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In the field of network security, the detection of intrusions is an important task to prevent and analyse attacks.
In recent years, an increasing number of works have been published on this subject, which perform this detection based on machine learning techniques.
Thereby not only the well-studied detection of intrusions, but also the real-time capability must be considered.
This thesis addresses the real-time functionality of machine learning based network intrusion detection.
For this purpose we introduce the network feature generator library PyNetFlowGen, which is designed to allow real-time processing of network data.
This library generates 83 statistical features based on reassembled data flows.
The introduced performant Cython implementation allows processing individual packets within 4.58 microseconds.
Based on the generated features, machine learning models were examined with regard to their runtime and real-time capabilities.
The selected Decision-Tree-Classifier model created in Python was further optimised by transpiling it into C-Code, what reduced the prediction time of a single sample to 3.96 microseconds on average.
Based on the feature generator and the machine learning model, an basic IDS system was implemented, which allows a data throughput between 63.7 Mbit/s and 2.5 Gbit/s.
Android is the most popular mobile operating system. Its omnipresence leads to the fact that it is also the most popular target amongst malware developers and other computer criminals. Hence, this thesis shows the security-relevant structures of Android’s system and application architecture. Furthermore, it provides laboratory exercises on various security-related issues to understand them not only theoretically but also deal with them in a practical way. In order to provide infrastructure-independent education, the exercises are based on Android Virtual Devices (AVDs).
Implementation and Evaluation of an Assisting Fuzzer Harness Generation Tool for AUTOSAR Code
(2024)
The digitalization in vehicles tends to add more connectivity such as over-the-air (OTA) updates. To achieve this digitization, each ECU (Electronic Control Unit) becomes smarter and needs to support more and more different externally available protocols such as TLS, which increases the attack surface for attackers. To ensure the security of a vehicle, fuzzing has proven to be an effective method to discover memory-related security vulnerabilities. Fuzzing the software run- ning on a ECU is not an easy task and requires a harness written by a human. The author needs a deep understanding of the specific service and protocol, which is time consuming. To reduce the time needed by a harness author, this thesis aims to develop FuzzAUTO, the first assistant harness generation tool targeting the AUTOSAR (AUTomotive Open System ARchitecture) BSW (Basic Software) to support manual harness generation.
The core logging and tracing facility in Windows operating system is called Event Tracing for Windows (ETW).
Data sources providing events for ETW are instrumented all over the operating system.
That means most hard- and software assets in a Windows system are instrumented with ETW and so are able to contribute low-level information.
ETW can be used by developers and administrators to get low-level information about operating system's activity.
We describe existing tools to interact with the ETW faciltity and evaluate them based on defined criteria.
Based on relevant application scenarios, we show the richness of informational content for debugging or detecting security incidents with ETW.
The widely used instrumentation of ETW in the operating system and its application results also in security risks according to confidentiality.
Based on common ETW providers we show the impact to confidentiality what ETW offers an adversary.
At the end we evaluate solutions and approaches for a customizable telemetry infrastructure using ETW in large-scale environments.
This work addresses the conceptualization, design, and implementation of an Application Programming Interface (API) for the Common Security Advisory Framework (CSAF) 2.0, introducing another method for distributing CSAF documents in addition to two already existing methods. These don't allow the use of flexible queries as well as filtering, which makes it difficult for operators of software and hardware to use CSAF. An API is intended to simplify this process and thus advance the automation goal of CSAF.
First, it is evaluated whether the current standard allows the implementation of an API. Any conflicts are highlighted and suggestions for standard adaptations are made. Based on these results, the API is designed to meet the previously defined requirements. Subsequently, a proof of concept is successfully developed according to the design and extensively tested with specially prepared test data. Finally, the results and the necessary standard adjustments are summarized and justified.
The conceptual design and the implementation were successfully completed. However, during the implementation of the proof of concept, some routes could not be fully implemented.