Relevant bibliographies by topics / P2P Botnet Detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'P2P Botnet Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "P2P Botnet Detection"

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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 bot itself is a kind of overlay network, and research shows that the nodes with shared neighbors usually belong to a certain community. Aiming at unstructured P2P botnets and exploiting complex network theory, from the perspective of shared neighbor nodes, this article proposes a botnet detection framework called Peertrap based on self-avoiding random walks (SAW) community detection under the condition of incomplete topological information. Firstly, network traffic is converted into Netflow, by utilizing Apache Flink big data platform. Also, a P2P traffic cluster feature extraction rule is proposed for distinguishing P2P traffic from non-P2P traffic, and it is formulated by using the upstream and downstream traffic and address distribution threshold features. Then, the confidence between P2P clusters is calculated by the Jaccard coefficient to construct a shared neighbor graph, and the same type of P2P communities are mined by hierarchical clustering using SAW algorithm combined with PCA. Finally, two community attributes, mean address distribution degree and mean closeness degree, are used to distinguish botnets. Experiments are conducted on three unstructured P2P botnets datasets, Sality, Kelihos, and ZeroAccess, and the CTU classic datasets, and then good detection results can be achieved. The framework overcomes one of the most critical P2P botnet detection challenges. It can detect P2P bots with high accuracy in the presence of legitimate P2P traffic, incomplete information network topology, and C&C channel encryption. Our method embodies the typical application of complex network theory in botnet detection field, and it can detect botnets from different families in the network, with good parallelism and scalability.
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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 the most effective deep learning techniques, which is the Multi-Layer Perceptron, with certain parameter settings to detect this type of botnet, unlike most current research, which is entirely based on machine learning techniques. The reason for employing machine learning/deep learning techniques, besides data analysis, is because the bots under the same botnet have a symmetrical behavior, and that makes them recognizable compared to benign network traffic. The PeerAmbush also takes the challenge of detecting P2P botnets with fewer selected features compared to the existing related works by proposing a novel feature engineering method based on Best First Union (BFU). The proposed approach showed considerable results, with a very high detection accuracy of 99.9%, with no FPR. The experimental results showed that PeerAmbush is a promising approach, and we look forward to building on it to develop better security defenses.
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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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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 technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and F1-score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% F1 score, which outperform the previous botnet detection approaches.
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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 nodes individually. The method's final result is obtained by applying an ensemble algorithm to the results of the three graph-mining techniques. A synthetic dataset from a testbed is used to assess the method's performance. In addition, the method is evaluated using a publicly available dataset. Experimental results show that the method performs with an accuracy of 99.99%, a precision of 94.29% ,and a recall of 98.02%, which is better than existing methods.
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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 today are used for the analysis process of the network traffic. In the analysis phase, <br /> the botnet detection strategy based on the signature and DNS anomaly approach are selected to identify the behaviour and the characteristic of the botnet. In anomaly approach most of the behavioural and characteristic identification of the botnet is done by comparing between the normal and anomalous traffic. The main focus of the network analysis is studied on UDP protocol network traffic. Based on the analysis of the network traffic, <br /> the following anomalies are identified, anomalous DNS packet request, <br /> the NetBIOS attack, anomalous DNS MX query, DNS amplification attack and UDP flood attack. This study, identify significant Botnet characteristic in local network traffic for UDP network as additional approach for Botnet detection mechanism.</span>
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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 multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.
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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 sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection.
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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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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 set to detect P2P botnets. The experimental results show that this method can effectively detect P2P botnets with an accuracy of >85% when the correlation coefficient is high, even in cases with fewer commonly connected nodes.
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Dissertations / Theses on the topic "P2P Botnet Detection"

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Venkatesh, Bharath. "Fast Identification of Structured P2P Botnets Using Community Detection Algorithms." Thesis, 2013. http://etd.iisc.ernet.in/2005/3470.

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Botnets are a global problem, and effective botnet detection requires cooperation of large Internet Service Providers, allowing near global visibility of traffic that can be exploited to detect them. The global visibility comes with huge challenges, especially in the amount of data that has to be analysed. To handle such large volumes of data, a robust and effective detection method is the need of the hour and it must rely primarily on a reduced or abstracted form of data such as a graph of hosts, with the presence of an edge between two hosts if there is any data communication between them. Such an abstraction would be easy to construct and store, as very little of the packet needs to be looked at. Structured P2P command and control have been shown to be robust against targeted and random node failures, thus are ideal mechanisms for botmasters to organize and command their botnets effectively. Thus this thesis develops a scalable, efficient and robust algorithm for the detection of structured P2P botnets in large traffic graphs. It draws from the advances in the state of the art in Community Detection, which aim to partition a graph into dense communities. Popular Community Detection Algorithms with low theoretical time complexities such as Label Propagation, Infomap and Louvain Method have been implemented and compared on large LFR benchmark graphs to study their efficiency. Louvain method is found to be capable of handling graphs of millions of vertices and billions of edges. This thesis analyses the performance of this method with two objective functions, Modularity and Stability and found that neither of them are robust and general. In order to overcome the limitations of these objective functions, a third objective function proposed in the literature is considered. This objective function has previously been used in the case of Protein Interaction Networks successfully, and used in this thesis to detect structured P2P botnets for the first time. Further, the differences in the topological properties - assortativity and density, of structured P2P botnet communities and benign communities are discussed. In order to exploit these differences, a novel measure based on mean regular degree is proposed, which captures both the assortativity and the density of a graph and its properties are studied. This thesis proposes a robust and efficient algorithm that combines the use of greedy community detection and community filtering using the proposed measure mean regular degree. The proposed algorithm is tested extensively on a large number of datasets and found to be comparable in performance in most cases to an existing botnet detection algorithm called BotGrep and found to be significantly faster.
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Kai-WeiChan and 詹鎧瑋. "Study On Unsupervised Session-Based P2P Botnet Detection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/12073386091925909864.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>103<br>Decentralized or Peer-to-Peer (P2P) Botnets are difficult to recognize than traditional centralized Botnets because of intrinsic of their network topology. Most previous works on P2P Botnet detection, only focus on analyzing the attack phase. It is hard to detect P2P Botnets before their attacks because of the lack of network trace. For detecting P2P Botnets, in this paper, we proposed a session-based P2P Botnets detection system based on unsupervised machine learning with large traffic volume to obtain the suspicious behavior patterns. We believe that all P2P Botnet has its own communication patterns, and it cannot hide anymore inside long periods even using randomized noise during their talks.
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Huang, Yu-Hao, and 黃羽豪. "Conversation-based P2P botnet detection with machine learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/cdj6j9.

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YANG, SHAN-YI, and 楊善壹. "P2P Botnet Detection based on Network Behavior Similarity Evaluation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/49006421710006240805.

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碩士<br>逢甲大學<br>資訊工程學系<br>105<br>Recently, many devices are hacked and become bot. Hackers use C & C servers to control these bot. Bots usually hide their information in specific network traffic such as P2P traffic. It is not easy for network administrators to find these malicious traffics in P2P traffic. For the P2P botnet, if a bot is found and block, the hacker can issue commands from another bot and the botnet still works. In order to update the status of the entire P2P botnet, the bot master periodically sends commands to the bot. Bots also regularly download the peer list from other bots. The feature of the connect time and the packet length are very regular. Besides, the bots will connect to other bots according to the peer list, the simultaneous connections are very large. In this thesis, we propose a methodology based on the network behavior similarity. We use the machine learning algorithm to aggregate similarity flows in the same cluster and calculate the similarity of the flows for each cluster to find the suspicious cluster. In a suspicious cluster, the method uses the host connection behavior to find bot.
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Chih-HangSu and 蘇誌航. "Enhancing P2P Botnet Detection through Cross-Domain NetFlow Analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v3x73p.

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Pin-HaoChen and 陳品豪. "Study on Deep Neural Network Approach to P2P botnet detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ucsrs6.

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Wei-ChengLing and 凌偉誠. "A Visualization Framework for P2P Botnet Detection Based on Netflow Analysis." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/u64cmx.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>104<br>In recent years, the cyber-crimes become a significant issue threat everyone on Internet. There are numerous researches about botnet detection, but most of them only provide the text-based informatics that is not intuitive for humanity cognition. There are trends about leveraging modern Web technology to present a more deep insight from data itself. Using visualization on bot activities we think can help network operator to disclose more perceptions about their behaviors. We proposed a botnet visualization framework to apply malicious consequences into a perceptible representation. The visualization framework uses Node.js and HTLM5 with Jquery to construct a front-end interface. Network log and malicious behaviors are indexing and store in the Elasticsearch. Besides, we also characterize those traces to build some compendium into a pivot table to promote the query speed in user interactive. With the sustenance of several viewpoints, we expect our framework can support administrators to identify more sophisticated acumen about botnet activities.
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Ye, Jia-Siang, and 葉佳祥. "SCAP : A P2P Botnet Detection System by Analyzing Composite Traffic Characteristic." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ug9sfz.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>104<br>During the last two decades, P2P botnets have severe security threat to the contemporary information networks. Usually attackers first distribute malware to control the victim’s host and then use the host as a springboard to launch attack on the specific targets. Because the botnets become smarter than ever to avoid security detection,many researches on both centralized and decentralized botnets regarding security detection have been reported. Among them, some researchers focused on the conversation-based detection. However, the problem of composite traffic occurs frequently in these researches. In our study, we do not use ”conversation” to detect botnet but use ”payload conversation”. With the characteristic of ”payload conversation”, our system can tackle with the composite traffic problems. We then propose a new algorithm called ”Spatial Clustering of Applications without Parameter” (SCAP) to classify the traffic problems. SCAP is a nonparametric algorithm which is an improved version of K-means. SCAP can automatically cluster training data without setting any parameters. With this advantage, our system can deal with the traffic problemsin different P2P applications.
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Yu-EnChang and 張育恩. "A Clustering Algorithm with Fluctuant-Centroid Adjustment for P2P Botnet Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/25hn2k.

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Sheng-MinHsu and 徐晟旼. "A Similarity-based P2P Botnet Detection Algorithm for Inter-Domain NetFlow Analysis." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/89939394803554483764.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>104<br>Recently, peer-to-peer (P2P) botnets have been adopted for a variety of cyber-crimes. Many approaches for P2P botnet detections had studied, but most of them are based on a single domain traffic to analyze bot activities. It seems hard to recognize the malicious activities from a single domain traffic, especially for P2P botnets that often scattered across the Internet to exchange information. In this paper, we propose an innovative P2P botnet detection algorithm to federate multiple sites to inter-domain traffic analysis. Our algorithm first extracts traffic as feature vectors, and then run a cooperative graph-based algorithm across multiple domains to improve precision. We believe our P2P botnet detection can solve well-known and unknown botnets. Evaluation based on real traffic journal shows the availability of our approach, and the verification was given using VirusTotal to validate the outcomes correctness which at least 80 percentage malicious IPs appeared on it.
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Book chapters on the topic "P2P Botnet Detection"

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Han, Kyoung-Soo, and Eul Gyu Im. "A Survey on P2P Botnet Detection." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2911-7_56.

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Felix, John, Charles Joseph, and Ali A. Ghorbani. "Group Behavior Metrics for P2P Botnet Detection." In Information and Communications Security. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34129-8_9.

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Joshi, Chirag, Vishal Bharti, and Ranjeet Kumar Ranjan. "Analysis of Feature Selection Methods for P2P Botnet Detection." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6634-9_25.

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Nagaraja, Shishir. "Botyacc: Unified P2P Botnet Detection Using Behavioural Analysis and Graph Analysis." In Computer Security - ESORICS 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11212-1_25.

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Garg, Shree, Anil K. Sarje, and Sateesh Kumar Peddoju. "Improved Detection of P2P Botnets through Network Behavior Analysis." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54525-2_30.

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Qiao, Yong, Yuexiang Yang, Jie He, Bo Liu, and Yingzhi Zeng. "Detecting Parasite P2P Botnet in eMule-like Networks through Quasi-periodicity Recognition." In Information Security and Cryptology - ICISC 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31912-9_9.

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Wang, Chun-Yu, Jia-Hong Yap, Kuan-Chung Chen, Jyh-Biau Chang, and Ce-Kuen Shieh. "The Impact of the Observation Period for Detecting P2P Botnets on the Real Traffic Using BotCluster." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9190-3_8.

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"A Survey on P2P Botnets Detection." In International Conference on Computer Engineering and Technology, 3rd (ICCET 2011). ASME Press, 2011. http://dx.doi.org/10.1115/1.859735.paper110.

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Conference papers on the topic "P2P Botnet Detection"

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Thangapandiyan, M., and P. M. Rubesh Anand. "An efficient botnet detection system for P2P botnet." In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016. http://dx.doi.org/10.1109/wispnet.2016.7566330.

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Dhayal, Himanshi, and Jitender Kumar. "Botnet and P2P Botnet Detection Strategies: A Review." In 2018 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2018. http://dx.doi.org/10.1109/iccsp.2018.8524529.

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Ying, Wang. "Encrypted Botnet Detection Scheme." In 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2014. http://dx.doi.org/10.1109/3pgcic.2014.110.

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Wang, Jiajia, and Yu Chen. "P2P Botnet Detection Method Based on Data Flow." In 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/isaeece-17.2017.44.

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Barthakur, Pijush, Manoj Dahal, and Mrinal Kanti Ghose. "A Framework for P2P Botnet Detection Using SVM." In 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2012. http://dx.doi.org/10.1109/cyberc.2012.40.

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Li, Huabo, Guyu Hu, Jian Yuan, and Haiguang Lai. "P2P Botnet Detection Based on Irregular Phased Similarity." In 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2012. http://dx.doi.org/10.1109/imccc.2012.25.

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Zhao, David, and Issa Traore. "P2P Botnet Detection through Malicious Fast Flux Network Identification." In 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2012. http://dx.doi.org/10.1109/3pgcic.2012.48.

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Chang, Su, and Thomas E. Daniels. "P2P botnet detection using behavior clustering & statistical tests." In the 2nd ACM workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1654988.1654996.

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Lu, Chen, and Richard R. Brooks. "P2P hierarchical botnet traffic detection using hidden Markov models." In the 2012 Workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2379616.2379622.

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Muthumanickam, K., and E. Ilavarasan. "P2P Botnet detection: Combined host- and network-level analysis." In 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT 2012). IEEE, 2012. http://dx.doi.org/10.1109/icccnt.2012.6395940.

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