Machine Learning for Network-Based Intrusion Detection Systems: An Analysis of the CIDDS-001 Dataset

Carneiro, José; Oliveira, Nuno; Sousa, Norberto; Maia, Eva; Praça, Isabel

doi:10.1007/978-3-030-86261-9_15

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…The CIDDS [29] CIDDS dataset is a collection of network traffic data sets for evaluating intrusion detection systems based on anomalies. It was created by researchers from Hochschule Coburg in Germany, and it simulates a small business environment with normal and malicious activities.…”

Section: Cidds Datasetsmentioning

confidence: 99%

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Idouglid,

Tkatek,

Elfayq

et al. 2024

IJECE

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In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

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Section: Cidds Datasetsmentioning

confidence: 99%

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Idouglid,

Tkatek,

Elfayq

et al. 2024

IJECE

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“…Currently, the technological infrastructure with high networks and systems, is collecting significant data amounts and analytics which are instrumental for critical cyber security systems [111]. Majority of threat detection systems focus on surface attacks, such as firewalls, whose protection is medial and doesn't account for lateral threats that infiltrate networks, remaining unseen [112], [113].The studies in [114], [115], [116] have established that only about 20% of threats are medial.…”

Section: Common Non-artificial Intelligence Based Techniquesmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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“…Due to its straight forward design, research in selecting the NN in K-NN is optimized till recently [31]. Because of its simple interpretation and easy implementation, K-NN is still a most used ML algorithms for jamming, intrusion, anomaly detection in different kind of networks [32][33][34][35][36]. In this paper, an efficient LSH based K-NN is developed, deployed and evaluated for jamming detection in IoT networks.…”

Section: Related Workmentioning

confidence: 99%

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

Ganeshkumar¹,

Albalawi²

2022

Computers, Materials &Amp; Continua

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Internet of things (IoT) comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life. Generally, IoT nodes are connected in wireless media and thus they are prone to jamming attacks. In the present scenario jamming detection (JD) by using machine learning (ML) algorithms grasp the attention of the researchers due to its virtuous outcome. In this research, jamming detection is modelled as a classification problem which uses several features. Using one/two or minimum number of features produces vague results that cannot be explained. Also the relationship between the feature and the class label cannot be efficiently determined, specifically, if the chosen number of features for training is minimum (say 1 or 2). To obtain good results, machine-learning algorithms are trained by large number of data sets. However, collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic. In this paper, to solve this problem, more number of features with nominal number of data's is considered that eases the data collection and the classification accuracy. In this research, an efficient technique based on locality sensitive hashing (LSH) for K-nearest neighbor algorithm (K-NN), which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated. From the results, it is clear that the obtained results are validatable and the model is more sensible.

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Machine Learning for Network-Based Intrusion Detection Systems: An Analysis of the CIDDS-001 Dataset

Cited by 12 publications

References 19 publications

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Assessment of existing cyber-attack detection models for web-based systems

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

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