botnet detection, flow-based, machine learning, independent component analysis, traffic analysis


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 be a promising approach for solving these problems because it does not rely on analyzing the network traffic payload. Other than that, to predict novel types of botnet, a detection model should be developed. This study focuses on using flow-based behavior analysis to detect novel botnets, necessary due to the difficulties of detecting existing patterns in a botnet that continues to modify the signature in concealment strategy. This study also recommends introducing Independent Component Analysis (ICA) and data pre-processing standardization to increase data quality before classification. With and without ICA implementation, we compared the percentage of significant features. Through the experiment, we found that the results produced from ICA show significant improvements.  The highest F-score was 83% for Neris bot. The average F-score for a novel botnet sample was 74%. Through the feature importance test, the feature importance increased from 22% to 27%, and the training model false positive rate also decreased from 1.8% to 1.7%.

ABSTRAK: Botnet merupakan ancaman siber yang sentiasa berevolusi. Pemilik bot sentiasa memperbaharui strategi keselamatan bagi botnet agar tidak dapat dikesan. Setiap saat, kod-kod sumber baru botnet telah dikesan dan setiap serangan dilihat menunjukkan tahap kesukaran dan ketahanan dalam mengesan bot. Model pengesanan rangkaian botnet konvensional telah menggunakan analisis berdasarkan tanda pengenalan bagi mengatasi halangan besar dalam mengesan corak botnet tersembunyi seperti teknik penyulitan dan teknik polimorfik. Masalah ini lebih bertumpu pada perubahan struktur berpusat kepada struktur bukan berpusat seperti rangkaian rakan ke rakan (P2P). Analisis tingkah laku ini seperti sesuai bagi menyelesaikan masalah-masalah tersebut kerana ianya tidak bergantung kepada analisis rangkaian beban muatan trafik. Selain itu, bagi menjangka botnet baru, model pengesanan harus dibangunkan. Kajian ini bertumpu kepada penggunaan analisa tingkah-laku berdasarkan aliran bagi mengesan botnet baru yang sukar dikesan pada corak pengenalan botnet sedia-ada yang sentiasa berubah dan menggunakan strategi tersembunyi. Kajian ini juga mencadangkan penggunakan Analisis Komponen Bebas (ICA) dan pra-pemprosesan data yang standard bagi meningkatkan kualiti data sebelum pengelasan. Peratusan ciri-ciri penting telah dibandingkan dengan dan tanpa menggunakan ICA. Dapatan kajian melalui eksperimen menunjukkan dengan penggunaan ICA, keputusan adalah jauh lebih baik. Skor F tertinggi ialah 83% bagi bot Neris. Purata skor F bagi sampel botnet baru adalah 74%. Melalui ujian kepentingan ciri, kepentingan ciri meningkat dari 22% kepada 27%, dan kadar positif model latihan palsu juga berkurangan dari 1.8% kepada 1.7%.


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Author Biographies

Wan Nurhidayah Ibrahim, Universiti Teknologi Malaysia

Wan Nur Hidayah Ibrahim (Member, IEEE) received the B.S. degree in engineering (electrical) and the master’s degree in technical education (TVET) from Universiti Teknologi Tun Hussein Onn (UTHM), in 2006 and 2008, respectively. She is currently pursuing the Ph.D. degree with Universiti Teknologi Malaysia, Skudai. Her thesis focuses on detecting botnet in network traffic. From 2009 until 2015, she was a Senior Lecturer with the Department of Electrical Engineering, Polytechnic Sultan Idris Shah, Selangor, Malaysia, where she was teaching in Information and Communication Technology, from 2015 until 2017. Her research interests include machine learning, data analytics, malware, network security and generative adversarial network (GAN).

Mohd Syahid Anuar, Universiti Teknologi Malaysia Kuala Lumpur

Syahid Anuar is currently a Senior Lecturer with Universiti Teknologi Malaysia Kuala Lumpur, under Razak Faculty of Technology and Informatics. His research interests include teaching machine learning, data mining, and cloud computing subjects. He is also as a Leader in a research project named the IoT and machine learning to detect driving behavior. He is a Team Member of research project named machine learning in cybersecurity for botnet prediction.

Ali Selamat,

LI SELAMAT is currently a Full Professor with Universiti Teknologi Malaysia (UTM), Malaysia. He has also been the Dean of the Malaysia Japan International Institute of Technology (MJIIT), UTM, since 2018. An academic institution established under the cooperation of the Japanese International Cooperation Agency (JICA) and the Ministry of Education Malaysia (MOE) to provide the Japanese style of education in Malaysia. He is also a Professor with the Software Engineering Department, Faculty of Computing, UTM. He has published more than 60 IF research papers. His h-index is 20, and his number of citations in WoS is over 800. His research interests include software engineering, software process improvement, software agents, web engineering, information retrievals, pattern recognition, genetic algorithms, neural networks, soft computing, computational collective intelligence, strategic management, key performance indicator, and knowledge management. He is on the Editorial Board of the journal Knowledge-Based Systems (Elsevier). He has been serving as the Chair of the IEEE Computer Society Malaysia, since 2018.

Ondrej Krejcar

ONDREJ KREJCAR is a full professor in systems engineering and informatics at the University of Hradec Kralove, Faculty of Informatics and Management, Center for Basic and Applied Research, Czech Republic; and Research Fellow at Malaysia-Japan International Institute of Technology, University Technology Malaysia, Kuala Lumpur, Malaysia. In 2008 he received his Ph.D. title in technical cybernetics at Technical University of Ostrava, Czech Republic. He is currently a vice-rector for science and creative activities of the University of Hradec Kralove from June 2020. At present, he is also a director of the Center for Basic and Applied Research at the University of Hradec Kralove. In years 2016-2020 he was vice-dean for science and research at Faculty of Informatics and Management, UHK. His h-index is 20, with more than 1300 citations received in the Web of Science, where more than 100 IF journal articles is indexed in JCR index. In 2018, he was the 14th top peer reviewer in Multidisciplinary in the World according to Publons and a Top Reviewer in the Global Peer Review Awards 2019 by Publons. Currently, he is on the editorial board of the MDPI Sensors IF journal (Q1/Q2 at JCR), and several other ESCI indexed journals. He is a Vice-leader and Management Committee member at WG4 at project COST CA17136, since 2018. He has also been a Management Committee member substitute at project COST CA16226 since 2017. Since 2019, he has been Chairman of the Program Committee of the KAPPA Program, Technological Agency of the Czech Republic as a regulator of the EEA/Norwegian Financial Mechanism in the Czech Republic (2019-2024). Since 2020, he has been Chairman of the Panel 1 (Computer, Physical and Chemical Sciences) of the ZETA Program, Technological Agency of the Czech Republic. Since 2014 until 2019, he has been Deputy Chairman of the Panel 7 (Processing Industry, Robotics, and Electrical Engineering) of the Epsilon Program, Technological Agency of the Czech Republic. At the University of Hradec Kralove, he is a guarantee of the doctoral study program in Applied Informatics, where he is focusing on lecturing on Smart Approaches to the Development of Information Systems and Applications in Ubiquitous Computing Environments.


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How to Cite

Ibrahim, W. N., Anuar, M. S., Selamat, A., & Krejcar, O. (2022). BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS . IIUM Engineering Journal, 23(1), 95–115.



Electrical, Computer and Communications Engineering

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