پدافند الکترونیکی و سایبری

پدافند الکترونیکی و سایبری

دالون: دفاع فرافعال سایبری با چارچوب تله سایبری هجومی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، دانشگاه جامع امام حسین (ع)، تهران،ایران
2 استادیار، دانشگاه جامع امام حسین (ع)، تهران، ایران
چکیده
امروزه شناسایی، تعقیب و اقدام بازدارنده علیه مهاجمین سایبری، یکی از چالش‌های اصلی در حوزه امنیت و دفاع سایبری است. سازوکار‌های سنتی تشخیص حملات به دلیل رویکرد واکنشی به مسئله دفاع و نرخ بالای هشدارهای مثبت نادرست، فرایند تشخیص را پیچیده کرده است. روش‌های متعددی برای حل این چالش ارایه شده است و استفاده از تله‌های سایبری، یکی از روش‌های مؤثری است که در تشخیص هدفمند و پیش از موعد تهدیدات در حال توسعه و بهره‌برداری است. راه‌کارهای موجود تله‌های سایبری به‌دلیل ساختار منفعلانه و یک‌طرفه عمل نمودن، منجر به بازدارندگی و شناسایی منشاء وقوع حمله نمی‌گردند. در این مقاله، یک چارچوب توسعه‌یافته شبکه‌ تله ترکیبی‌ به نام دالون پیشنهاد می‌شود. دالون از طریق بخش «تله وب انفجاری»، قابلیت ضدحمله و نفوذمعکوس علیه مهاجمین به‌دام‌افتاده را فراهم می‌کند و ضمن شناسایی و تعقیب مهاجم، امکان اقدام تنبیهی که منجر به بازدارندگی سایبری می‌شود را فراهم می‌کند. دالون با طراحی تله وب‌ جعلی و شبیه‌سازی HMI سیستم کنترل صنعتی اسکادا و آلوده‌سازی آن به آسیب‌پذیری جعلی و عمدی تزریق‌کد و چندین آسیب‌پذیری دیگر پیاده‌سازی شده است. نتایج آزمایش‌ها نشان می‌دهد که دالون عملکرد موفقی در شناسایی و برخورد با سه مورد از روش‌های تهاجمی دارد و اقدام نفوذ معکوس و «ضدحمله» را با موفقیت انجام داده است. طرح پیشنهادی تله ترکیبی دالون، با قابلیت نفوذ معکوس، منجر به اتخاذ رویکرد دفاع فرافعال سایبری یعنی دفاع حین حمله می‌شود و علاوه بر کاهش فرایند مثبت غلط در تحلیل حملات، فرایند یک‌طرفه تله‌های سایبری معمول را که صرفاً دفاعی و منفعل هستند، به فرآیند دوطرفه که هجومی و بازدارنده هستند، تبدیل می‌کند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Dalon: Proactive Cyber Defence with a Counter Attack Honypot Framework

نویسندگان English

morteza kheiry 1
Reza Jalaei 2
1 PhD Student, Imam Hossein (AS) University, Tehran, Iran
2 Assistant Professor, Imam Hossein (AS) University, Tehran, Iran
چکیده English

Today, detecting, tracking, and taking deterrent action against cyber attackers is one of the main challenges in the field of cybersecurity and cyber defense. Traditional attack detection mechanisms, due to their reactive approach to defense and the high rate of false-positive alerts, have complicated the detection process.Various methods have been proposed to address this challenge, and the use of cyber deception traps is one of the effective approaches currently being developed and utilized for targeted and proactive detection of emerging threats. Existing cyber trap solutions, due to their passive structure and one-directional operation, do not lead to deterrence or identification of the origin of the attack.

In this article, an enhanced hybrid trap-network framework called Daloon is proposed. Daloon, through its “Explosive Web Trap” component, enables counterattack and reverse intrusion against trapped attackers and, while identifying and tracking the attacker, provides the capability for punitive action that results in cyber deterrence. Daloon is implemented by designing a fake web trap and simulating the HMI of a SCADA industrial control system and contaminating it with a fake and intentionally crafted code-injection vulnerability as well as several other vulnerabilities. Experimental results show that Daloon performs successfully in detecting and responding to three types of offensive techniques and has successfully carried out reverse intrusion and “counterattack.” The proposed Daloon hybrid trap, with reverse-intrusion capability, leads to the adoption of a proactive cyber defense approach—i.e., defense during the attack—and, in addition to reducing false positives in attack analysis, transforms the one-way process of traditional cyber traps, which are purely defensive and passive, into a two-way process that is offensive and deterrent.

کلیدواژه‌ها English

Hybrid Honeypot
Honeynet
Counter Attack
HackBack
Active Defence
Cyber Deception
ProActive Cyber Defence
[1]    J. Z. Ahmadabadi and K. Ebrahimi, “Investigating the Impact of Environmental Data Changes on the Lifetime of Wireless Sensor Network,” J ELECTRONICAL & CYBER DEFENCE, vol. 12, no. 3, 2024.  DOR:20.1001.1.23224347 .1403.12.3.2.4, https://ecdj.ihu.ac.ir. (in Persian)
[2]    A. R. Nadinejad and M. Alaei, “A Heuristic Data Diffusion and Gathering Scheme Using Virtual Line for Wireless Sensor Networks with Mobile Sink,” J ELECTRONICAL & CYBER DEFENCE, vol. 9, no.2, 2021. https://ecdj.ihu.ac.ir. (in Persian)
[3]    M. Alaei and F. Yazdanpanah, “A Method for Energy and Delay Aware Routing in Wireless Multimedia Sensor Networks,” J ELECTRONICAL & CYBER DEFENCE, vol.12, no. 47, 2024. https://ecdj.ihu.ac.ir. (in Persian)
[4]    H. Zhou and J. Li, “Sleep Scheduling for Enhancing the Lifetime of Three-Dimensional Heterogeneous Wireless Sensor Networks,” In book: Computer Supported Cooperative Work and Social Computing, pp.365-375, 2023. https://doi.org/ 10.1007/978-981-99-2356-4_29.
[5]    M. Alaei and F. Yazdanpanah, “ZOGLO: A Scheme of Zoning and Data Gathering for Lifetime Optimization in Wireless Sensor Networks,” Journal of Soft Computing and Information Technology (JSCIT), vol. 7, Issue 2, pp. 71-80, 2019. (in Persian)
[6]    A. Seyyedabbasi, G. Dogan, and F. Kiani, “HEEL: A New Clustering Method To Improve Wireless Sensor Network Lifetime,” IET WIRELESS SENSOR SYS, vol. 10, no. 3, pp. 130-136, 2020. https://doi.org/10.1049/ietwss.2019.0153.
[7]    S. Jadhav and S. Jadhav, “KPSO: K-Mean and PSO Based Clustering Algorithm for Wireless Sensor Network,” 6th IEEE International Conference On Computing, Communica-tion, Control and Automation (ICCUBEA), 2020. https://doi.org/10.1109/ICCUBEA54992.2022.10011024.
[8]    A. Joseph, R. Asaletha, V. J. Manoj, and R. Nishanth, “Enhancing the Network Lifetime of a Wireless Sensor Network using Modified K-Means Firefly Optimization,” J PHYS, vol. 2466, no. 012019, 2023. https://doi.org/10.1088/ 1742- 6596/2466/1/012019.
[9]    F. Elfouly, A. Khedr, M. H. Sharif, E. Alreshidi, K. Yadav, H. Kusetogullari, and R. Ramadan, “ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks,” SENSORS,  vol. 22, no. 8950, pp. 1-17, 2022. https://doi.org/10.3390/s22228950.
[10]  M. Alaei and F. Yazdanpanah, “EELCM: An Energy Efficient Load-Based Clustering Method for Wireless Mobile Sensor Networks,” MOBILE NETW APPL, vol. 24, no. 5, pp.1486-1498, 2019. https://doi.org/10.1007/s11036-019-01270 -2.
[11]  B. Sarangi and B. Tripathy, “Outlier Detection Technique for Wireless Sensor Network Using GAN with Autoencoder to Increase the Network Lifetime,” INT J COMP NETW INF SEC, vol. 15, pp. 26-38, 2023. https://doi.org/10.4018/ IJBDCN.286705.
[12]  S. Z. Majidian and M. Shirmohammadi, “Clustering and Routing in Wireless Sensor Networks Using Multi-Objective Cuckoo Search and Game Theory,” J ELECTRONICAL & CYBER DEFENCE, vol. 10, no. 3, 2023. DOR:20.1001 .1.23224347.1401.10.3.2.0, https://ecdj.ihu.ac.ir.  (in Persian)
[13]  D. S. Sultana, D. Bordoloi, C. Singh, D. Srivastava, N. Thiyagarajan, and N. Chinthamu, “A Comparative Approach on Enhancing Lifetime of Wireless Sensor Networks,” 5th IEEE International Conference on Contemporary Computing and Informatics, 2023. https://doi.org/10.47750/pnr.2022.13 .S07.189.
[14]  R. Subha and A. Haldorai, “Improved EPOA Clustering Protocol for Lifetime Longevity in Wireless Sensor Network,” SENSORS,  vol. 3. no. 100199, 2022. https://doi.org/10.1016/j.sintl.2022.100199.
[15]  D. Agrawal, S. Pandey, P. Gupta, and M. K. Goyal, “Optimization of Cluster Heads Through Harmony Search Algorithm in Wireless Sensor Networks,” J INTELL FUZZY SYST, vol. 39, no. 6, pp. 8587-8597, 2020, https://doi.org/ 10.3233/ JIFS-189175.
[16]  L. Jawad and A. Idrees, “Integrated Encoding and Scheduling Protocol for Improving the Lifetime in Wireless Sensor Networks,” INT J COMPUT APPL T, vol. 69, no. 334, pp. 334-343, 2022. https://doi.org/ 10.1504/IJCAT. 2022.10054564.
[17]  V. Narayan  and A. K. Daniel, “Energy Efficient Protocol for Lifetime Prediction of Wireless Sensor Network using Multivariate Polynomial Regression Model,” J SCI IND RES INDIA, vol. 81, no. 12, pp. 1297-1309, 2022. https://doi.org /0.56042/jsir.v81i12.54908.
[18]  K. Debasis, L. Sharma, V. Bohat, and R. Bhadoria, “An Energy-Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks using Machine Learning,” MOBILE NETW APPL, vol.28, no.2, pp. 1-15, 2023. https://doi.org/ 10.1007/s11036-023-02109-7.
[19]  S. Phommasan, Widyawan, and I. W. Mustika “Cluster Selection Technique with Fuzzy Logic-Based Wireless Sensor Network to Increase the Lifetime of Networks,” 5th International Conference on Research of Information Technology and Intelligent Systems (ISRITI), 2022. https://doi.org/ 10.1109/ISRITI56927.2022.10052871.
[20]  A. Taha, H. Abouroumia, S. Mohamed, and L. Amar, “Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm,” FUTURE INTERNET, vol. 14. no. 365, pp. 1-17, 2022. https://doi.org/ 10.3390/fi14120365.
[21]  C. D. Tran, N. Tam, N. Quy, and H. Binh, “An Energy-Efficient Scheme for Maximizing Data Aggregation Tree Lifetime in Wireless Sensor Network,” J AMB INTEL HUM COMP, vol. 14, 2023. https://doi.org/s12652-023-04621-w.
[22]  A. J. Yuste-Delgado, J. C. Cuevas-Martinez, and A. Trivino-Cabrera, “A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks,” SENSORS, vol. 20, no. 8, ID: 2312, pp. 1-18, 2020. https://doi.org/10.3390/s20082312.
[23]  A. M. Jubair, R. Hassan, A. Aman, H. Sallehudin, Z. Al-Mekhlafi, B. Mohammed, and M. Alsaffar, “Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols,” APPL SCI, vol. 11, no. 23, 11448, pp. 1-30, 2021. https://doi.org/10.3390/app112311448.
[24]  M. Mirzasadeghi and H. Bakhshi, “A New Method for Clustering Wireless Sensor Networks to Improve the Energy Consumption,” J COMMUN ENG, vol. 5, no. 2, pp. 136-149, 2016. https://doi.org/10.22070/jce.2017.1614.
[25]  M. Khan, J. Khan, K. Mahmood, I. Bari, H. Ali, N. Jan, and R. Ghoniem, “Algorithm for Increasing Network Lifetime in Wireless Sensor Networks Using Jumping and Mobile Sensor Nodes,” ELECTRONICS, vol. 11, no.2913, pp. 1-15, 2022. https://doi.org/ 10.3390/electronics11182913.
[26]  R. Medeiros, J. M. Villanueva, and E. Macedo, “Lifetime Increase for Wireless Sensor Networks Using Cellular Learning Automata,” WIRELESS PERS COMMUN, vol. 123, pp. 3413–3432, 2022. https://doi.org/10.1007/s11277-021-09295-1.
[27]  R. Marappan, P. Vardhini, G. Kaur, S. Murugesan, M. Kathiravan, N. Bharathiraja, and R. Venkatesan, “Efficient Evolutionary Modeling in Solving Maximization of Lifetime of Wireless Sensor Healthcare Networks,” SOFT COMPUT, vol. 27, Issue. 16, PP. 11853-11867, 2023. https://doi.org/ 10.1007 /s00500-023-08623-w.
[28]  M. Alaei, P. Sabbagh, and F. Yazdanpanah, “A QoS-aware Congestion Control Mechanism for Wireless Multimedia Sensor Networks,” WIREL NETW, vol. 25, no. 3, pp. 4173-4192, 2019. https://doi.org/10.1007/s11276-018-1738-8.
[29]  S. Misra and R. Kumar, “An Analytical Study of LEACH and PEGASIS Protocol in Wireless Sensor Networks,” International Conference on Innovations in information Embedded and Communication Systems (ICIIECS), 2017, https://doi.org/10.1109/ICIIECS.2017.8276118.
[30]  R. Chang and C. Kuo, “An Energy Efficient Routing Mechanism for Wireless Sensor Networks,” 20th International Conference on Advanced Information Networking and Applications (AINA’06), 2006. https://doi. org/10.1109/AINA.2006.86.
[31]  W. B. Heinzelman, A.P. Chandrakasan, and H. Balakrish-nan, “An Application-specific Protocol Architecture for Wireless Microsensor Networks,” IEEE T WIREL COMMUN, vol. 1, Issue 4, 2002. https://doi.org/10.1109/ TWC.2002.804190.
دوره 13، شماره 4 - شماره پیاپی 52
زمستان
زمستان 1404
صفحه 91-104

  • تاریخ دریافت 16 مهر 1404
  • تاریخ بازنگری 03 آذر 1404
  • تاریخ پذیرش 28 آذر 1404
  • تاریخ انتشار 01 دی 1404