A Botnet Detection Method on SDN using Deep Learning

Maeda, Shogo; Kanai, Atsushi; Tanimoto, Shigeaki; Hatashima, Takashi; Ohkubo, Kazuhiko

doi:10.1109/icce.2019.8662080

Cited by 38 publications

(20 citation statements)

References 11 publications

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“…Since the Software Defined Networking (SDN) solutions are now a de-facto standard, the presence of a controller with a networkwide view can be a promising feature for blocking the malicious network traffic. Several research attempts have already been made to investigate this topic: for instance, the authors of [196] presented an SDN system which dynamically modifies the network structure when malware activity is discovered, and there are also a few studies focused on the ransomware [197], [198], [199], botnets [200] as well as zero-day attacks [201] detection. However, much more research is needed.…”

Section: Attack Trends and Research Directionsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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Section: Attack Trends and Research Directionsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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“…On the other hand, classifiers based on Deep Learning (DL) were recently proposed for binary classification in botnet detection. For example, the work of Maeda et al [37] used a Deep Neural Network (DNN) to identify the botnet traffic traces. Maeda et al reported an accuracy of 99.2% in a dataset built by joining data from the CTU-13 dataset [29], the ISOT dataset [38] and self-captured not-malicious traffic.…”

Section: A Botnet Detectionmentioning

confidence: 99%

Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

Velasco-Mata¹,

González-Castro

Fidalgo

et al. 2021

IEEE Access

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Botnets are one of the online threats with the most significant presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze extensive network traffic data. In this work, we focus on increasing the performance of botnet traffic classification by selecting those features that further increase the detection rate. For this purpose, we use two feature selection techniques, i.e., Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets and three models, i.e., Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models, we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. Finally, we measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set, which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.

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“…The proposed technique achieved 98% accuracy having a 0.02 false alarm rate and for evaluation purpose, it uses the Kitsune dataset. The authors also proposed a technique to prevent the detection of botnet after infecting the machine in SDN [14]. For normal traffic, ISOT dataset and for botnet, CTU-13 are used.…”

Section: A Organizationmentioning

confidence: 99%

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

Qureshi

Tunio

et al. 2021

IEEE Access

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The astonishing growth of sophisticated ever-evolving cyber threats and attacks throws the entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the infrastructure of interconnected devices, it brings along significant security challenges. Cyber threat analysis is an augmentation of a network security infrastructure that primarily emphasizes on detection and prevention of sophisticated network-based threats and attacks. Moreover, it requires the security of network by investigation and classification of malicious activities. In this study, we propose a DL-enabled malware detection scheme using a hybrid technique based on the combination of a Deep Neural Network(DNN) and Long Short-Term Memory(LSTM) for the efficient identification of multi-class malware families in IoT infrastructure. The proposed scheme utilizes latest 2018 dataset named as N_BaIoT. Furthermore, our proposed scheme is evaluated using standard performance metrics such as accuracy, recall, precision, F1score, and so forth. The DL-based malware detection system achieves 99.96% detection accuracy for IoT based threats. Finally, we also compare our proposed work with other robust and state-of-the-art detection schemes.

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A Botnet Detection Method on SDN using Deep Learning

Cited by 38 publications

References 11 publications

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

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