Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

Lin, Kuan‐Cheng; Chen, Sih-Yang; Hung, Jason C.

doi:10.1155/2014/986428

Cited by 38 publications

(20 citation statements)

References 24 publications

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“…The classification of whether the observed signal indicates an attack or not is produced by a neural network. In another approach, Lin et al [19], employed Artificial Fish Swarm Algorithm to produce the optimal feature set, which was then provided to a Support Vector Machine which detected botnet traffic. They reported a slight increase in accuracy when compared with Genetic Algorithms for feature selection, but produced great improvement time-wise.…”

Section: Botnets In Iotmentioning

confidence: 99%

See 1 more Smart Citation

Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques

Koroniotis

Moustafa

Sitnikova

et al. 2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The IoT is a network of interconnected everyday objects called "things" that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by a variety of different botnet activities. As botnets have been the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. This is because commercial tools mainly depend on signature-based approaches that cannot discover new forms of botnet. In literature, several studies have conducted the use of Machine Learning (ML) techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. The experimental results using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets' attacks and their tracks.

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Section: Botnets In Iotmentioning

confidence: 99%

“…[19][22][23][24][25][26] have employed Machine learning techniques, to distinguish between normal and botnet network traffic and designing Network forensic techniques and tools. In their work, Roux et al…”

mentioning

confidence: 99%

Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques

Koroniotis

Moustafa

Sitnikova

et al. 2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Yinglian Xie clustered the spam e-mails based on the URL included in the spam e-mail and detected the botnet group by checking whether the spam e-mail sender's IP was listed in several autonomous systems (AS) or a large quantity of spam e-mails were sent within a short period of time [20][21][22]. If several IPs sending the same spam e-mails in the same time slot are listed in several ASs, this method can be regarded as an effective way of detecting the botnet group considering the social engineering characteristics.…”

Section: Related Workmentioning

confidence: 99%

Detection of Malware Propagation in Sensor Node and Botnet Group Clustering Based on E-mail Spam Analysis

Lee

Cho

Park

et al. 2015

International Journal of Distributed Sensor Networks

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Cyber incidents are increasing continuously. More than 200,000 new malicious codes appear, with more than 30,000 malicious codes distributed each day on average. These cyber attacks are expanding gradually to the social infrastructure (nuclear energy, power, water, etc.) and smart sensor networks. This paper proposes a method of detecting malware propagation in sensor Node and botnet clustering automatically by analyzing e-mails. More than 80% of spam e-mails are generated by the Node infected with malicious code, using various methods to avoid filtering such as direct-to-MX, fake Received header, and open relay vulnerability. This paper proposes a scheme that detects those types accurately, including a clustering method that targets the URL included in the e-mail body, e-mail subject, attached file, and hosting server, to detect the botnet group infected with the same malicious code. The proposed method recorded about 85% zombie IP detection rate when spam e-mails distributed in a commercial environment were analyzed. When applied to the portal site that delivers 10 million e-mails, the proposed technology is expected to detect at least 150,000 zombie Nodes each day. If advanced measures are taken against the detected zombie Nodes, the spread of cyber attack damages can apparently be reduced.

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“…Currently, AFSA is widely applied in combinatorial optimization [17], and parameter optimization of neural networks [18] and SVM [19]. In [18], a radial basis function neural network (RBFNN) is adopted to train data and forecast the stock indices, where AFSA is used to optimize the parameters in RBFNN.…”

Section: Swarm Intelligence (Si) and Artificial Fish-swarm Algorithm mentioning

confidence: 99%

“…In [18], a radial basis function neural network (RBFNN) is adopted to train data and forecast the stock indices, where AFSA is used to optimize the parameters in RBFNN. In [19], a SVC model with parameters optimized by AFSA is proposed to identify the critical features determining the pattern of a botnet.…”

Section: Swarm Intelligence (Si) and Artificial Fish-swarm Algorithm mentioning

confidence: 99%

A Short-Term Prediction Model Based on Support Vector Regression Optimized by Artificial Fish-Swarm Algorithm

Wang¹,

Chen²,

Liu³

et al. 2015

IJCA

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In urban management, it is important to precisely forecast the short-term demand for necessary resources, including water, electric power, and gas. Although a variety of prediction models have been proposed in literature, the underlying defects and limitations confine the effectiveness and forecasting precision of these models. In this paper, the shortterm prediction problem is modeled as a non-linear multivariate regression problem, which is solved by support vector regression (SVR). The parameters in SVR are optimized by artificial fish-swarm algorithm (AFSA). The proposed prediction model (termed SVR-AFSA) overcomes the defects of existing prediction models, thus promoting forecasting precision. In order to verify the effectiveness and prediction precision of SVR-AFSA, this paper conducts experiments on a real dataset of two-month hourly water consumption. It also compares SVR-AFSA with two commonly adopted models, i.e., traditional BP neural network, and SVR optimized by grid method (SVR-grid). The experiments results show that SVR-AFSA outperforms these two models in prediction precision in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).

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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

Cited by 38 publications

References 24 publications

Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques

Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques

Detection of Malware Propagation in Sensor Node and Botnet Group Clustering Based on E-mail Spam Analysis

A Short-Term Prediction Model Based on Support Vector Regression Optimized by Artificial Fish-Swarm Algorithm

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