@phdthesis{Jaeger2018,
  author    = {Jaeger, David},
  title     = {Enabling Big Data security analytics for advanced network attack detection},
  doi       = {10.25932/publishup-43571},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-435713},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XVII, 201, XXXIII},
  year      = {2018},
  abstract  = {The last years have shown an increasing sophistication of attacks against enterprises. Traditional security solutions like firewalls, anti-virus systems and generally Intrusion Detection Systems (IDSs) are no longer sufficient to protect an enterprise against these advanced attacks. One popular approach to tackle this issue is to collect and analyze events generated across the IT landscape of an enterprise. This task is achieved by the utilization of Security Information and Event Management (SIEM) systems. However, the majority of the currently existing SIEM solutions is not capable of handling the massive volume of data and the diversity of event representations. Even if these solutions can collect the data at a central place, they are neither able to extract all relevant information from the events nor correlate events across various sources. Hence, only rather simple attacks are detected, whereas complex attacks, consisting of multiple stages, remain undetected. Undoubtedly, security operators of large enterprises are faced with a typical Big Data problem. In this thesis, we propose and implement a prototypical SIEM system named Real-Time Event Analysis and Monitoring System (REAMS) that addresses the Big Data challenges of event data with common paradigms, such as data normalization, multi-threading, in-memory storage, and distributed processing. In particular, a mostly stream-based event processing workflow is proposed that collects, normalizes, persists and analyzes events in near real-time. In this regard, we have made various contributions in the SIEM context. First, we propose a high-performance normalization algorithm that is highly parallelized across threads and distributed across nodes. Second, we are persisting into an in-memory database for fast querying and correlation in the context of attack detection. Third, we propose various analysis layers, such as anomaly- and signature-based detection, that run on top of the normalized and correlated events. As a result, we demonstrate our capabilities to detect previously known as well as unknown attack patterns. Lastly, we have investigated the integration of cyber threat intelligence (CTI) into the analytical process, for instance, for correlating monitored user accounts with previously collected public identity leaks to identify possible compromised user accounts. In summary, we show that a SIEM system can indeed monitor a large enterprise environment with a massive load of incoming events. As a result, complex attacks spanning across the whole network can be uncovered and mitigated, which is an advancement in comparison to existing SIEM systems on the market.},
  language  = {en}
}
@phdthesis{Sapegin2018,
  author    = {Sapegin, Andrey},
  title     = {High-Speed Security Log Analytics Using Hybrid Outlier Detection},
  doi       = {10.25932/publishup-42611},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426118},
  school      = {Universit{\"a}t Potsdam},
  pages     = {162},
  year      = {2018},
  abstract  = {The rapid development and integration of Information Technologies over the last decades influenced all areas of our life, including the business world. Yet not only the modern enterprises become digitalised, but also security and criminal threats move into the digital sphere. To withstand these threats, modern companies must be aware of all activities within their computer networks. The keystone for such continuous security monitoring is a Security Information and Event Management (SIEM) system that collects and processes all security-related log messages from the entire enterprise network. However, digital transformations and technologies, such as network virtualisation and widespread usage of mobile communications, lead to a constantly increasing number of monitored devices and systems. As a result, the amount of data that has to be processed by a SIEM system is increasing rapidly. Besides that, in-depth security analysis of the captured data requires the application of rather sophisticated outlier detection algorithms that have a high computational complexity. Existing outlier detection methods often suffer from performance issues and are not directly applicable for high-speed and high-volume analysis of heterogeneous security-related events, which becomes a major challenge for modern SIEM systems nowadays. This thesis provides a number of solutions for the mentioned challenges. First, it proposes a new SIEM system architecture for high-speed processing of security events, implementing parallel, in-memory and in-database processing principles. The proposed architecture also utilises the most efficient log format for high-speed data normalisation. Next, the thesis offers several novel high-speed outlier detection methods, including generic Hybrid Outlier Detection that can efficiently be used for Big Data analysis. Finally, the special User Behaviour Outlier Detection is proposed for better threat detection and analysis of particular user behaviour cases. The proposed architecture and methods were evaluated in terms of both performance and accuracy, as well as compared with classical architecture and existing algorithms. These evaluations were performed on multiple data sets, including simulated data, well-known public intrusion detection data set, and real data from the large multinational enterprise. The evaluation results have proved the high performance and efficacy of the developed methods. All concepts proposed in this thesis were integrated into the prototype of the SIEM system, capable of high-speed analysis of Big Security Data, which makes this integrated SIEM platform highly relevant for modern enterprise security applications.},
  language  = {en}
}
@article{RoschkeChengMeinel2013,
  author    = {Roschke, Sebastian and Cheng, Feng and Meinel, Christoph},
  title     = {High-quality attack graph-based IDS correlation},
  series = {Logic journal of the IGPL},
  volume    = {21},
  journal   = {Logic journal of the IGPL},
  number    = {4},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-0751},
  doi       = {10.1093/jigpal/jzs034},
  pages     = {571 -- 591},
  year      = {2013},
  abstract  = {Intrusion Detection Systems are widely deployed in computer networks. As modern attacks are getting more sophisticated and the number of sensors and network nodes grow, the problem of false positives and alert analysis becomes more difficult to solve. Alert correlation was proposed to analyse alerts and to decrease false positives. Knowledge about the target system or environment is usually necessary for efficient alert correlation. For representing the environment information as well as potential exploits, the existing vulnerabilities and their Attack Graph (AG) is used. It is useful for networks to generate an AG and to organize certain vulnerabilities in a reasonable way. In this article, a correlation algorithm based on AGs is designed that is capable of detecting multiple attack scenarios for forensic analysis. It can be parameterized to adjust the robustness and accuracy. A formal model of the algorithm is presented and an implementation is tested to analyse the different parameters on a real set of alerts from a local network. To improve the speed of the algorithm, a multi-core version is proposed and a HMM-supported version can be used to further improve the quality. The parallel implementation is tested on a multi-core correlation platform, using CPUs and GPUs.},
  language  = {en}
}