Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network

Mustafa Abdmajeed Shihab, Haydar Abdulameer Marhoon, Saadaldeen Rashid Ahmed, Ahmed Dheyaa Radhi, Ravi Sekhar

Abstract


Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.

Keywords


Adversarial Attack; Cybersecurity; Machine Learning; IoT.

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References


D. Dasgupta, Z. Akhtar, and S. Sen, “Machine learning in cybersecurity: a comprehensive survey,” The Journal of Defense Modeling and Simulation, vol. 19, no. 1, pp. 57–106, 2022.

V. Shah, “Machine learning algorithms for cybersecurity: detecting and preventing threats,” Revista Espanola de Documentacion Cientifica, vol. 15, no. 4, pp. 42–66, 2021.

B. T. Yaseen, S. Kurnaz, and S. R. Ahmed, “Detecting and classifying drug interaction using data mining techniques,” in 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 952-956, Oct. 2022.

S. R. Ahmed, A. K. Ahmed, and S. J. Jwmaa, “Analyzing the employee turnover by using decision tree algorithm,” in 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1-4, Jun. 2023.

N. Z. Mahmood, S. R. Ahmed, A. F. Al-Hayaly, S. Algburi, and J. Rasheed, “The evolution of administrative information systems: assessing the revolutionary impact of artificial intelligence,” in 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–7, 2023.

B. A. Abubaker, S. R. Ahmed, A. T. Guron, M. Fadhil, S. Algburi, and B. F. Abdulrahman, “Spiking neural network for enhanced mobile robots’ navigation control,” in 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-8, Nov. 2023.

A. K. Ahmed, S. Q. Younus, S. R. Ahmed, S. Algburi, and M. A. Fadhel, “A machine learning approach to employee performance prediction within administrative information systems,” in 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-7, Nov. 2023.

M. H. B. A. Alkareem, F. Q. Nasif, S. R. Ahmed, L. D. Miran, S. Algburi, and M. T. ALmashhadany, “Linguistics for crimes in the world by AI-based cyber security,” in 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-5, Nov. 2023.

S. R. Ahmed, I. Ahmed Najm, A. Talib Abdulqader, and K. B. Fadhil, “Energy improvement using massive MIMO for soft cell in cellular communication,” IOP Conference Series: Materials Science and Engineering, vol. 928, no. 3, p. 032009, 2020.

I. H. Sarker, “Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview,” Security and Privacy, vol. 6, no. 5, 2023.

G. Apruzzese, M. Colajanni, L. Ferretti, and M. Marchetti, “Addressing adversarial attacks against security systems based on machine learning,” in 2019 11th International Conference on Cyber Conflict (CyCon), pp. 1–18, 2019.

S. Zhou, C. Liu, D. Ye, T. Zhu, W. Zhou, and P. S. Yu, “Adversarial attacks and defenses in deep learning: from a perspective of cybersecurity,” ACM Computing Surveys, vol. 55, no. 8, pp. 1–39, 2022, doi: 10.1145/3528797.

I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” Journal of Big Data, vol. 7, pp. 1–29, 2020.

I. H. Sarker, “Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective,” SN Computer Science, vol. 2, no. 3, p. 154, 2021, doi: 10.1007/s42979-021-00379-3.

J. Martínez Torres, C. Iglesias Comesaña, and P. J. García-Nieto, “Machine learning techniques applied to cybersecurity,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 10, pp. 2823–2836, 2019, doi: 10.1007/s12652-018-01242-9.

P. Dini, A. Elhanashi, A. Begni, S. Saponara, Q. Zheng, and K. Gasmi, “Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity,” Applied Sciences, vol. 13, no. 13, p. 7507, 2023, doi: 10.3390/app13137507.

T. Berghout, M. Benbouzid, and S. M. Muyeen, “Machine learning for cybersecurity in smart grids: a comprehensive review-based study on methods, solutions, and prospects,” International Journal of Critical Infrastructure Protection, vol. 38, 2022.

I. F. Kilinçer, F. Ertam, and A. Sengur, “Machine learning methods for cyber security intrusion detection: datasets and comparative study,” Computer Networks, vol. 188, pp. 107840, 2021.

J. Lin, L. Dang, M. Rahouti, and K. Xiong, “ML attack models: adversarial attacks and data poisoning attacks,” arXiv preprint arXiv:2112.02797, 2021.

H. Y. Lin and B. Biggio, “Adversarial machine learning: attacks from laboratories to the real world,” Computer, vol. 54, no. 5, pp. 56–60, 2.

M. Usama, M. Asim, S. Latif, and J. Qadir, “Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems,” in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 78–83, 2019, doi: 10.1109/IWCMC.2019.8754126.

F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro, “Intriguing properties of adversarial ml attacks in the problem space,” in 2020 IEEE Symposium on Security and Privacy (SP), pp. 1332–1349, 2020, doi: 10.1109/SP.2020.00132.

S. G. Finlayson, J. D. Bowers, J. Ito, J. Zittrain, J. L. Beam, and I. S. Kohane, “Adversarial attacks on medical machine learning,” Science, vol. 363, no. 6433, pp. 1287–1289, 2019.

X. Wang, J. Li, X. Kuang, Y. A. Tan, and J. Li, “The security of machine learning in an adversarial setting: a survey,” Journal of Parallel and Distributed Computing, vol. 130, pp. 12–23, 2019, doi: 10.1016/j.jpdc.2018.10.015.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083, 2017.

B. D. Rouani, M. Samragh, T. Javidi, and F. Koushanfar, “Safe machine learning and defeating adversarial attacks,” IEEE Security & Privacy, vol. 17, no. 2, pp. 31–38, 2019.

M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, "Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning," in IEEE Access, vol. 10, pp. 40281-40306, 2022.

N. M. Murad et al., “Real-Time Image Denoising Using Deep Learning for Cybersecurity Applications,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 328-334, May 2024, doi: 10.1145/3660853.3660936.

G. H. A. Alshmeel, A. S. B. Al-Doori, S. R. Ahmed, Z. A. Abrahim, A. J. Ghaffoori, and A.-S. T. Hussain, “Self-Sustaining Buoy System: Harnessing Water Wave Energy for Smart, Wireless Sensing and Data Transmission,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 349-356, May 2024, doi: 10.1145/3660853.3660940.

K. A. Thamer, S. R. Ahmed, M. T. M. Almashhadany, S. G. Abdulqader, W. Abduladheem, and S. Algburi, “Secure Data Transmission in IoT Networks using Machine Learning-based Encryption Techniques,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 285-291, May 2024, doi: 10.1145/3660853.3660929

M. I. Ashour et al., “Enhancing Arabic Speaker Identification through Lip Movement Analysis and Deep Representation Learning,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 335-340, May 2024, doi: 10.1145/3660853.3660938.

S. R. Ahmed et al., “A Novel Approach to Malware Detection using Machine Learning and Image Processing,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 298-302, May 2024, doi: 10.1145/3660853.3660931.

A. Fenjan et al., “Adaptive Intrusion Detection System Using Deep Learning for Network Security,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 279-284, May 2024, doi: 10.1145/3660853.3660928.

I. A. Najm et al., “Enhanced Network Traffic Classification with Machine Learning Algorithms,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 322-327, May 2024, doi: 10.1145/3660853.3660935.

Y. T. Salih et al., “Machine Learning Approaches for Botnet Detection in Network Traffic,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 310-315, May 2024, doi: 10.1145/3660853.3660933.

L. I. Khalaf, B. Alhamadani, O. A. Ismael, A. A. Radhi, S. R. Ahmed, and S. Algburi, “Deep Learning-Based Anomaly Detection in Network Traffic for Cyber Threat Identification,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 303-309, May 2024, doi: 10.1145/3660853.3660932

A. A. Hammad, S. R. Ahmed, M. K. Abdul-Hussein, M. R. Ahmed, D. A. Majeed, and S. Algburi, “Deep Reinforcement Learning for Adaptive Cyber Defense in Network Security,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 292-297, May 2024, doi: 10.1145/3660853.3660930.

J. F. Yonan and N. A. A. Zahra, “Node Intrusion Tendency Recognition Using Network Level Features Based Deep Learning Approach,” Babylonian Journal of Networking, vol. 2023, pp. 1–10, Jan. 2023, doi: 10.58496/bjn/2023/001

M. A. Ali and A. Alqaraghuli, “A Survey on the Significance of Artificial intelligence (AI) in Network cybersecurity,” Babylonian Journal of Networking, vol. 2023, pp. 21–29, Apr. 2023, doi: 10.58496/bjn/2023/004

R. H. K. Al-Rubaye and A. K. Türkben, “Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks,” Babylonian Journal of Networking, vol. 2024, pp. 45–56, Apr. 2024, doi: 10.58496/bjn/2024/006.

M. Pawlicki, M. Choraś, and R. Kozik, “Defending network intrusion detection systems against adversarial evasion attacks,” Future Generation Computer Systems, vol. 110, pp. 148–154, 2020, doi: 10.1016/j.future.2020.03.024.

G. Apruzzese, M. Andreolini, M. Marchetti, A. Venturi, and M. Colajanni, “Deep reinforcement adversarial learning against botnet evasion attacks,” IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 1975–1987, 2020, doi: 10.1109/TNSM.2020.2979813.

S. R. Sahoo and B. B. Gupta, “Classification of various attacks and their defence mechanism in online social networks: a survey,” Enterprise Information Systems, vol. 13, no. 6, pp. 832–864, 2019, doi: 10.1080/17517575.2019.1615152.

D. Li and Q. Li, “Adversarial deep ensemble: evasion attacks and defenses for malware detection,” IEEE Transactions on Information Forensics and Security, vol. 15, no. 11, pp. 3886–3900, 2020, doi: 10.1109/TIFS.2020.2979146.

M. A. Ayub, W. A. Johnson, D. A. Talbert, and A. Siraj, “Model evasion attack on intrusion detection systems using adversarial machine learning,” in 2020 54th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6, 2020, doi: 10.1109/CISS.2020.9052690.

E. Badawi and G. V. Jourdan, “Cryptocurrencies emerging threats and defensive mechanisms: a systematic literature review,” IEEE Access, vol. 8, pp. 200021–200037, 2020, doi: 10.1109/ACCESS.2020.3037488.

M. Ghiasi, T. Niknam, Z. Wang, M. Mehrandezh, M. Dehghani, and N. Ghadimi, “A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: past, present and future,” Electric Power Systems Research, vol. 215, p. 108975, 2023, doi: 10.1016/j.epsr.2023.108975.

G. S. Nadella, H. Gonaygunta, K. Meduri, and S. Satish, “Adversarial attacks on deep neural network: developing robust models against evasion technique,” Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023, doi: 10.30537/j.issn.2519-1168.2023.0404.

N. Pitropakis, E. Panaousis, T. Giannetsos, E. Anastasiadis, and G. Loukas, “A taxonomy and survey of attacks against machine learning,” Computer Science Review, vol. 34, p. 100199, 2019, doi: 10.1016/j.cosrev.2019.100199.

P. Sharma, B. Dash, and M. F. Ansari, “Anti-phishing techniques–a review of cyber defense mechanisms,” International Journal of Advanced Research in Computer and Communication Engineering ISO, vol. 11, no. 7, pp. 3297–3307, 2022.

D. Hitaj, B. Hitaj, and L. V. Mancini, “Evasion attacks against watermarking techniques found in MLaaS systems,” in 2019 Sixth International Conference on Software Defined Systems (SDS), pp. 55–63, 2019, doi: 10.1109/SDS.2019.8769734.

Y. Jia, F. Zhong, A. Alrawais, B. Gong, and X. Cheng, “Flowguard: an intelligent edge defense mechanism against IoT DDoS attacks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9552–9562, 2020, doi: 10.1109/JIOT.2019.2955456.

M. Z. Hameed, A. György, and D. Gündüz, “The best defense is a good offense: adversarial attacks to avoid modulation detection,” IEEE Transactions on Information Forensics and Security, vol. 15, no. 9, pp. 2445–2459, 2020, doi: 10.1109/TIFS.2020.2985590.

F. Zhang, Y. Wang, S. Liu, and H. Wang, “Decision-based evasion attacks on tree ensemble classifiers,” World Wide Web, vol. 23, no. 8, pp. 2957–2977, 2020, doi: 10.1007/s11280-019-00652-8.

I. Rosenberg, A. Shabtai, Y. Elovici, and L. Rokach, “Defense methods against adversarial examples for recurrent neural networks,” arXiv preprint arXiv:1901.09963, 2019.

D. Maiorca, B. Biggio, and G. Giacinto, “Towards adversarial malware detection: Lessons learned from PDF-based attacks,” ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1–36, 2019, doi: 10.1145/3324736.

I. Rosenberg, A. Shabtai, Y. Elovici, and L. Rokach, “Adversarial machine learning attacks and defense methods in the cyber security domain,” ACM Computing Surveys (CSUR), vol. 54, no. 5, pp. 1–36, 2021, doi: 10.1145/3454411.

G. Apruzzese, M. Colajanni, L. Ferretti, and M. Marchetti, “Addressing adversarial attacks against security systems based on machine learning,” in 2019 11th International Conference on Cyber Conflict (CyCon), pp. 1–18, 2019, doi: 10.1109/CyCon.2019.8784601.

C. S. Yadav et al., “Malware analysis in IoT & android systems with defensive mechanism,” Electronics, vol. 11, no. 15, p. 2354, 2022, doi: 10.3390/electronics11152354.

H. Xu, Y. Ma, H. C. Liu, D. Deb, H. Liu, J. L. Tang, and A. K. Jain, “Adversarial attacks and defenses in images, graphs and text: A review,” International Journal of Automation and Computing, vol. 17, no. 2, pp. 151–178, 2020, doi: 10.1007/s11633-019-1207-4.

O. O. Olakanmi and A. Dada, “Wireless sensor networks (WSNs): security and privacy issues and solutions,” Wireless Mesh Networks-Security, Architectures and Protocols, pp. 1–16, 2020, doi: 10.1007/978-3-030-53035-7_1.

U. Iqbal and A. H. Mir, “Secure and practical access control mechanism for WSN with node privacy,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 3630–3646, 2022, doi: 10.1016/j.jksuci.2021.06.008.

O. O. Olakanmi, “A lightweight security and privacy-aware routing scheme for energy-constraint multi-hop wireless sensor networks,” International Journal of Information and Computer Security, vol. 15, no. 2–3, pp. 231–253, 2021, doi: 10.1504/IJICS.2021.110293.

M. A. Elsadig, A. Altigani, and M. A. Baraka, “Security issues and challenges on wireless sensor networks,” Int. J. Adv. Trends Comput. Sci. Eng, vol. 8, no. 4, pp. 1551–1559, 2019.

Z. W. Hussien, D. S. Qawasmeh, and M. Shurman, “MSCLP: Multi-sinks cluster-based location privacy protection scheme in WSNs for IoT,” in 2020 32nd International Conference on Microelectronics (ICM), pp. 1–4, 2020, doi: 10.1109/ICM48755.2020.9318780.

B. Bhushan and G. Sahoo, “Requirements, protocols, and security challenges in wireless sensor networks: an industrial perspective,” in Handbook of Computer Networks and Cyber Security: Principles and Paradigms, pp. 683–713, 2020.

X. Qi, X. Liu, J. Yu, and Q. Zhang, “A privacy data aggregation scheme for wireless sensor networks,” Procedia Computer Science, vol. 174, pp. 578–583, 2020, doi: 10.1016/j.procs.2020.09.115.

F. Mukamanzi, M. Raja, T. Koduru, and R. Datta, “Position-independent and section-based source location privacy protection in WSN,” IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 6636–6646, 2022, doi: 10.1109/TII.2022.3152999.

D. S. Ibrahim, A. F. Mahdi, and Q. M. Yas, “Challenges and issues for wireless sensor networks: a survey,” J. Glob. Sci. Res, vol. 6, no. 1, pp. 1079–1097, 2021, doi: 10.47013/jgsr.v6i1.123.

A. O. Salau, N. Marriwala, and M. Athaee, “Data security in wireless sensor networks: attacks and countermeasures,” in Mobile Radio Communications and 5G Networks: Proceedings of MRCN 2020, pp. 173–186, 2021, doi: 10.1007/978-981-16-1874-9_19.

R. Manjula, T. Koduru, and R. Datta, “Protecting source location privacy in IoT-Enabled wireless sensor networks: the case of multiple Assets,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10807–10820, 2022, doi: 10.1109/JIOT.2021.3062046.

T. M. Mengistu, T. Kim, and J. W. Lin, “A survey on heterogeneity taxonomy, security and privacy preservation in the integration of IoT, wireless sensor networks and federated learning,” Sensors, vol. 24, no. 3, p. 968, 2024, doi: 10.3390/s24030968.

F. Alshohoumi, M. Sarrab, A. AlHamadani, and D. Al-Abri, “Systematic review of existing IoT architectures security and privacy issues and concerns,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 7, 2019.

K. Ramasamy, M. H. Anisi, and A. Jindal, “E2DA: Energy efficient data aggregation and end-to-end security in 3D reconfigurable WSN,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 2, pp. 787–798, 2021, doi: 10.1109/TGCN.2020.3040870.

K. Özlem, M. K. Kuyucu, Ş. Bahtiyar, and G. İnce, “Security and privacy issues for E-textile applications,” in 2019 4th International Conference on Computer Science and Engineering (UBMK), pp. 102–107, 2019, doi: 10.1109/UBMK.2019.8873505.

E. Romero, J. Blesa, and A. Araujo, “An adaptive energy aware strategy based on game theory to add privacy in the physical layer for cognitive WSNs,” Ad Hoc Networks, vol. 92, pp. 101800, 2019, doi: 10.1016/j.adhoc.2019.101800.

G. S. Ilgi and Y. K. Ever, “Critical analysis of security and privacy challenges for the Internet of drones: a survey,” in Drones in Smart-Cities, pp. 207–214, 2020.

S. Zhong, H. Zhong, X. Huang, P. Yang, J. Shi, L. Xie, and K. Wang. Security and Privacy for Next-Generation Wireless Networks. Springer International Publishing, 2019, doi: 10.1007/978-3-030-12003-7.

S. Lata, S. Mehfuz, and S. Urooj, “Secure and reliable WSN for internet of things: challenges and enabling technologies,” IEEE Access, vol. 9, pp. 161103–161128, 2021, doi: 10.1109/ACCESS.2021.3128504.

N. Jan, A. H. Al-Bayatti, N. Alalwan, and A. I. Alzahrani, “An enhanced source location privacy based on data dissemination in wireless sensor networks (DeLP),” Sensors, vol. 19, no. 9, p. 2050, 2019, doi: 10.3390/s19092050.




DOI: https://doi.org/10.18196/jrc.v5i5.23214

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