The second case used stacked models for feature extraction, including stacked autoencoder and deep belief network. The feature extraction output from the five models was fine-tuned to the fully connected layer using the BoT-IoT dataset. The results showed a good detection performance of almost 100...
There are many MOAs used for feature selection and parameter reduction in intrusion detection. Table 2 shows the comparative analysis of multi-objective techniques in the detection of intrusion. Table 2. Multi-objective optimization algorithms for Intrusion Detection. S.NoTechniqueObjectivesDataset(s)...
It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. ...
An intrusion detection system (IDS) is a security mechanism that works mainly in the network layer of an IoT system. An IDS deployed for an IoT system should be able to analyze packets of data and generate responses in real time, analyze data packets in different layers of the IoT network ...
It is widely used in the most recent studies of cybersecurity for real-world intrusion detection. This dataset also contains network traffic analysis results obtained by using CICFlowMeter. It contains 79 features for each Conclusion One of the most important technological progress over the past ...
Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic inve... I Kerrak...
So, in this paper, the Blockchain-based African Buffalo (BbAB) scheme with Recurrent Neural Network (RNN) model is proposed for detecting the intrusion by enhancing security. Furthermore, normal and malware user datasets are collected and trained in the system and the dataset is encrypted using...
For our experiments, we rely on TON-IoT, a realistic IoT network traffic dataset, associating each IP address with a single FL client. Additionally, we explore pre-training and investigate various aggregation methods to mitigate the impact of data heterogeneity. Lastly, we benchmark our approach ...
The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be...
Data availability The dataset used is publicly available.References Ge M, Fu X, Syed N, Baig Z, Teo G, Robles-Kelly A (2019) Deep learning-based intrusion detection for IoT networks. In: 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), Kyoto, Japan pp 256...