Framework for Zombie Detection Using Neural Networks

Salvador, Paulo; Nogueira, António; França, Ulisses; Valadas, Rui

doi:10.1109/icimp.2009.10

Cited by 24 publications

(7 citation statements)

References 5 publications

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“…Nogueira et al 21 and Salvador et al 22 proposed a botnet detection method based on characteristic traffic patterns. They used artificial neural networks (ANNs) as classification method to distinguish between malicious and normal traffic patterns.…”

Section: Related Workmentioning

confidence: 99%

A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems

Duan

Zhou

You

et al. 2022

International Journal of Distributed Sensor Networks

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In smart systems, attackers can use botnets to launch different cyber attack activities against the Internet of Things. The traditional methods of detecting botnets commonly used machine learning algorithms, and it is difficult to detect and control botnets in a network because of unbalanced traffic data. In this article, we present a novel and highly efficient botnet detection method based on an autoencoder neural network in cooperation with decision trees on a given network. The deep flow inspection method and statistical analysis are first applied as a feature selection technique to select relevant features, which are used to characterize the communication-related behavior between network nodes. Then, the autoencoder neural network for feature selection is used to improve the efficiency of model construction. Finally, Tomek-Recursion Borderline Synthetic Minority Oversampling Technique generates additional minority samples to achieve class balance, and an improved gradient boosting decision tree algorithm is used to train and establish an abnormal traffic detection model to improve the detection of unbalanced botnet data. The results of experiments on the ISCX-botnet traffic dataset show that the proposed method achieved better botnet detection performance with 99.10% recall, 99.20% accuracy, 99.1% F1 score, and 99.0% area under the curve.

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Section: Related Workmentioning

confidence: 99%

A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems

Duan

Zhou

You

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Therefore, this research tends to look at any potential machine learning techniques to fulfill the detection system requirements. In general, machine learning has been proven by previous www.ijacsa.thesai.org researches as being able to solve issues like accuracy (Salvador et al 2009 [26]; Al-Hammadi 2010 [27]; Bilge et al 2011 [20]; Guntuku et al 2013 [22]; Ritu & Kaushal 2015 [25]) and real-time (Salvador et al 2009 [26]; Guntuku et al 2013 [22]) in Botnet detection.…”

Section: Malaysia Botnet Drone 2012-2017mentioning

confidence: 99%

A Framework for Detecting Botnet Command and Control Communication over an Encrypted Channel

Ismail¹,

Jantan²,

Najwadi³

2020

IJACSA

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Botnet employs advanced evasion techniques to avoid detection. One of the Botnet evasion techniques is by hiding their command and control communication over an encrypted channel like SSL and TLS. This paper provides a Botnet Analysis and Detection System (BADS) framework for detecting Botnet. The BADS framework has been used as a guideline to devise the methodology, and we divided this methodology into six phases: i. data collection, customization, and conversion, ii. feature extraction and feature selection, iii. Botnet prediction and classification, iv. Botnet detection, v. attack notification, and vi. testing and evaluation. We tend to use the machine learning algorithm for Botnet prediction and classification. We also found several challenges in implementing this work. This research aims to detect Botnet over an encrypted channel with high accuracy, fast detection time, and provides autonomous management to the network manager.

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“…The model outperformed several other contemporary networks such as NaiveBayes, ADTrees and Bayesian Networks, achieving an accuracy of 99.60%. Salvador et al (2009) [20] suggested a novel approach for detecting Zombie PCs using neural networks. Their approach was to analyze the network traffic using a neural network that is trained on synthetic data.…”

Section: A Deep (Feedforward) Neural Networkmentioning

confidence: 99%

Deep Learning Applications in Cyber Security: A Comprehensive Review, Challenges and Prospects

Sharma¹,

Mangrulkar²

2019

IJEAST

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Deep Learning applications are starting to pervade every commercial and technological sector. This paper surveys deep learning (DL) methods for cyber security applications, highlights the security considerations when using deep learning networks and presents possible malicious uses of such models. Initially, a brief description of the architectures studied and presented in this paper is provided. In the next section, the security applications are indicated for different models including CNNs, RNNs and GANs. Lastly, security is-sues are divided into two sections: attacks on various deep learning net-works, and attacks on a network using Deep Learning models themselves.

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Framework for Zombie Detection Using Neural Networks

Cited by 24 publications

References 5 publications

A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems

A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems

A Framework for Detecting Botnet Command and Control Communication over an Encrypted Channel

Deep Learning Applications in Cyber Security: A Comprehensive Review, Challenges and Prospects

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