Detection of IoT Botnet Based on Deep Learning

Liu, Junyi; Liu, Shiyue; Zhang, Sihua

doi:10.23919/chicc.2019.8866088

Cited by 32 publications

(21 citation statements)

References 5 publications

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“…They also reported 95.14% accuracy on CNN, whereas our CNN achieved 100% accuracy. The one notable difference where our models returned minimum accuracy is against the N_BaIoT dataset, where Reference [53] reported 99.57% accuracy on CNN and 96.13% on MLP, our models achieving on average 90.80% accuracy. Lastly, Reference [85] reported only 54.43% accuracy on MLP compared to our MLP classifier achieving 100% accuracy.…”

Section: Models Versus Datasets a Comparison Studymentioning

confidence: 87%

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Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

et al. 2021

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Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these limitations in previous work by analyzing various benchmark datasets and various individual and hybrid deep learning classifiers towards finding the best IDS solution for IoT that is efficient, lightweight, and comprehensive in detecting network anomalies. We also showed the model’s localized predictions and analyzed the top contributing features impacting the global performance of deep learning models. This paper aims to extract the aggregate knowledge from various datasets and classifiers and analyze the commonalities to avoid any possible bias in results and increase the trust and transparency of deep learning models. We believe this paper’s findings will help future researchers build a comprehensive IDS based on well-performing classifiers and utilize the aggregated knowledge and the minimum set of significantly contributing features.

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Section: Models Versus Datasets a Comparison Studymentioning

confidence: 87%

“…Firstly, we implemented a Multilayer Perceptron (MLP), which is a feed-forward Neural Network and consists of an input, hidden, and an output layer. It has frequently been used for network anomaly detection [11,[51][52][53][54]. Autoencoders (A.E.)…”

Section: Deep Learning Classifiers For Sequential Datamentioning

confidence: 99%

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

et al. 2021

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“…ey exploit the weight sharing properties of the CNN to classify the traffic, which makes it efficient to be deployed in resource-constrained hardware. To efficiently extract features from network traffic, authors in [29] employ damped incremental statistics as basic features. ey then use triangle area maps (TAMs)-based multivariate correlation analysis (MCA) to generate grayscale images as training data from normalized traffic features.…”

Section: Network Behaviours In Iotmentioning

confidence: 99%

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Yue

Legg

et al. 2021

Security and Communication Networks

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Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.

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“…Research in [14] produces output from detecting malware in the form of malware label and benign label. Research in [25] considers the output is in the form of benign traffic label and attack traffic label. Research in [7] produces output in the form of a normal label and attack label.…”

Section: Outputmentioning

confidence: 99%

The trend malware source of IoT network

Susanto¹,

Arifin

Stiawan

et al. 2021

IJEECS

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<span>Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.</span>

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Detection of IoT Botnet Based on Deep Learning

Cited by 32 publications

References 5 publications

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

The trend malware source of IoT network

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