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Transfer Learning Approach for Botnet Detection Based on Recurrent Variational Autoencoder

Jeeyung Kim, Alex Sim, Jinoh Kim, Kesheng Wu, Jaegyoon Hahm
2020 Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics  
We propose transfer learning as a more effective approach for botnet detection, as it can learn from well curated source data and transfer the knowledge to a target problem domain not seen before.  ...  Tests show that the proposed transfer learning method is able to detect botnets better than semisupervised learning method that was trained on the target domain data.  ...  In our tests, we train neural network on labeled data from CTU-13 and apply the network for anomaly detection on a fresh set of network monitoring data.  ... 
doi:10.1145/3391812.3396273 dblp:conf/hpdc/KimSKWH20 fatcat:fhleeeyfvjhfffsxpgkgqro7pa

Machine Learning Based Botnet Detection in Software Defined Networks

Farhan Tariq, Shamim Baig
2017 International Journal of Security and Its Applications  
This paper proposed a flow-based approach to detect botnet by applying machine learning algorithms to software defined networks without reading packet payload.  ...  The botnet detection techniques chasing the trends of botnet started with protocol and structure dependent signature-based techniques and moves toward more sophisticated network behavioral-based approaches  ...  The increasing number of botnet attacks and their evolving nature drive the need for continuous improvement of detection techniques.  ... 
doi:10.14257/ijsia.2017.11.11.01 fatcat:2svyzondnnfnfbl45hrb4ftlxu

Fast flux botnet detection framework using adaptive dynamic evolving spiking neural network algorithm

Ahmad Al-Nawasrah, Ammar Al-Momani, Farid Meziane, Mohammad Alauthman
2018 2018 9th International Conference on Information and Communication Systems (ICICS)  
Although several methods have been suggested for detecting FFSNs, they have low detection accuracy especially with zero-day domain.  ...  A Domain Name System method known as Fast-Flux Service Network (FFSN) -a special type of botnet -has been engaged by bot herders to cover malicious botnet activities and increase the lifetime of malicious  ...  Router-based detection methods Various information extracted from network traffic to solve several network problems, generally and particularly for the fast -flux botnet problem.  ... 
doi:10.1109/iacs.2018.8355433 fatcat:g27gogbntrhltih5yzwprgnhxu

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Simon Nam Thanh Vu, Mads Stege, Peter Issam El-Habr, Jesper Bang, Nicola Dragoni
2021 Future Internet  
Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions.  ...  The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security.  ...  Machine Learning and Neural Networks for Botnet Detection Detecting botnets using machine learning and neural networks has gained prominence amongst researchers and developers.  ... 
doi:10.3390/fi13080198 fatcat:5umqenw47ncdxggi4kiotkeag4

A STUDY ON ADVANCED BOTNETS DETECTION IN VARIOUS COMPUTING SYSTEMS USING MACHINE LEARNING TECHNIQUES

K. Vamshi Krishna
2020 EPRA international journal of research & development  
Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets.  ...  KEYWORDS: Botnet, Study, Security, Internet-network, Machine Learning, Techniques.  ...  In this paper we are going to present Machine Learning Techniques, Application and Research Issues towards Botnet detection.  ... 
doi:10.36713/epra5902 fatcat:3r4d3b7qnfcdjbenezwfum2xda

Improving Botnet Detection with Recurrent Neural Network and Transfer Learning [article]

Jeeyung Kim, Alex Sim, Jinoh Kim, Kesheng Wu, Jaegyoon Hahm
2021 arXiv   pre-print
For fast-evolving botnets, it might take too long to create sufficient training samples before the botnets have changed again.  ...  Another common shortcoming of ML-based approaches is the need to retrain neural networks in order to detect the evolving botnets; however, the training process is time-consuming and requires significant  ...  [14] proposed a supervised approach to detect botnet hosts by tracking the network activities over time and extract graph-based features from NetFlow data for botnet detection.  ... 
arXiv:2104.12602v1 fatcat:cxo37mdyavhxllftslowvfktrq

Survey of Peer-to-Peer Botnets and Detection Frameworks

Ramesh Singh Rawat, Emmanuel S. Pilli, Ramesh Chandra Joshi
2018 International Journal of Network Security  
Botnet is a network of compromised computers controlled by the attacker(s) from remote locations via Command and Control (C&C) channels.  ...  These P2P botnets are continuously evolving from diverse C&C protocols using hybrid structures and are turning to be more complicated and stealthy.  ...  Host-based Detection: Methods employ system calls monitoring for abnormal activities or data taint analysis techniques for detecting the malicious operations.  ... 
dblp:journals/ijnsec/RawatPJ18 fatcat:dm7i5mh2czasvdlblrzyucowr4

Using Unsupervised Machine Learning to Detect Peer-to-Peer Botnet Flows

Andrea E. Medina Paredes, Yuan-Yuan Su, Wei Wu, Hung-Min Sun
2016 Proceedings of Engineering and Technology Innovation  
The main approach will consist of a behavior comparison among features extracted from network flows, focusing only in the flows from P2P applications including P2P botnets.  ...  In this paper we are going to focus on the behavior of Peer 2 Peer (P2P) botnets, which along with hybrid botnets is a growing trend among attackers.  ...  Introduction Malicious software such as botnets has been around for quite a t ime already and it keeps improving, evolving and growing, as for the detection systems, they try to keep track of these new  ... 
doaj:0284f7b9f1a941c199223e3e8af20ec5 fatcat:qaiiot2ptrgdfeaulo5bfbxlca

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Majda Wazzan, Daniyal Algazzawi, Omaima Bamasaq, Aiiad Albeshri, Li Cheng
2021 Applied Sciences  
IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment.  ...  This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios.  ...  Therefore, the authors gratefully acknowledge the technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia.  ... 
doi:10.3390/app11125713 fatcat:d56mns6avfhwtk4rqkwxoomoqi

Combined Forest: a New Supervised Approach for a Machine-Learning-based Botnets Detection

Christophe Maudoux, Selma Boumerdassi, Alex Barcello, Eric Renault
2021 2021 IEEE Global Communications Conference (GLOBECOM)  
Using a supervised data approach, each tree is built from a labelled dataset. In order to achieve this, we aggregate the IP-flows into Traffic-flows to extract key features and avoid over-fitting.  ...  Nowadays, botnet-based attacks are the most prevalent cyber-threats type. It is therefore essential to detect this kind of malware using efficient bots detection techniques.  ...  Future works consist in transposing our detection system to mobile network. II -MACHINE LEARNING ALGORITHMS MLAs are programs that can learn from data and improve from experience.  ... 
doi:10.1109/globecom46510.2021.9685261 fatcat:ycyrqguqxnfblkwuae7r2umxdm

A Report on Botnet Detection Techniques for Intrusion Detection Systems

Sathya D
2022 International Journal for Research in Applied Science and Engineering Technology  
The report presents a survey of various techniques of botnet detection models built using several types of machine learning techniques.  ...  The peer-to-peer attack takes place to by passing botnet attacks from one system to another in a peer-to-peer network while the command-and-control attack takes place by a botmaster attack on a server  ...  The Raw Data Layer collects network traffic data from several network devices and stores it in a central database.  ... 
doi:10.22214/ijraset.2022.44253 fatcat:iifsrkznwvbc7dt55aghv3t6li

Leveraging Image Representation of Network Traffic Data and Transfer Learning in Botnet Detection

Shayan Taheri, Milad Salem, Jiann-Shiun Yuan
2018 Big Data and Cognitive Computing  
In this work, we propose a deep learning-based engine for botnet detection to be utilized in the IoT and the wearable devices.  ...  In this system, the normal and botnet network traffic data are transformed into image before being given into a deep convolutional neural network, named DenseNet with and without considering transfer learning  ...  bidirectional long short term memory-based recurrent neural network (BLSTM-RNN) for learning from and detecting of botnets.  ... 
doi:10.3390/bdcc2040037 fatcat:mytiidz2lzfz3gf7rel4xgdbje

Botnet Detection Using Recurrent Variational Autoencoder [article]

Jeeyung Kim, Alex Sim, Jinoh Kim, Kesheng Wu
2020 arXiv   pre-print
More specifically, we propose a novel machine learning based method, named Recurrent Variational Autoencoder (RVAE), for detecting botnets through sequential characteristics of network traffic flow data  ...  In addition, we propose an approach to assign anomaly score based on probability distributions, which allows us to detect botnets in streaming mode as the new networking statistics becomes available.  ...  By bringing pre-trained Convolutional Neural Network (CNN) model which is suitable for image data, the authors do transfer learning to adapt network traffic data. B.  ... 
arXiv:2004.00234v1 fatcat:dwrwwzdydrdjtapumaz5bk7lai

Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

Ammar Almomani, Ahmad Al Nawasrah, Mohammad Alauthman, Mohammed Azmi Al Betar, Farid Meziane
2021 International Journal of Ad Hoc and Ubiquitous Computing  
This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm.  ...  Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities.  ...  It is dependent on new connection and a scheme for evolving a spiking neuron. That leads to learning new patterns obtained from the data that is arriving.  ... 
doi:10.1504/ijahuc.2021.112981 fatcat:v4zpabpvirbphcfevgbevetsni

Review of Peer-to-Peer Botnets and Detection Mechanisms [article]

Khoh Choon Hwa, Selvakumar Manickam, Mahmood A. Al-Shareeda
2022 arXiv   pre-print
The detection of P2P (Peer to Peer) botnet, which has emerged as one of the primary hazards in network cyberspace for acting as the infrastructure for several cyber-crimes, has proven more difficult than  ...  As a result, this study will explore various P2P botnet detection algorithms by outlining their essential characteristics, advantages and disadvantages, obstacles, and future research.  ...  There is no one detection method that can consistently identify evolving botnets because each detection method has its own strengths, weaknesses, and scope.  ... 
arXiv:2207.12937v1 fatcat:75b742gtvzfeleyestrrklvgb4
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