Uses and Challenges for Network Datasets

Heidemann, John; Papdopoulos, Christos

doi:10.1109/catch.2009.29

Cited by 15 publications

(3 citation statements)

References 56 publications

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“…Heide-mann and Papadopoulos used trace data in research to find common problems that cut across types of data and defined four aspects, namely privacy and anonymization, unavailable type of data such as local-observation or localinference, developing new techniques, and moving target and coverage. They suggested that one of the most important aspects even when some data already exist is continued observation [31].…”

Section: Testbed Design Strategiesmentioning

confidence: 99%

Towards a Reliable Intrusion Detection Benchmark Dataset

Sharafaldin¹,

Gharib²,

Lashkari³

et al. 2017

JSN

213

100

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The urgently growing number of security threats on Internet and intranet networks highly demands reliable security solutions. Among various options, Intrusion Detection (IDSs) and Intrusion Prevention Systems (IPSs) are used to defend network infrastructure by detecting and preventing attacks and malicious activities. The performance of a detection system is evaluated using benchmark datasets. There exist a number of datasets, such as DARPA98, KDD99, ISC2012, and ADFA13, that have been used by researchers to evaluate the performance of their intrusion detection and prevention approaches. However, not enough research has focused on the evaluation and assessment of the datasets themselves and there is no reliable dataset in this domain. In this paper, we present a comprehensive evaluation of the existing datasets using our proposed criteria, a design and evaluation framework for IDS and IPS datasets, and a dataset generation model to create a reliable IDS or IPS benchmark dataset.

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Section: Testbed Design Strategiesmentioning

confidence: 99%

Towards a Reliable Intrusion Detection Benchmark Dataset

Sharafaldin¹,

Gharib²,

Lashkari³

et al. 2017

JSN

213

100

View full text Add to dashboard Cite

show abstract

“…They enable researchers and practitioners to address a diverse array of real-world problems, from image and speech recognition to predictive analytics and natural language processing. [8] [10] Following are the well knows datasets used for classification purpose in AI [9]:…”

Section: Datasetsmentioning

confidence: 99%

A Comprehensive Overview of Artificial Intelligence-Based Classification Techniques

Manmeet Kaur

2024

Int. J. Sci. Res. Arch.

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Artificial Intelligence (AI) is the steering force for Industry 5.0 revolution. Artificial Intelligence (AI) is the key for the challenging task that are aimed to act as solution provider for various needs in fields of analytical, interactive, functional, text based or visual based for the physical world data associated problems. This paper provides an in-depth analysis of the current state and future directions of classification techniques in the field of Artificial Intelligence (AI). The paper encompasses a comprehensive overview of the widespread adoption of AI in various domains, including supervised machine learning, deep learning, and neural networks. It also explores the versatile applications of AI-based classification techniques, such as image classification, text classification, and speech recognition. The insight on classification techniques delves into the improved accuracy and clinical use of AI-based classification systems, highlighting their significance in areas like medical imaging, natural language processing, and predictive analytics. Furthermore, the paper addresses the future directions of AI-based classification, emphasizing the need to overcome challenges related to the reliability and safety of AI tools, as well as the integration of AI in clinical use and precision medicine. This insight study concludes by outlining the potential impact of AI-based classification techniques on various industries and the implications for future research and innovation in the field of Artificial Intelligence. This abstract encapsulates the key themes and findings from the research papers, providing a insightful overview of the current state and future directions of AI-based classification techniques.

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“…Since the year 1999, many frameworks for evaluating the IDS dataset have been proposed [2][3][4][5][6][7][8][9]. As per the latest existing research evaluation of frameworks, namely diversity of attacks, even characteristics, presented protocols, anonymity, wide-ranging interaction, complete capture, comprehensive network configuration, featuring dataset, ample traffic, metadata, heterogeneity, as well as labelling, are critical factors for developing a valid and comprehensive IDS dataset [7,9].…”

Section: Introductionmentioning

confidence: 99%

Genetic programming support vector machine model for a wireless intrusion detection system

Dhoot

Nazarov

Voronkov³

2022

Rossijskij tehnologičeskij žurnal

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Objectives. The rapid penetration of wireless communication technologies into the activities of both humans and Internet of Things (IoT) devices along with their widespread use by information consumers represents an epochal phenomenon. However, this is accompanied by the growing intensity of successful information attacks, involving the use of bot attacks via IoT, which, along with network attacks, has reached a critical level. Under such circumstances, there is an increasing need for new technological approaches to developing intrusion detection systems based on the latest achievements of artificial intelligence. The most important requirement for such a system consists in its operation on various unbalanced sets of attack data, which use different intrusion techniques. The synthesis of such an intrusion detection system is a difficult task due to the lack of universal methods for detecting technologically different attacks; moreover, the consistent application of known methods is unacceptably long. The aim of the present work is to eliminate such a scientific gap.Methods. Using the achievements of artificial intelligence in the fight against attacks, the authors proposed a method based on a combination of the genetic programming support vector machine (GPSVM) model using an unbalanced CICIDS2017 dataset.Results. The presented technological intrusion detection system architecture offers the possibility to train a dataset for detecting attacks on CICIDS2017 and extracting detection objects. The architecture provides for the separation of the dataset into verifiable and not verifiable elements, with the latter being added to the training set by feedback. By training the model and improving GPSVM training set, better accuracy is ensured. The operability of the new flowchart of the GPSVM model is demonstrated in terms of the entry of input data and output of data after processing using the training set of the GPSVM model. Numerical analysis based on the results of model experiments on selected quality indicators showed an increase in the accuracy of the results as compared to the known SVM method.Conclusions. Computer experiments have confirmed the methodological correctness of choosing a combination of the GPSVM model using an unbalanced CICIDS2017 dataset to increase the effectiveness of intrusion detection. A procedure for forming a training dataset based on feedback is proposed. The procedure involving the separation of datasets is shown to create conditions for improving the training of the model. The combination of the GPSVM model with an unbalanced CICIDS2017 dataset to collect a sample increases theaccuracy of intrusion detection to provide improved intrusion detection performance as compared to the SVM method.

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Uses and Challenges for Network Datasets

Cited by 15 publications

References 56 publications

Towards a Reliable Intrusion Detection Benchmark Dataset

Towards a Reliable Intrusion Detection Benchmark Dataset

A Comprehensive Overview of Artificial Intelligence-Based Classification Techniques

Genetic programming support vector machine model for a wireless intrusion detection system

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