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From Passive Defense to Proactive Defence: Strategies and Technologies

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Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14509))

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Abstract

The goal of network defense mechanisms is to enable systems to actively detect and withstand attacks, reduce reliance on external security measures, and quickly recover and repair. This paper elaborates on relevant works from both passive defense and proactive defense perspectives. Our first contribution is to introduce strategies and technologies related to passive defense, discussing in detail access control strategies, identity authentication technologies, and firewall technologies. These technologies play a significant role in protecting computer systems and networks from unauthorized access and malicious activities. Addressing the limitations of passive defense, such as: difficult to resolve uncertainty attacks and passive self-defense, our second contribution is to introduce strategies and technologies related to proactive defense. Firstly, we provide a comparative introduction to moving target strategies, intrusion tolerance strategies, and mimic defense strategies. Secondly, based on the mimic defense strategy, we provide a detailed introduction to mimic routers and mimic server technologies, which simulate normal network traffic and service behavior to enhance system security. Moreover, we provide future prospects and suggest potential directions. These approaches can help protect computer systems and networks from various security threats and provide valuable insights for researchers and security professionals on how to address evolving threats.

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References

  1. Mijwil, M., et al.: Cybersecurity challenges in smart cities: an overview and future prospects. Mesop. J. Cybersecur. 2022, 1–4 (2022)

    Google Scholar 

  2. Sahana, Y.P., Gotkhindikar, A., Tiwari, S.K.: Survey on can-bus packet filtering firewall. In: 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE (2022)

    Google Scholar 

  3. Sreelaja, N.K.: A fireworks-based approach for efficient packet filtering in firewall. In: Handbook of Research on Fireworks Algorithms and Swarm Intelligence. IGI Global, pp. 315–333 (2020)

    Google Scholar 

  4. Durante, L., Seno, L., Valenzano, A.: A formal model and technique to redistribute the packet filtering load in multiple firewall networks. IEEE Trans. Inf. Forensics Secur. 16, 2637–2651 (2021)

    Article  Google Scholar 

  5. Malikovich, K.M., Rajaboevich, G.S., Karamatovich, Y.B.: Method of constructing packet filtering rules. In: 2019 International Conference on Information Science and Communications Technologies (ICISCT). IEEE (2019)

    Google Scholar 

  6. Ari Muzakir, A.: Analisis Kinerja Packet Filtering Berbasis Mikrotik Routerboard Pada Sistem Keamanan Jaringan. Analisis Kinerja Packet Filtering Berbasis Mikrotik Routerboard pada Sistem Keamanan Jaringan (2022)

    Google Scholar 

  7. Liang, J., Kim, Y.: Evolution of firewalls: toward securer network using next generation firewall. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE (2022)

    Google Scholar 

  8. Jingyao, S., Chandel, S., Yunnan, Yu., Jingji, Z., Zhipeng, Z.: Securing a network: how effective using firewalls and VPNs are? In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 70, pp. 1050–1068. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12385-7_71

    Chapter  Google Scholar 

  9. Muzaki, R.A., et al.: Improving security of web-based application using ModSecurity and reverse proxy in web application firewall. In: 2020 International Workshop on Big Data and Information Security (IWBIS). IEEE (2020)

    Google Scholar 

  10. Yina, Q.: Discussion on computer network security technology and firewall technology. Int. J. New Dev. Eng. Soc. 6(4), 1–5 (2022)

    Google Scholar 

  11. Amouei, M., Rezvani, M., Fateh, M.: RAT: reinforcement-learning-driven and adaptive testing for vulnerability discovery in web application firewalls. IEEE Trans. Dependable Secure Comput. 19(5), 3371–3386 (2021)

    Article  Google Scholar 

  12. Praise, J., Jeya, R., Raj, J.S., Bibal Benifa, J.V.: Development of reinforcement learning and pattern matching (RLPM) based firewall for secured cloud infrastructure. Wirel. Personal Commun. 115, 993–1018 (2020)

    Article  Google Scholar 

  13. Bagheri, S., Shameli-Sendi, A.: Dynamic firewall decomposition and composition in the cloud. IEEE Trans. Inf. Forensics Secur. 15, 3526–3539 (2020)

    Article  Google Scholar 

  14. Chebrolu, C.S., Chung-Horng, L., Ajila, S.A.: Dynamic packet filtering using machine learning. In: 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE (2022)

    Google Scholar 

  15. Kailanya, E., Mwadulo, M., Omamo, A.: Dynamic deep stateful firewall packet analysis model. Afr. J. Sci. Technol. Soc. Sci. 1(2), 116–123 (2022)

    Google Scholar 

  16. Malikovich, K.M., Rajaboevich, G.S., Karamatovich, Y.B.: Method of constructing packet filtering rules. In: 2019 International Conference on Information Science and Communications Technologies (ICISCT). IEEE (2019)

    Google Scholar 

  17. Sandhu, R., Munawer, Q.: How to do discretionary access control using roles. In: Proceedings of the Third ACM Workshop on Role-Based Access Control (1998)

    Google Scholar 

  18. Dranger, S., Sloan, R.H., Solworth, J.A.: The complexity of discretionary access control. In: Yoshiura, H., Sakurai, K., Rannenberg, K., Murayama, Y., Kawamura, S. (eds.) IWSEC 2006. LNCS, vol. 4266, pp. 405–420. Springer, Heidelberg (2006). https://doi.org/10.1007/11908739_29

    Chapter  Google Scholar 

  19. Solworth, J.A., Sloan, R.H.: A layered design of discretionary access controls with decidable safety properties. In: Proceedings of IEEE Symposium on Security and Privacy, 2004. IEEE (2004)

    Google Scholar 

  20. Vijayalakshmi, K., Jayalakshmi, V.: A study on current research and challenges in attribute-based access control model. Intell. Data Commun. Technol. Internet Things Proc. ICICI 2022, 17–31 (2021)

    Google Scholar 

  21. Aftab, M.U., et al.: Traditional and hybrid access control models: a detailed survey. Secur. Commun. Netw. 2022, 1–5 (2022)

    Article  Google Scholar 

  22. Gihleb, R., Giuntella, O., Zhang, N.: The effect of mandatory-access prescription drug monitoring programs on foster care admissions. J. Human Resourc. 57(1), 217–240 (2022)

    Article  Google Scholar 

  23. Namane, S., Dhaou, I.B.: Blockchain-based access control techniques for IoT applications. Electronics 11(14), 2225 (2022)

    Article  Google Scholar 

  24. Fragkos, G., Johnson, J., Tsiropoulou, E.E.: Dynamic role-based access control policy for smart grid applications: an offline deep reinforcement learning approach. IEEE Trans. Human-Mach. Syst. 52(4), 761–773 (2022)

    Article  Google Scholar 

  25. Ameer, S., Benson, J., Sandhu, R.: An attribute-based approach toward a secured smart-home IoT access control and a comparison with a role-based approach. Information 13(2), 60 (2022)

    Article  Google Scholar 

  26. Kormpakis, G., et al.: An advanced visualisation engine with role-based access control for building energy visual analytics. In: 2022 13th International Conference on Information, Intelligence, Systems Applications (IISA). IEEE (2022)

    Google Scholar 

  27. Ghazal, R., et al.: Intelligent role-based access control model and framework using semantic business roles in multi-domain environments. IEEE Access 8, 12253–12267 (2020)

    Article  Google Scholar 

  28. Alshammari, S.T., Albeshri, A., Alsubhi, K.: Integrating a high-reliability multicriteria trust evaluation model with task role-based access control for cloud services. Symmetry 13(3), 492 (2021)

    Article  Google Scholar 

  29. Ding, S., et al.: A novel attribute-based access control scheme using blockchain for IoT. IEEE Access 7, 38431–38441 (2019)

    Article  Google Scholar 

  30. Bhatt, S., et al.: Attribute-based access control for AWS internet of things and secure industries of the future. IEEE Access 9, 107200–107223 (2021)

    Article  Google Scholar 

  31. Aghili, S.F., et al.: MLS-ABAC: efficient multi-level security attribute-based access control scheme. Future Gener. Comput. Syst. 131, 75–90 (2022)

    Article  Google Scholar 

  32. Guo, H., Meamari, E., Shen, C.-C.: Multi-authority attribute-based access control with smart contract. In: Proceedings of the 2019 International Conference on Blockchain Technology (2019)

    Google Scholar 

  33. Zhong, H., et al.: An efficient and outsourcing-supported attribute-based access control scheme for edge-enabled smart healthcare. Future Gener. Comput. Syst. 115, 486–496 (2021)

    Article  Google Scholar 

  34. Alenezi, M.N., Alabdulrazzaq, H., Mohammad, N.Q.: Symmetric encryption algorithms: review and evaluation study. Int. J. Commun. Netw. Inf. Secur. 12(2), 256–272 (2020)

    Google Scholar 

  35. He, K., et al.: Secure dynamic searchable symmetric encryption with constant client storage cost. IEEE Trans. Inf. Forensics Secur. 16, 1538–1549 (2020)

    Article  Google Scholar 

  36. Li, J., et al.: Searchable symmetric encryption with forward search privacy. IEEE Trans. Dependable Secure Comput. 18(1), 460–474 (2019)

    Article  Google Scholar 

  37. Patranabis, S., Mukhopadhyay, D.: Forward and backward private conjunctive searchable symmetric encryption. Cryptology ePrint Archive (2020)

    Google Scholar 

  38. Gui, Z., Paterson, K.G., Patranabis, S.: Rethinking searchable symmetric encryption. In: 2023 IEEE Symposium on Security and Privacy (SP). IEEE (2023)

    Google Scholar 

  39. Zhang, Q.: An overview and analysis of hybrid encryption: the combination of symmetric encryption and asymmetric encryption. In: 2021 2nd International Conference on Computing and Data Science (CDS). IEEE (2021)

    Google Scholar 

  40. Sharifovich, A.S., Maxmudovich, H.X., Mansurovich, B.M.: Protocol for electronic digital signature of asymmetric encryption algorithm, based on asymmetric encryption algorithm based on the complexity of prime decomposition of a sufficiently large natural number. Texas J. Multidiscip. Stud. 7, 238–241 (2022)

    Google Scholar 

  41. Verma, G., et al.: An optical asymmetric encryption scheme with biometric keys. Optics Lasers Eng. 116, 32–40 (2019)

    Article  Google Scholar 

  42. Bao, Z., Xue, R., Jin, Y.: Image scrambling adversarial autoencoder based on the asymmetric encryption. Multimed. Tools App. 80(18), 28265–28301 (2021)

    Article  Google Scholar 

  43. Hu, Z., et al.: Reversible 3D optical data storage and information encryption in photo-modulated transparent glass medium. Light Sci. App. 10(1), 140 (2021)

    Article  Google Scholar 

  44. Jiang, F., et al.: Research on the application of transparent encryption in distributed file system HDFS. In: 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE (2020)

    Google Scholar 

  45. Su, N., Zhang, Y., Li, M.: Research on data encryption standard based on AES algorithm in internet of things environment. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE (2019)

    Google Scholar 

  46. Yazdeen, A.A., et al.: FPGA implementations for data encryption and decryption via concurrent and parallel computation: a review. Qubahan Acad. J. 1(2), 8–16 (2021)

    Article  Google Scholar 

  47. Ramachandra, M.N., et al.: An efficient and secure big data storage in cloud environment by using triple data encryption standard. Big Data Cogn. Comput. 6(4), 101 (2022)

    Article  Google Scholar 

  48. Akande, O.N., Abikoye, O.C., Kayode, A.A., Aro, O.T., Ogundokun, O.R.: A dynamic round triple data encryption standard cryptographic technique for data security. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 487–499. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58817-5_36

    Chapter  Google Scholar 

  49. Rivest, R., et al.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)

    Article  MathSciNet  Google Scholar 

  50. Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)

    Article  MathSciNet  Google Scholar 

  51. Ye, G., Liu, M., Mingfa, W.: Double image encryption algorithm based on compressive sensing and elliptic curve. Alex. Eng. J. 61(9), 6785–6795 (2022)

    Article  Google Scholar 

  52. Cui, H., et al.: TraceDroid: A Robust Network Traffic Analysis Framework for Privacy Leakage in Android Apps. In: Su, C., Sakurai, K., Liu, F. (eds.) Science of Cyber Security. SciSec 2022. LNCS, vol. 13580, pp. 541–556. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17551-0_35

  53. Singh, S.K., Yi, P., Park, J.H.: Blockchain-enabled secure framework for energy-efficient smart parking in sustainable city environment. Sustainable Cities Soc. 76, 103364 (2022)

    Article  Google Scholar 

  54. Kaur, S., Kaur, G., Shabaz, M.: A secure two-factor authentication framework in cloud computing. Secur. Commun. Netw. 2022, 1–9 (2022)

    Google Scholar 

  55. Watters, P., et al.: This would work perfectly if it weren’t for all the humans: two factor authentication in late modern societies. First Monday (2019)

    Google Scholar 

  56. Palma, D., Montessoro, P.L.: Biometric-based human recognition systems: an overview. In: Recent Advances Biometrics, pp. 1–21 (2022)

    Google Scholar 

  57. Singh, V., Kant, C.: Biometric-based authentication in Internet of Things (IoT): a review. Adv. Inf. Commun. Technol. Comput. Proc. AICTC 2022, 309–317 (2021)

    Google Scholar 

  58. Bera, B., et al.: On the design of biometric-based identity authentication protocol in smart city environment. Pattern Recogn. Lett. 138, 439–446 (2020)

    Article  Google Scholar 

  59. Gupta, S., Buriro, A., Crispo, B.: DriverAuth: a risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms. Comput. Secur. 83, 122–139 (2019)

    Article  Google Scholar 

  60. Sengupta, S.: A secured biometric-based authentication scheme in IoT-based patient monitoring system. In: Mandal, J.K., Bhattacharya, D. (eds.) Emerging Technology in Modelling and Graphics. AISC, vol. 937, pp. 501–518. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7403-6_44

    Chapter  Google Scholar 

  61. Priesnitz, J., et al.: An overview of touchless 2D fingerprint recognition. EURASIP J. Image Video Process. 2021(1), 1–28 (2021)

    Article  Google Scholar 

  62. Rajasekar, V., et al.: Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm. Sci. Rep. 12(1), 622 (2022)

    Article  Google Scholar 

  63. Boyd, A., et al.: Post-mortem iris recognition-a survey and assessment of the state of the art. IEEE Access 8, 136570–136593 (2020)

    Article  Google Scholar 

  64. Wang, C., et al.: Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition. IEEE Trans. Inf. Forensics Secur. 15, 2944–2959 (2020)

    Article  Google Scholar 

  65. Dargan, S., Kumar, M.: A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Syst. Appl. 143, 113114 (2020)

    Article  Google Scholar 

  66. Capece, G., Ghiron, N.L., Pasquale, F.: Blockchain technology: redefining trust for digital certificates. Sustainability 12(21), 8952 (2020)

    Article  Google Scholar 

  67. Rahardja, U., et al.: Immutable ubiquitous digital certificate authentication using blockchain protocol. J. Appl. Res. Technol. 19(4), 308–321 (2021)

    Article  Google Scholar 

  68. Maulani, G., et al.: Digital certificate authority with blockchain cybersecurity in education. Int. J. Cyber IT Serv. Manage. 1(1), 136–150 (2021)

    Article  Google Scholar 

  69. Hu, H., et al.: Mimic defense: a designed-in cybersecurity defense framework. IET Inf. Secur. 12(3), 226–237 (2018)

    Article  Google Scholar 

  70. Zhuang, R., et al.: A theory of cyber attacks: a step towards analyzing MTD systems. In: Proceedings of the Second ACM Workshop on Moving Target Defense (2015)

    Google Scholar 

  71. Reynolds, J., et al.: The design and implementation of an intrusion tolerant system. In: Proceedings International Conference on Dependable Systems and Networks. IEEE (2002)

    Google Scholar 

  72. Wang, F., et al.: SITAR: a scalable intrusion-tolerant architecture for distributed services. In: Workshop on Information Assurance and Security, vol. 1 (2003)

    Google Scholar 

  73. Cachin, C., et al.: Malicious-and Accidental-Fault Tolerance in Internet Applications: reference model and use cases (2000)

    Google Scholar 

  74. Pal, P., et al.: Intrusion tolerance by unpredictable adaptation (ITUA). Technical report. AFRL-IF-RS-TR-2005-119 (2005)

    Google Scholar 

  75. Bangalore, A.K., Sood, A.K.: Securing web servers using self cleansing intrusion tolerance (SCIT). In: 2009 Second International Conference on Dependability. IEEE (2009)

    Google Scholar 

  76. Huang, Y., Anup K. Ghosh. "Introducing diversity and uncertainty to create moving attack surfaces for web services. In: Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats, pp. 131–151.Springer, New York, NY (2011)

    Google Scholar 

  77. Okhravi, H., et al.: Creating a cyber moving target for critical infrastructure applications using platform diversity. Int. J. Critical Infrastruct. Protect. 5(1), 30–39 (2012)

    Article  Google Scholar 

  78. Li, X., et al.: A router abnormal traffic detection strategy based on active defense. In: Journal of Physics: Conference Series. Vol. 1738. No. 1. IOP Publishing (2021)

    Google Scholar 

  79. Tong, Q., et al.: Design and implementation of mimic defense Web server. J. Softw. 28(4), 883–897 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Instituite of Artificial Intelligence, Guangzhou University, 510006, Guangzhou, China

    Chong Shi, Jiahao Peng, Shuying Zhu & Xiaojun Ren

  2. Guangdong Provincial Key Laboratory of Blockchain Security, 510006, Guangzhou, China

    Xiaojun Ren

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  1. Chong Shi
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  2. Jiahao Peng
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Correspondence to Xiaojun Ren .

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Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Shi, C., Peng, J., Zhu, S., Ren, X. (2024). From Passive Defense to Proactive Defence: Strategies and Technologies. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_14

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