1,724 research outputs found
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
Toward an efficient ontology-based event correlation in SIEM
Cooperative intrusion detection use several intrusion detection systems (IDS) and analyzers in order to build a reliable overview of the monitored system trough a central security information and event management system (SIEM). In such environment, the definition of a shared vocabulary describing the exchanged information between tools is prominent. Since these pieces of information are structured, we propose in this paper to use an ontological representation based on Description Logics (DLs) which is a powerful tool for knowledge representation. Moreover, DLs are able to ensure a decidable reasoning. An alert correlation prototype is presented using this ontology, and an illustrative attack scenario is carried out to show the usefulness of the proposed ontolog
Toward an efficient ontology-based event correlation in SIEM
Cooperative intrusion detection use several intrusion detection systems (IDS) and analyzers in order to build a reliable overview of the monitored system trough a central security information and event management system (SIEM). In such environment, the definition of a shared vocabulary describing the exchanged information between tools is prominent. Since these pieces of information are structured, we propose in this paper to use an ontological representation based on Description Logics (DLs) which is a powerful tool for knowledge representation. Moreover, DLs are able to ensure a decidable reasoning. An alert correlation prototype is presented using this ontology, and an illustrative attack scenario is carried out to show the usefulness of the proposed ontolog
Security information management with frame-based attack presentation and first-order reasoning
Internet has grown by several orders of magnitude in recent years, and this growth has escalated the importance of computer security. Intrusion Detection System (IDS) is used to protect computer networks. However, the overwhelming flow of log data generated by IDS hamper security administrators from uncovering new insights and hidden attack scenarios. Security Information Management (SIM) is a new growing area of interest for intrusion detection. The research work in this dissertation explores the semantics of attack behaviors and designs Frame-based Attack Representation and First-order logic Automatic Reasoning (FAR-FAR) using linguistics and First-order Logic (FOL) based approaches. Techniques based on linguistics can provide efficient solutions to acquire semantic information from alert contexts, while FOL can tackle a wide variety of problems in attack scenario reasoning and querying. In FAR-FAR, the modified case grammar PCTCG is used to convert raw alerts into frame-structured alert streams and the alert semantic network 2-AASN is used to generate the attack scenarios, which can then inform the security administrator. Based on the alert contexts and attack ontology, Space Vector Model (SVM) is applied to categorize the intrusion stages. Furthermore, a robust Variant Packet Sending-interval Link Padding algorithm (VPSLP) is proposed to prevent links between the IDS sensors and the FAR-FAR agents from traffic analysis attacks. Recent measurements and studies demonstrated that real network traffic exhibits statistical self-similarity over several time scales. The bursty traffic anomaly detection method, Multi-Time scaling Detection (MTD), is proposed to statistically analyze network traffic\u27s Histogram Feature Vector to detect traffic anomalies
On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach
While digitization of distribution grids through information and
communications technology brings numerous benefits, it also increases the
grid's vulnerability to serious cyber attacks. Unlike conventional systems,
attacks on many industrial control systems such as power grids often occur in
multiple stages, with the attacker taking several steps at once to achieve its
goal. Detection mechanisms with situational awareness are needed to detect
orchestrated attack steps as part of a coherent attack campaign. To provide a
foundation for detection and prevention of such attacks, this paper addresses
the detection of multi-stage cyber attacks with the aid of a graph-based cyber
intelligence database and alert correlation approach. Specifically, we propose
an approach to detect multi-stage attacks by leveraging heterogeneous data to
form a knowledge base and employ a model-based correlation approach on the
generated alerts to identify multi-stage cyber attack sequences taking place in
the network. We investigate the detection quality of the proposed approach by
using a case study of a multi-stage cyber attack campaign in a
future-orientated power grid pilot.Comment: IEEE International Conference on Communications, Control, and
Computing Technologies for Smart Grids (SmartGridComm) 202
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