKT590 - University of Agder 2017Supervisory control and data acquisition (SCADA) systems play an important role in our critical infrastructure (CI). Several of the protocols used in SCADA communication are old and lack of security mechanisms. This master thesis presents a SCADA Intrusion Detection System Test Framework that can be used to simulate SCADA traffic and detect malicious network activity. The framework uses a signature-based approach and utilize two different IDS engines, Suricata and Snort. The IDS engines include rule-sets for the IEC 60870-5-104, DNP3 and Modbus protocols. The IDS engines ships detected events to a distributed cluster and visualize them using a web interface. The experiments carried out in this project show that there generally is little difference between Suricata and Snort's ability to detect malicious traffic. Suricata is compatible with signatures written in snort lightweight rules description language. I did however, discover some compatibility issues. The purposed framework applies additional latency to the analysis of IDS events. The perceived latency was generally higher for Snort events than for Suricata events. The reason for this is probably the additional processing time applied by the implemented log conversion tool. Keywords: SCADA, IDS, SIE

Data Exfiltration:A Review of External Attack Vectors and Countermeasures

AbstractContext One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework