Botnet attacks represent a significant threat to cybersecurity, compromising vast numbers of devices and causing extensive harm. BOT DEFENDER is a novel collaborative defense framework designed to mitigate botnet intrusions effectively. This framework leverages the power of collective intelligence, integrating data from multiple sources to detect and respond to botnet threats in real-time. By utilizing advanced machine learning algorithms, BOT DEFENDER can identify anomalous network behavior indicative of botnet activities, allowing for swift intervention. The framework's collaborative nature ensures a comprehensive defense strategy, enhancing the resilience of individual systems through shared threat intelligence. Experimental results demonstrate that BOT DEFENDER significantly reduces the impact of botnet intrusions, showcasing its potential as a robust solution for modern cybersecurity challenges. Bot Defender, a collaborative framework that protects against botnet attacks. Bot Defender combines a proposed network traffic analyzer and machine learning technique to prevent botnet attacks. The proposed network traffic analyzer performs an in-depth traffic analysis to detect bots and filter out all the traffic from the identified bots. It significantly reduces network traffic by filtering out a huge amount of traffic from the bots and transfers significantly reduced amounts of traffic to the machine learning model for further analysis. The machine learning models such as DT, XGBOOST is powered by a novel feature selection technique, an extended dataset construction technique inspired by human learning patterns and a stacking ensemble-based machine learning model, to detect bots. This proposed work exhibits a consistent performance of the proposed machine learning model. Finally, to evaluate the performance of Bot Defender, we design and develop a live botnet attack strategy

Collaborative framework, Network traffic analyzer, Machine learning, Bot detection,
Live botnet attack strategy

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1. Newman P. The internet of things 2020: here’s what over 400 IoT decision-makers say about the future of enterprise connectivity and how IoT companies can use it to grow revenue. Bus Insider. 2020.
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9. Khan AY, Latif R, Latif S, Tahir S, Batool G, Saba T. Malicious insider attack detection in IoTs using data analytics. IEEE Access. 2020;8:11743–53. doi:10.1109/ACCESS.201.2959047
10. Ahmad Z, Khan AS, Shiang CW, Abdullah J, Ahmad F. Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol. 2020; e4150. doi:10.1002/ett.4150
11. Sarker IH, Shahriar B, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big Data. 2020;7(1). doi:10.1186/s40537-020-00318-5
12. Soe YN, Santosa PI, Hartanto R. DDoS attack detection based on simple ANN with SMOTE for IoT environment. In: 2019 Fourth International Conference on Informatics and Computing (ICIC). 2019. p. 1–5. doi:10.1109/ICIC47613.2019.8985853
13. Chaabouni N, Mosbah M, Zemmari A, Sauvignac C, Faruki P. Network intrusion detection for IoT security based on learning techniques. IEEE Commun Surv Tutor. 2019;21(3):2671–701. doi:10.1109/COMST.2019.2896380
14. García-Teodoro P, Díaz-Verdejo J, Maciá-Fernández G, Vázquez E. Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur. 2009;28(1):18–28

Botnet attacks represent a significant threat to cybersecurity, compromising vast numbers of devices and causing extensive harm. BOT DEFENDER is a novel collaborative defense framework designed to mitigate botnet intrusions effectively. This framework leverages the power of collective intelligence, integrating data from multiple sources to detect and respond to botnet threats in real-time. By utilizing advanced machine learning algorithms, BOT DEFENDER can identify anomalous network behavior indicative of botnet activities, allowing for swift intervention. The framework's collaborative nature ensures a comprehensive defense strategy, enhancing the resilience of individual systems through shared threat intelligence. Experimental results demonstrate that BOT DEFENDER significantly reduces the impact of botnet intrusions, showcasing its potential as a robust solution for modern cybersecurity challenges. Bot Defender, a collaborative framework that protects against botnet attacks. Bot Defender combines a proposed network traffic analyzer and machine learning technique to prevent botnet attacks. The proposed network traffic analyzer performs an in-depth traffic analysis to detect bots and filter out all the traffic from the identified bots. It significantly reduces network traffic by filtering out a huge amount of traffic from the bots and transfers significantly reduced amounts of traffic to the machine learning model for further analysis. The machine learning models such as DT, XGBOOST is powered by a novel feature selection technique, an extended dataset construction technique inspired by human learning patterns and a stacking ensemble-based machine learning model, to detect bots. This proposed work exhibits a consistent performance of the proposed machine learning model. Finally, to evaluate the performance of Bot Defender, we design and develop a live botnet attack strategy

Collaborative framework, Network traffic analyzer, Machine learning, Bot detection,
Live botnet attack strategy

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Newman P. The internet of things 2020: here’s what over 400 IoT decision-makers say about the future of enterprise connectivity and how IoT companies can use it to grow revenue. Bus Insider. 2020.
2. Gillum J, Kao J, Larson J. Millions of Americans’ medical images and data are available on the internet. Anyone can take a peek. ProPublica. 2019.
3. Threat landscape trends – Q1 2020. Symantec Enterprise Blogs. 2020.
4. Symantec ISTR. Internet security threat report (ISTR). Symantec Inc. 2019.
5. Osterweil E, Stavrou A, Zhang L. 20 years of DDoS: a call to action. arXiv preprint arXiv:1904.02739. 2019.
6. Saxena U, Sodhi J, Singh Y. An analysis of DDoS attacks in a smart home networks. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2020. p. 272–6. doi:10.1109/Confluence47617.2020.9058087
7. Alzahrani S, Hong L. Generation of DDoS attack dataset for effective IDS development and evaluation. J Inf Secur. 2018;9(4):225–41. doi:10.4236/jis.2018.94016
8. Anthi E, Williams L, Słowin´ska M, Theodorakopoulos G, Burnap P. A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J. 2019;6(5):9042–53. doi:10.1109/JIOT.2019.2926365
9. Khan AY, Latif R, Latif S, Tahir S, Batool G, Saba T. Malicious insider attack detection in IoTs using data analytics. IEEE Access. 2020;8:11743–53. doi:10.1109/ACCESS.201.2959047
10. Ahmad Z, Khan AS, Shiang CW, Abdullah J, Ahmad F. Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol. 2020; e4150. doi:10.1002/ett.4150
11. Sarker IH, Shahriar B, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big Data. 2020;7(1). doi:10.1186/s40537-020-00318-5
12. Soe YN, Santosa PI, Hartanto R. DDoS attack detection based on simple ANN with SMOTE for IoT environment. In: 2019 Fourth International Conference on Informatics and Computing (ICIC). 2019. p. 1–5. doi:10.1109/ICIC47613.2019.8985853
13. Chaabouni N, Mosbah M, Zemmari A, Sauvignac C, Faruki P. Network intrusion detection for IoT security based on learning techniques. IEEE Commun Surv Tutor. 2019;21(3):2671–701. doi:10.1109/COMST.2019.2896380
14. García-Teodoro P, Díaz-Verdejo J, Maciá-Fernández G, Vázquez E. Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur. 2009;28(1):18–28