A dataset generator for next generation system call host intrusion detection systems

Pendleton, Marcus; Xu, Shouhuai

doi:10.1109/milcom.2017.8170835

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…Moreover, the normal behavior and its origin are either not described at all or only insufficiently described. Pendleton and Xu describe in [1] based on a syscall collector for data generation. For instrumentation the Intel tool "Pin" is used 1 .…”

Section: Background and Related Workmentioning

confidence: 99%

“…Pendleton and Xu describe in [1] based on a syscall collector for data generation. For instrumentation the Intel tool "Pin" is used 1 . It allows applications to be extended with their own source code at runtime and to profile the application.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In recent years, various data sets have been published to evaluate a HIDS. As it turns out all them have at least one serious problem [1]. In addition for comparability and evaluation, their metrics must be applied to a set of coherent standardized data sets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Standardized container virtualization approach for collecting host intrusion detection data

Rohling¹,

Grimmer²,

Kreubel³

et al. 2019

Annals of Computer Science and Information Systems

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Anomaly-based Intrusion Detection Systems (IDS) can be instrumental in detecting attacks on IT systems. For evaluation and training of IDS, data sets containing samples of common security-scenarios are essential. Existing data sets are not sufficient for training modern IDS. This work introduces a new methodology for recording data that is useful in the context of intrusion detection. The approach presented is comprised of a system architecture as well as a novel framework for simulating security-related scenarios.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Standardized container virtualization approach for collecting host intrusion detection data

Rohling¹,

Grimmer²,

Kreubel³

et al. 2019

Annals of Computer Science and Information Systems

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show abstract

“…The need for datasets and their generation are recurrent topics related to several research fields. Thus, there are published works in research areas as varied as radio signal processing [29], vehicular technology [30,31], vehicle-to-vehicle and vehicle-to-infrastructure wireless communication [32], computer vision [33] and pattern recognition [34], cyber threat intelligence [35], host intrusion detection [36], network intrusion detection system [37,38], smart grids [39], and software vulnerabilities [40][41][42][43][44][45], among many others.…”

Section: Introductionmentioning

confidence: 99%

Collecting Vulnerable Source Code from Open-Source Repositories for Dataset Generation

et al. 2020

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Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human intervention. However, these algorithms require data-driven engines based on the processing of huge amounts of data, known as datasets. This paper introduces the SonarCloud Vulnerable Code Prospector for C (SVCP4C). This tool aims to collect vulnerable source code from open source repositories linked to SonarCloud, an online tool that performs static analysis and tags the potentially vulnerable code. The tool provides a set of tagged files suitable for extracting features and creating training datasets for Machine Learning algorithms. This study presents a descriptive analysis of these files and overviews current status of C vulnerabilities, specifically buffer overflow, in the reviewed public repositories.

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“…They present an experiment using a network simulator with 10 simulated scenarios of mobility of VANET hosts and 5 types of emulated security threats with the capacity to define the total number of vehicles and the number of malicious hosts in the VANET. In [124] a generator architecture (semi-synthetic approach) is proposed for datasets of system calls used for host intrusion detection systems (HIDS). The generator architecture is generic, but it is demonstrated using Ubuntu Linux and Mozilla Firefox as the profiled application.…”

Section: Synthetic Data Generationmentioning

confidence: 99%

Novel applications of Machine Learning to Network Traffic Analysis and Prediction

Martín¹

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A dataset generator for next generation system call host intrusion detection systems

Cited by 12 publications

References 13 publications

Standardized container virtualization approach for collecting host intrusion detection data

Standardized container virtualization approach for collecting host intrusion detection data

Collecting Vulnerable Source Code from Open-Source Repositories for Dataset Generation

Novel applications of Machine Learning to Network Traffic Analysis and Prediction

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