NIDS-Network Intrusion Detection System Based on Deep and Machine Learning Frameworks with CICIDS2018 using Cloud Computing

Bharati, Manisha; Tamane, Sharvari

doi:10.1109/icsidempc49020.2020.9299584

Cited by 19 publications

(7 citation statements)

References 2 publications

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“…Similarly, in [9]- [13] supervised learning of known models in the literature is used, where minor modifications in the numerical optimisation algorithms are proposed, referring only to the UNSW-NB15 dataset for the evaluation of the obtained performance. Similar arguments apply to other works in the recent literature, such as [14]- [16], in which the authors present results related to the performance of proposed methods or classical ML/AI models considering only one type of dataset such as the CSE-CIC-IDS-2018. The great limitation of the proposed techniques lies in the fact that a priori knowledge of the types of threats that can affect the computer network is required.…”

Section: B Related Worksupporting

confidence: 78%

“…It can also be stated that our results are fully comparable with the state of the art on ADS/IDS systems based on binary classifiers, which need to be trained with inputs from both classes (normal) and anomaly. In fact, [4]- [7] adopts KNN and ANN models and obtains an accuracy of 97% (only on KDD99 dataset); in [9]- [13] and [42], authors report the results of binary classifiers applied on UNSW-NB15 highlighting mean accuracy level around 95% with also some high FPR rate; in [14]- [16] and [43] it is reported a comparison of supervised machine learning (SVM, DT, DA) and deep learning (ANN, CNN, Autoencoder) models applied to CSE-CIC-IDS-2018, reveling an accuracy level close to 98%, for binary classifiers; in [35] the authors report the results of binary classifier applied on EDGE-IIOTSET 2022 using different type of machine learning (DT, RF, SVM) and deep learning (DNN) methods that provide an accuracy level of 99%. Summarizing, our method reaches a very similar level of detection performance, with low FPR respect some of Note that, in order not have dependency issues with respect to the computational platform, the SVM and ELM models have been re-implemented following the design specifications reported by the authors cited in Section A.II.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Dini

Begni

Ciavarella³

et al. 2022

IEEE Access

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This work exploits the concept of one-class classifier applied to the problem of anomaly detection in communication networks. The article presents the design of an innovative anomaly detection algorithm based on polynomial interpolation technique and statistical analysis. The innovative method is applied to datasets largely used in the scientific community for bench-marking such as KDD99, UNSW-NB15 and CSE-CIC-IDS-2018, and further evaluated with application to a novel available dataset EDGE-IIOTSET 2022. The paper also reports experimental results showing that the proposed methodology outperforms classic one-class classifiers, such as Extreme Learning Machine and Support Vector Machine models, and rule-based intrusion detection system like SNORT. With respect to binary classifiers, this work has the advantage of not requiring any a-priori knowledge about attacks and is based on the collection of only normal data traffic.

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Section: B Related Worksupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Dini

Begni

Ciavarella³

et al. 2022

IEEE Access

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“…For example, the needs of companies are changing, and advanced technologies are being developed accordingly. According to study [14], one primary future direction is using machine learning and AI techniques in NIDS to improve its ability to detect threats. Since cybersecurity attacks are becoming very common these days, these technologies can help companies monitor their security systems in real time.…”

Section: F Future Directions and Challenges For Nids In Cloud Environ...mentioning

confidence: 99%

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Ahmadi

2024

IJACSA

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This research study comprehensively analyzes network intrusion detection in cloud environments by examining several approaches. These approaches have been explored and compared to determine the optimal and appropriate choice based on specific conditions. This research study employs a qualitative approach, specifically conducting a thematic literature analysis from 2020 to 2024. The research material has been exclusively obtained via Google Scholar. The traditional approaches identified in this research include anomaly-based and signaturebased detection, along with innovative technologies and methods such as user behavior monitoring and machine learning. The findings of these studies demonstrate the effectiveness of conventional methods in known threat detection. They also struggle to identify novel attacks and understand the need for hybrid approaches that integrate the strengths of both. In this research study, the authors have addressed challenges such as privacy compliance, performance scalability, and false positives, highlighting the importance of continuous monitoring, privacypreserving technologies, and real-time threat intelligence integration. This study also highlights the importance of stakeholder buy-in and staff training for the successful implementation of a network intrusion detection system (NIDS), especially when determining the evolving nature of cyber threats. This study concludes by defining a balanced approach combining new and old methodologies to offer an effective defense against diverse cyber threats in cloud environments. The future scope of NIDS in cloud environments has also been discussed, including enhancing privacy compliance capabilities and integrating AIdriven anomaly detection to meet emerging threats and regulatory requirements.

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“…Other datasets suffer from the negative effects of low traffic variety and volumes; do not cover the range of attacks; and anonymize bundle data and payload, which cannot reflect current patterns, or they must include set and metadata. Prakash et al [29] present an experimental method for deep and machine learning networks with hyperactive boundary improvements using a convenient IDS digital dataset (CIC-CSE-IDS-2018) considering the maximum frontier attack (PCAP). Liu et al [30] develop an outlier detection algorithm that generates artificial outliers using the generative adversarial active learning (GAAL) framework.…”

Section: Common Nids Usagementioning

confidence: 99%

A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation

Wang,

Zheng,

Liu

et al. 2023

Electronics

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The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed networks because network flow data are kept private. Existing methods cannot obtain an accurate NIDS under such a federated scenario. To construct an NIDS while preserving data privacy, we propose a combined model that integrates binary classifiers into a whole network based on simple classifier networks to specify the type of attack on anomalous data and offer instruction to other security system components. We also introduce federated learning (FL) methods into our system and design a new aggregation algorithm named vertical blocking aggregation (FedVB) according to our model structure. Our experiments demonstrate that our system can be more effective than simple multi-classifiers in terms of accuracy and significantly reduce communication and computation overhead when applying FedVB.

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NIDS-Network Intrusion Detection System Based on Deep and Machine Learning Frameworks with CICIDS2018 using Cloud Computing

Cited by 19 publications

References 2 publications

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation

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