Intrusion Detection from Heterogenous Sensors

RÉSUMÉ De nos jours, la protection des systèmes et réseaux informatiques contre différentes attaques avancées et distribuées constitue un défi vital pour leurs propriétaires. L’une des menaces critiques à la sécurité de ces infrastructures informatiques sont les attaques réalisées par des individus dont les intentions sont malveillantes, qu’ils soient situés à l’intérieur et à l’extérieur de l’environnement du système, afin d’abuser des services disponibles, ou de révéler des informations confidentielles. Par conséquent, la gestion et la surveillance des systèmes informatiques est un défi considérable considérant que de nouvelles menaces et attaques sont découvertes sur une base quotidienne. Les systèmes de détection d’intrusion, Intrusion Detection Systems (IDS) en anglais, jouent un rôle clé dans la surveillance et le contrôle des infrastructures de réseau informatique. Ces systèmes inspectent les événements qui se produisent dans les systèmes et réseaux informatiques et en cas de détection d’activité malveillante, ces derniers génèrent des alertes afin de fournir les détails des attaques survenues. Cependant, ces systèmes présentent certaines limitations qui méritent d’être adressées si nous souhaitons les rendre suffisamment fiables pour répondre aux besoins réels. L’un des principaux défis qui caractérise les IDS est le grand nombre d’alertes redondantes et non pertinentes ainsi que le taux de faux-positif générés, faisant de leur analyse une tâche difficile pour les administrateurs de sécurité qui tentent de déterminer et d’identifier les alertes qui sont réellement importantes. Une partie du problème réside dans le fait que la plupart des IDS ne prennent pas compte les informations contextuelles (type de systèmes, applications, utilisateurs, réseaux, etc.) reliées à l’attaque. Ainsi, une grande partie des alertes générées par les IDS sont non pertinentes en ce sens qu’elles ne permettent de comprendre l’attaque dans son contexte et ce, malgré le fait que le système ait réussi à correctement détecter une intrusion. De plus, plusieurs IDS limitent leur détection à un seul type de capteur, ce qui les rend inefficaces pour détecter de nouvelles attaques complexes. Or, ceci est particulièrement important dans le cas des attaques ciblées qui tentent d’éviter la détection par IDS conventionnels et par d’autres produits de sécurité. Bien que de nombreux administrateurs système incorporent avec succès des informations de contexte ainsi que différents types de capteurs et journaux dans leurs analyses, un problème important avec cette approche reste le manque d’automatisation, tant au niveau du stockage que de l’analyse. Afin de résoudre ces problèmes d’applicabilité, divers types d’IDS ont été proposés dans les dernières années, dont les IDS de type composant pris sur étagère, commercial off-the-shelf (COTS) en anglais, qui sont maintenant largement utilisés dans les centres d’opérations de sécurité, Security Operations Center (SOC) en anglais, de plusieurs grandes organisations. D’un point de vue plus général, les différentes approches proposées peuvent être classées en différentes catégories : les méthodes basées sur l’apprentissage machine, tel que les réseaux bayésiens, les méthodes d’extraction de données, les arbres de décision, les réseaux de neurones, etc., les méthodes impliquant la corrélation d’alertes et les approches fondées sur la fusion d’alertes, les systèmes de détection d’intrusion sensibles au contexte, les IDS dit distribués et les IDS qui reposent sur la notion d’ontologie de base. Étant donné que ces différentes approches se concentrent uniquement sur un ou quelques-uns des défis courants reliés aux IDS, au meilleure de notre connaissance, le problème dans son ensemble n’a pas été résolu. Par conséquent, il n’existe aucune approche permettant de couvrir tous les défis des IDS modernes précédemment mentionnés. Par exemple, les systèmes qui reposent sur des méthodes d’apprentissage machine classent les événements sur la base de certaines caractéristiques en fonction du comportement observé pour un type d’événements, mais ils ne prennent pas en compte les informations reliées au contexte et les relations pouvant exister entre plusieurs événements. La plupart des techniques de corrélation d’alerte proposées ne considèrent que la corrélation entre plusieurs capteurs du même type ayant un événement commun et une sémantique d’alerte similaire (corrélation homogène), laissant aux administrateurs de sécurité la tâche d’effectuer la corrélation entre les différents types de capteurs hétérogènes. Pour leur part, les approches sensibles au contexte n’emploient que des aspects limités du contexte sous-jacent. Une autre limitation majeure des différentes approches proposées est l’absence d’évaluation précise basée sur des ensembles de données qui contiennent des scénarios d’attaque complexes et modernes. À cet effet, l’objectif de cette thèse est de concevoir un système de corrélation d’événements qui peut prendre en considération plusieurs types hétérogènes de capteurs ainsi que les journaux de plusieurs applications (par exemple, IDS/IPS, pare-feu, base de données, système d’exploitation, antivirus, proxy web, routeurs, etc.). Cette méthode permettra de détecter des attaques complexes qui laissent des traces dans les différents systèmes, et d’incorporer les informations de contexte dans l’analyse afin de réduire les faux-positifs. Nos contributions peuvent être divisées en quatre parties principales : 1) Nous proposons la Pasargadae, une solution complète sensible au contexte et reposant sur une ontologie de corrélation des événements, laquelle effectue automatiquement la corrélation des événements par l’analyse des informations recueillies auprès de diverses sources. Pasargadae utilise le concept d’ontologie pour représenter et stocker des informations sur les événements, le contexte et les vulnérabilités, les scénarios d’attaques, et utilise des règles d’ontologie de logique simple écrites en Semantic Query-Enhance Web Rule Language (SQWRL) afin de corréler diverse informations et de filtrer les alertes non pertinentes, en double, et les faux-positifs. 2) Nous proposons une approche basée sur, méta-événement , tri topologique et l‘approche corrélation d‘événement basée sur sémantique qui emploie Pasargadae pour effectuer la corrélation d’événements à travers les événements collectés de plusieurs capteurs répartis dans un réseau informatique. 3) Nous proposons une approche alerte de fusion basée sur sémantique, contexte sensible, qui s‘appuie sur certains des sous-composantes de Pasargadae pour effectuer une alerte fusion hétérogène recueillies auprès IDS hétérogènes. 4) Dans le but de montrer le niveau de flexibilité de Pasargadae, nous l’utilisons pour mettre en oeuvre d’autres approches proposées d‘alertes et de corrélation d‘événements. La somme de ces contributions représente une amélioration significative de l’applicabilité et la fiabilité des IDS dans des situations du monde réel. Afin de tester la performance et la flexibilité de l’approche de corrélation d’événements proposés, nous devons aborder le manque d’infrastructures expérimental adéquat pour la sécurité du réseau. Une étude de littérature montre que les approches expérimentales actuelles ne sont pas adaptées pour générer des données de réseau de grande fidélité. Par conséquent, afin d’accomplir une évaluation complète, d’abord, nous menons nos expériences sur deux scénarios d’étude d‘analyse de cas distincts, inspirés des ensembles de données d’évaluation DARPA 2000 et UNB ISCX IDS. Ensuite, comme une étude déposée complète, nous employons Pasargadae dans un vrai réseau informatique pour une période de deux semaines pour inspecter ses capacités de détection sur un vrai terrain trafic de réseau. Les résultats obtenus montrent que, par rapport à d’autres améliorations IDS existants, les contributions proposées améliorent considérablement les performances IDS (taux de détection) tout en réduisant les faux positifs, non pertinents et alertes en double.----------ABSTRACT Nowadays, protecting computer systems and networks against various distributed and multi-steps attack has been a vital challenge for their owners. One of the essential threats to the security of such computer infrastructures is attacks by malicious individuals from inside and outside of the system environment to abuse available services, or reveal their confidential information. Consequently, managing and supervising computer systems is a considerable challenge, as new threats and attacks are discovered on a daily basis. Intrusion Detection Systems (IDSs) play a key role in the surveillance and monitoring of computer network infrastructures. These systems inspect events occurred in computer systems and networks and in case of any malicious behavior they generate appropriate alerts describing the attacks’ details. However, there are a number of shortcomings that need to be addressed to make them reliable enough in the real-world situations. One of the fundamental challenges in real-world IDS is the large number of redundant, non-relevant, and false positive alerts that they generate, making it a difficult task for security administrators to determine and identify real and important alerts. Part of the problem is that most of the IDS do not take into account contextual information (type of systems, applications, users, networks, etc.), and therefore a large portion of the alerts are non-relevant in that even though they correctly recognize an intrusion, the intrusion fails to reach its objectives. Additionally, to detect newer and complicated attacks, relying on only one detection sensor type is not adequate, and as a result many of the current IDS are unable to detect them. This is especially important with respect to targeted attacks that try to avoid detection by conventional IDS and by other security products. While many system administrators are known to successfully incorporate context information and many different types of sensors and logs into their analysis, an important problem with this approach is the lack of automation in both storage and analysis. In order to address these problems in IDS applicability, various IDS types have been proposed in the recent years and commercial off-the-shelf (COTS) IDS products have found their way into Security Operations Centers (SOC) of many large organizations. From a general perspective, these works can be categorized into: machine learning based approaches including Bayesian networks, data mining methods, decision trees, neural networks, etc., alert correlation and alert fusion based approaches, context-aware intrusion detection systems, distributed intrusion detection systems, and ontology based intrusion detection systems. To the best of our knowledge, since these works only focus on one or few of the IDS challenges, the problem as a whole has not been resolved. Hence, there is no comprehensive work addressing all the mentioned challenges of modern intrusion detection systems. For example, works that utilize machine learning approaches only classify events based on some features depending on behavior observed with one type of events, and they do not take into account contextual information and event interrelationships. Most of the proposed alert correlation techniques consider correlation only across multiple sensors of the same type having a common event and alert semantics (homogeneous correlation), leaving it to security administrators to perform correlation across heterogeneous types of sensors. Context-aware approaches only employ limited aspects of the underlying context. The lack of accurate evaluation based on the data sets that encompass modern complex attack scenarios is another major shortcoming of most of the proposed approaches. The goal of this thesis is to design an event correlation system that can correlate across several heterogeneous types of sensors and logs (e.g. IDS/IPS, firewall, database, operating system, anti-virus, web proxy, routers, etc.) in order to hope to detect complex attacks that leave traces in various systems, and incorporate context information into the analysis, in order to reduce false positives. To this end, our contributions can be split into 4 main parts: 1) we propose the Pasargadae comprehensive context-aware and ontology-based event correlation framework that automatically performs event correlation by reasoning on the information collected from various information resources. Pasargadae uses ontologies to represent and store information on events, context and vulnerability information, and attack scenarios, and uses simple ontology logic rules written in Semantic Query-Enhance Web Rule Language (SQWRL) to correlate various information and filter out non-relevant alerts and duplicate alerts, and false positives. 2) We propose a meta-event based, topological sort based and semantic-based event correlation approach that employs Pasargadae to perform event correlation across events collected form several sensors distributed in a computer network. 3) We propose a semantic-based context-aware alert fusion approach that relies on some of the subcomponents of Pasargadae to perform heterogeneous alert fusion collected from heterogeneous IDS. 4) In order to show the level of flexibility of Pasargadae, we use it to implement some other proposed alert and event correlation approaches. The sum of these contributions represent a significant improvement in the applicability and reliability of IDS in real-world situations. In order to test the performance and flexibility of the proposed event correlation approach, we need to address the lack of experimental infrastructure suitable for network security. A study of the literature shows that current experimental approaches are not appropriate to generate high fidelity network data. Consequently, in order to accomplish a comprehensive evaluation, first, we conduct our experiments on two separate analysis case study scenarios, inspired from the DARPA 2000 and UNB ISCX IDS evaluation data sets. Next, as a complete field study, we employ Pasargadae in a real computer network for a two weeks period to inspect its detection capabilities on a ground truth network traffic. The results obtained show that compared to other existing IDS improvements, the proposed contributions significantly improve IDS performance (detection rate) while reducing false positives, non-relevant and duplicate alerts

Cybersecurity knowledge graphs

Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge

Context-aware Security for Vehicles and Fleets: A Survey

Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future

Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections

Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination

The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course

Addressing Multi-Stage Attacks Using Expert Knowledge and Contextual Information

New challenges in the cyber-threat domain are driven by tactical and meticulously designed Multi-Stage Attacks (MSAs). Current state-of-the-art (SOTA) Intrusion Detection Systems (IDSs) are developed to detect individual attacks through the use of signatures or identifying manifested anomalies in the network environment. However, an MSA differs from traditional one-off network attacks as it requires a set of sequential stages, whereby each stage may not be malicious when manifested individually, therefore, potentially be underestimated by current IDSs. This work proposes a new approach towards addressing this challenging type of cyber-attacks by employing external sources of information, beyond the conventional use of signatures and monitored network data. In particular, both expert knowledge and contextual information in the form of Pattern-of-Life (PoL) of the network are shown to be influential in giving an advantage against SOTA techniques. We compare our proposed anomaly-based IDS, based on decision making powered by the Dempster-Shafer (D-S) Theory and Fuzzy Cognitive Maps (FCMs), against Snort, one of the most widely deployed IDS in the world. Our results verify that the use of contextual information improves the efficiency of our IDS by enhancing the Detection Rate (DR) of MSAs by almost 50%

Present and Future of Network Security Monitoring

This work was funded by the Ministry of Science and Innovation through CDTI through the Ayudas Cervera para Centros Tecnologicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project EGIDA under Grant CER-20191012, and in part by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (ERDF) funds under Project TIN2017-83494-R.Network Security Monitoring (NSM) is a popular term to refer to the detection of security incidents by monitoring the network events. An NSM system is central for the security of current networks, given the escalation in sophistication of cyberwarfare. In this paper, we review the state-of-the-art in NSM, and derive a new taxonomy of the functionalities and modules in an NSM system. This taxonomy is useful to assess current NSM deployments and tools for both researchers and practitioners. We organize a list of popular tools according to this new taxonomy, and identify challenges in the application of NSM in modern network deployments, like Software Defined Network (SDN) and Internet of Things (IoT).Ministry of Science and Innovation through CDTI through the Ayudas Cervera para Centros Tecnologicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project EGIDA CER-20191012Spanish Ministry of Economy and CompetitivenessEuropean Regional Development Fund (ERDF) funds TIN2017-83494-