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Journal articles on the topic 'P2P Botnet Detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Xing, Ying, Hui Shu, Fei Kang, and Hao Zhao. "Peertrap: An Unstructured P2P Botnet Detection Framework Based on SAW Community Discovery." Wireless Communications and Mobile Computing 2022 (February 8, 2022): 1–18. http://dx.doi.org/10.1155/2022/9900396.

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Botnet has become one of the serious threats to the Internet ecosystem, and botnet detection is crucial for tracking and mitigating network threats on the Internet. In the evolution of emerging botnets, peer-to-peer (P2P) botnets are more dangerous and resistant because of their distributed characteristics. Among them, unstructured P2P botnets use custom protocols for communication, which can be integrated with legitimate P2P traffic. Moreover, their topological structure is more complex, and a complete topology cannot be obtained easily, making them more concealed and difficult to detect. The

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2

Kabla, Arkan Hammoodi Hasan, Achmad Husni Thamrin, Mohammed Anbar, Selvakumar Manickam, and Shankar Karuppayah. "PeerAmbush: Multi-Layer Perceptron to Detect Peer-to-Peer Botnet." Symmetry 14, no. 12 (2022): 2483. http://dx.doi.org/10.3390/sym14122483.

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Due to emerging internet technologies that mostly depend on the decentralization concept, such as cryptocurrencies, cyber attackers also use the decentralization concept to develop P2P botnets. P2P botnets are considered one of the most serious and challenging threats to internet infrastructure security. Consequently, several open issues still need to be addressed, such as improving botnet intrusion detection systems, because botnet detection is essentially a confrontational problem. This paper presents PeerAmbush, a novel approach for detecting P2P botnets using, for the first time, one of th

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3

Zhang, You Lin. "Classification of Botnets and Botnet Defense Techniques." Applied Mechanics and Materials 373-375 (August 2013): 1665–69. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1665.

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As an effective platform for networking attacking, the botnet brings the most serious threats. In this paper, botnets are categorized into three classes based on network structure. They are centralized botnet, distributed (P2P) bornet and hybrid botnet. This paper divides botnet defense techniques into three fields: detection, measurement and restraint. It analyzes each field in detail, and discusses that which defense technique is suitable for what kind of botnet.

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4

Baruah, Sangita, Dhruba Jyoti Borah, and Vaskar Deka. "Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm." International Journal of Information Security and Privacy 17, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijisp.319303.

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Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning techn

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Borah, Dhruba Jyoti, and Abhijit Sarma. "Detection of Peer-to-Peer Botnets using Graph Mining." International journal of Computer Networks & Communications 15, no. 2 (2023): 105–25. http://dx.doi.org/10.5121/ijcnc.2023.15206.

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Peer-to-Peer (P2P) botnets are significant threats to the Internet. The botnet traffic is increasing rapidly every year and impacts the entire Internet. A P2P botnet is responsible for launching various malicious activities such as DDoS attacks, click fraud attacks, stealing confidential information from bank and government websites, etc. It is challenging to detect P2P botnets because of their high resiliency against detection. This paper proposes a method that uses a network communication graph from network flow data to detect botnets. Three graph-mining techniques are used to detect bot nod

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Safar, Noor Zuraidin Mohd, Noryusliza Abdullah, Hazalila Kamaludin, Suhaimi Abd Ishak, and Mohd Rizal Mohd Isa. "Characterising and detection of botnet in P2P network for UDP protocol." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1584. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1584-1595.

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<span>Developments in computer networking have raised concerns of the associated Botnets threat to the Internet security. Botnet is an inter-connected computers or nodes that infected with malicious software and being controlled as a group without any permission of the computer’s owner. <br /> This paper explores how network traffic characterising can be used for identification of botnet at local networks. To analyse the characteristic, behaviour or pattern of the botnet in the network traffic, a proper network analysing tools is needed. Several network analysis tools available tod

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7

Khan, Riaz Ullah, Xiaosong Zhang, Rajesh Kumar, Abubakar Sharif, Noorbakhsh Amiri Golilarz, and Mamoun Alazab. "An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers." Applied Sciences 9, no. 11 (2019): 2375. http://dx.doi.org/10.3390/app9112375.

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In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective m

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8

Yang, Zhixian, and Buhong Wang. "A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept." Symmetry 11, no. 3 (2019): 326. http://dx.doi.org/10.3390/sym11030326.

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A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sor

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9

Rivière, Lionel, and Sven Dietrich. "Experiments with P2P Botnet Detection." it - Information Technology 54, no. 2 (2012): 90–95. http://dx.doi.org/10.1524/itit.2012.0668.

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10

Yang, Zhixian, and Buhong Wang. "P2P Botnet Detection Based on Nodes Correlation by the Mahalanobis Distance." Information 10, no. 5 (2019): 160. http://dx.doi.org/10.3390/info10050160.

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Botnets are a common and serious threat to the Internet. The search for the infected nodes of a P2P botnet is affected by the number of commonly connected nodes, with a lower detection accuracy rate for cases with fewer commonly connected nodes. However, this paper calculates the Mahalanobis distance—which can express correlations between data—between indirectly connected nodes through traffic with commonly connected nodes, and establishes a relationship evaluation model among nodes. An iterative algorithm is used to obtain the correlation coefficient between the nodes, and the threshold is se

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11

Gao, Jian, Kang Feng Zheng, Yi Xian Yang, and Xin Xin Niu. "Research of Key Nodes of Botnet Based on P2P." Applied Mechanics and Materials 88-89 (August 2011): 386–90. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.386.

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The paper applies the segmentation of peer-to- peer network to the defense process of P2P-based botnet, in order to cause the greatest damage on the P2P network. A lot of papers have been researching how to find the key nodes in P2P networks. To solve this problem, this paper proposes distributed detection algorithm NEI and centralized detection algorithm COR for detecting cut vertex, NEI algorithm not only apply to detect cut vertex of directed graph but also to the undirected graph. COR algorithm can reduce the additional communication. Then, this paper carries out simulation on P2P botnet,

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12

Almutairi, Suzan, Saoucene Mahfoudh, Sultan Almutairi, and Jalal S. Alowibdi. "Hybrid Botnet Detection Based on Host and Network Analysis." Journal of Computer Networks and Communications 2020 (January 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/9024726.

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Botnet is one of the most dangerous cyber-security issues. The botnet infects unprotected machines and keeps track of the communication with the command and control server to send and receive malicious commands. The attacker uses botnet to initiate dangerous attacks such as DDoS, fishing, data stealing, and spamming. The size of the botnet is usually very large, and millions of infected hosts may belong to it. In this paper, we addressed the problem of botnet detection based on network’s flows records and activities in the host. Thus, we propose a general technique capable of detecting new bot

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13

Yin, Chunyong. "Towards Accurate Node-Based Detection of P2P Botnets." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/425491.

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Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node’s flows and extract the useful features over a given time period. We have tested our approach on real-life data sets and achieved detection rates of 99-100% and low false positives rates of 0–2%. Comparison with other similar approaches on the same data sets shows that our approach outperforms the existing approa

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14

N., Mohini. "Review on Botnet Threat Detection in P2P." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 2 (2015): 753–56. http://dx.doi.org/10.17762/ijritcc2321-8169.150266.

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15

He, Jie, Yuexiang Yang, Xiaolei Wang, and Zhiguo Tan. "Adaptive traffic sampling for P2P botnet detection." International Journal of Network Management 27, no. 5 (2017): e1992. http://dx.doi.org/10.1002/nem.1992.

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16

Li, Yue, Ya Qin Fan, Duo Yang, and Kai Yuan Zheng. "Research of Botnet Base on P2P Protocol." Advanced Materials Research 860-863 (December 2013): 2758–61. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2758.

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Because the model of Botnet posed a threat to the network security, so this paper studies the semi distributed P2P network. Base on this, we simulate the propagation model of semi distributed P2P network and obtained a more conform to the actual status's new communication model. Through the analysis of the result, we prove the effectiveness of the honeypot detection method and flow detection method. Pseudo honeypot" detection technique model is based on the first two detection, we also simulate it and get a desired result. The conclusion has important significance for the study of network secu

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17

Sun, Ru Xia, and Chun Yong Yin. "P2P Traffic Identification Algorithm Based on Topology." Advanced Materials Research 487 (March 2012): 297–300. http://dx.doi.org/10.4028/www.scientific.net/amr.487.297.

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The botnet consists of some computers controlled by an attacker and has become a major threat to the internet and users. Because the p2p botnet is a distributed network, making the identification of p2p bots is very difficult. In response to this threat, we present a p2p identification algorithm based on topology. This method only depends on three network behavior features. Our approach has a high detection rate and an acceptable low false alarm rate.

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18

Al-Nawasrah, Ahmad, Ammar Ali Almomani, Samer Atawneh, and Mohammad Alauthman. "A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing." International Journal of Cloud Applications and Computing 10, no. 3 (2020): 17–53. http://dx.doi.org/10.4018/ijcac.2020070102.

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A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increas

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19

Ibrahim, Wan Nurhidayah, Mohd Syahid Anuar, Ali Selamat, and Ondrej Krejcar. "BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS." IIUM Engineering Journal 23, no. 1 (2022): 95–115. http://dx.doi.org/10.31436/iiumej.v23i1.1789.

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Botnet is a significant cyber threat that continues to evolve. Botmasters continue to improve the security framework strategy for botnets to go undetected. Newer botnet source code runs attack detection every second, and each attack demonstrates the difficulty and robustness of monitoring the botnet. In the conventional network botnet detection model that uses signature-analysis, the patterns of a botnet concealment strategy such as encryption & polymorphic and the shift in structure from centralized to decentralized peer-to-peer structure, generate challenges. Behavior analysis seems to b

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20

Kaur, Navjot, and Sunny Behal. "P2P-BDS: Peer-2-Peer Botnet Detection System." IOSR Journal of Computer Engineering 16, no. 5 (2014): 28–33. http://dx.doi.org/10.9790/0661-16552833.

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21

LIU, Dan, Yi-chao LI, and Yue HU. "P2P-Botnet detection based on multi-stage filtration." Journal of Computer Applications 30, no. 12 (2011): 3354–56. http://dx.doi.org/10.3724/sp.j.1087.2010.03354.

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22

Obeidat, Atef Ahmed, Majd Mahmoud Al-Kofahi, Mohammad Jazi Bawaneh, and Essam Said Hanandeh. "A Novel Botnet Detection System for P2P Networks." Journal of Computer Science 13, no. 8 (2017): 329–36. http://dx.doi.org/10.3844/jcssp.2017.329.336.

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23

Zhang, Junjie, Roberto Perdisci, Wenke Lee, Xiapu Luo, and Unum Sarfraz. "Building a Scalable System for Stealthy P2P-Botnet Detection." IEEE Transactions on Information Forensics and Security 9, no. 1 (2014): 27–38. http://dx.doi.org/10.1109/tifs.2013.2290197.

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24

Huseynov, Khalid, Paul D. Yoo, and Kwangjo Kim. "Scalable P2P Botnet Detection with Threshold Setting in Hadoop Framework." Journal of the Korea Institute of Information Security and Cryptology 25, no. 4 (2015): 807–16. http://dx.doi.org/10.13089/jkiisc.2015.25.4.807.

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25

Fan, Yuhui, and Ning Xu. "A P2P Botnet Detection Method Used On-line Monitoring and Off-line Detection." International Journal of Security and Its Applications 8, no. 3 (2014): 87–96. http://dx.doi.org/10.14257/ijsia.2014.8.3.10.

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26

Tarng, Wernhuar. "A P2P Botnet Virus Detection System Based on Data-Mining Algorithms." International Journal of Computer Science and Information Technology 4, no. 5 (2012): 51–65. http://dx.doi.org/10.5121/ijcsit.2012.4505.

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27

Alauthaman, Mohammad, Nauman Aslam, Li Zhang, Rafe Alasem, and M. A. Hossain. "A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks." Neural Computing and Applications 29, no. 11 (2016): 991–1004. http://dx.doi.org/10.1007/s00521-016-2564-5.

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28

LIU, Jian-bo. "The Detection of Intrusion Through P2P Botnet Based on the Analysis of Successful Connection Rate and Average Packet." International Journal of Engineering and Manufacturing 2, no. 1 (2012): 22–26. http://dx.doi.org/10.5815/ijem.2012.01.04.

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29

Sun, Lei, Wei Du, and Na Che. "Data Center Protection Problems in the Zombie Network." Applied Mechanics and Materials 727-728 (January 2015): 948–50. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.948.

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With the development of the Botnet, new botnets use the peer-to-peer (P2P) protocol (such as for eMule download) appear, which have brought great challenges in detecting and preventing of botnet in the data center.27100Point to point protocol uses more decentralized control method, so information between each node can be shared, and each node has the function of connecting and recovering, which leads to that the class of Botnet is hard to be closed. In the protection of Botnet the data center also found that there are more and more botnet uses the high strength encryption technology after impl

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30

Su, Shang-Chiuan, Yi-Ren Chen, Shi-Chun Tsai, and Yi-Bing Lin. "Detecting P2P Botnet in Software Defined Networks." Security and Communication Networks 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/4723862.

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Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches th

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31

Rodríguez-Gómez, Rafael A., Gabriel Maciá-Fernández, Pedro García-Teodoro, Moritz Steiner, and Davide Balzarotti. "Resource monitoring for the detection of parasite P2P botnets." Computer Networks 70 (September 2014): 302–11. http://dx.doi.org/10.1016/j.comnet.2014.05.016.

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32

AsSadhan, Basil, Abdulmuneem Bashaiwth, Jalal Al-Muhtadi, and Saleh Alshebeili. "Analysis of P2P, IRC and HTTP traffic for botnets detection." Peer-to-Peer Networking and Applications 11, no. 5 (2017): 848–61. http://dx.doi.org/10.1007/s12083-017-0586-0.

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33

Huang, Zhiyong, Xiaoping Zeng, and Yong Liu. "Detecting and blocking P2P botnets through contact tracing chains." International Journal of Internet Protocol Technology 5, no. 1/2 (2010): 44. http://dx.doi.org/10.1504/ijipt.2010.032614.

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34

Ahmed Al-Azzawi, Nemir, and Shatha Mizhir Hasan. "Detection of P2P Botnets Based on Support Vector Machine: Case Study." Engineering and Technology Journal 32, no. 5 (2014): 1227–39. http://dx.doi.org/10.30684/etj.32.5a.12.

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35

Song, Yuanzhang. "Detecting P2P botnet by analyzing macroscopic characteristics with fractal and information fusion." China Communications 12, no. 2 (2015): 107–17. http://dx.doi.org/10.1109/cc.2015.7084406.

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36

Borah, Dhruba Jyoti, and Abhijit Sarma. "Cyber pirating and Detection of malicious activities p2p botnets using Markov cluster algorithm." International Journal of Electronics and Applied Research 4, no. 1 (2017): 24–36. http://dx.doi.org/10.33665/ijear.2017.v04i01.001.

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37

Jiang, Hongling, and Xiuli Shao. "Detecting P2P botnets by discovering flow dependency in C&C traffic." Peer-to-Peer Networking and Applications 7, no. 4 (2012): 320–31. http://dx.doi.org/10.1007/s12083-012-0150-x.

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38

Barthakur, Pijush, Manoj Dahal, and Mrinal Kanti Ghose. "An Efficient Machine Learning Based Classification Scheme for Detecting Distributed Command & Control Traffic of P2P Botnets." International Journal of Modern Education and Computer Science 5, no. 10 (2013): 9–18. http://dx.doi.org/10.5815/ijmecs.2013.10.02.

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39

Wan Yusuf, Wan Ahmad Ramzi, and Faizal M. A, Rudy Fadhlee M. D, Nur Hidayah M. S. "Revealing Influenced Selected Feature for P2P Botnet Detection." International Journal of Communication Networks and Information Security (IJCNIS) 9, no. 3 (2022). http://dx.doi.org/10.17762/ijcnis.v9i3.2927.

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P2P botnet has become a serious security threat for computer networking systems. Botnet attack causes a great financial loss and badly impact the information and communication technology (ICT) system. Current botnet detection mechanisms have limitations and flaws to deal with P2P botnets which famously known for their complexity and scalable attack. Studies show that botnets behavior can be detected based on several detection features. However, some of the feature parameters may not represent botnet behavior and may lead to higher false alarm detection rate. In this paper, we reveal selected f

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40

K P, Pavana, Rohith Adiga H R, Shubha M L, Vinayaka Patil K G, and Mohan H G. "Botnet Detection Based on Machine Learning Techniques in P2P Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 04 (2023). http://dx.doi.org/10.55041/ijsrem18697.

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A botnet is a network of computers that are controlled from a botmaster or a command and-control server. Botnet is a major threat on the internet. P2P botnet is a representative of P2P malicious programs. Botmaster gives a command and control (C&C) information via a unique communication channel. It remotely controls the bots that are compromised to initiate malicious activities like distributed denial of service (DDoS) attack, spamming, phishing, and sensitive information stealing. The approaches using Machine learning are used in botnet detection. They are useful to extract unexpected pat

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41

"Building a Scalable System for Stealthy P2P-Botnet Detection." International Journal of Modern Trends in Engineering & Research 4, no. 5 (2017): 168–74. http://dx.doi.org/10.21884/ijmter.2017.4170.ssujw.

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Xu, Lei, XiaoLong Xu, and Yue Zhuo. "P2P Botnet Detection Using Min-Vertex Cover." Journal of Networks 7, no. 8 (2012). http://dx.doi.org/10.4304/jnw.7.8.1176-1181.

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43

"Identifying Botnets: Classification and Detection." International Journal of Innovative Technology and Exploring Engineering 8, no. 9S (2019): 131–37. http://dx.doi.org/10.35940/ijitee.i1021.0789s19.

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The past few years have witnessed the threats caused by the evolving of botnets. It has been found that the nefarious network consisting of contagious systems called as bots are operated by the botmaster. These botnets have been used for malicious activities. This prevailing threat on the internet has led to spam, Distributed Denial of Service (DDoS) attacks, phishing emails, and other cyber-attacks. The detection of such networks is very important keeping the protocols and features they work upon. The paper talks about the various detection techniques that can be adapted to evade the attacks

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Wang, Binbin, Zhitang Li, Dong Li, Hao Chen, Feng Liu, and ZhengBin Hu. "The Aggregation and Stability Analysis of Network Traffic for Structured-P2P-based Botnet Detection." Journal of Networks 5, no. 5 (2010). http://dx.doi.org/10.4304/jnw.5.5.517-526.

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45

Zeidanloo, Hossein Rouhani, Farhoud Hosseinpour, and Farhood Farid Etemad. "New Approach for Detection of IRC and P2P Botnets." International Journal of Computer and Electrical Engineering, 2010, 1029–38. http://dx.doi.org/10.7763/ijcee.2010.v2.271.

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46

Syahirah, Raihana, Faizal M., and Zul Azri. "Multivariate Statistical Analysis on Anomaly P2P Botnets Detection." International Journal of Advanced Computer Science and Applications 8, no. 12 (2017). http://dx.doi.org/10.14569/ijacsa.2017.081259.

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Syahirah, Raihana, Faizal M.A., Zul Azri, and Nurulhuda Ahmad. "Automated Simulation P2P Botnets Signature Detection by Rule-based Approach." International Journal of Advanced Computer Science and Applications 7, no. 8 (2016). http://dx.doi.org/10.14569/ijacsa.2016.070819.

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48

Choi, Seung-hwan. "Detecting Members of P2P Botnets Using Probabilistic Dye-Pumping Algorithm." Korea Institute of Information Technology Review 11, no. 5 (2013). http://dx.doi.org/10.14801/kiitr.2013.11.5.85.

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