A Robust Framework for Detecting Brute-Force Attacks through Deep Learning Techniques
Article Sidebar
Main Article Content
Abstract
A considerable concern arises with the precise identification of brute-force threats within a networked environment. It emphasizes the need for new methods, as existing ones often lead to many false alarms, as well as delays in real-time threat detection. To tackle these issues, this study proposes a novel intrusion detection framework that utilizes deep learning models for more accurate and efficient detection of brute-force attacks. The framework’s structure includes data collection and preprocessing components performed at the outset of the study using the CSE-CICIDS2018 dataset. The design architecture includes data collection and preprocessing steps. Feature extraction and selection techniques are employed to optimize data for model training. Further, after building the model, various attributes are extracted from the data from feature selection to be used in the training. Then, the construction of multiple architectures of deep learning algorithms, which include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models. Evaluation results show CNN and LSTM achieved the highest accuracy of 99.995% and 99.99% respectively. It showcases its ability to detect complex attack patterns in network traffic. It indicates that the CNN network got the best optimum results with a test time of 9.94 seconds. This establishes CNN as an effective method, achieving high accuracy quickly. In comparison, we have surpassed the accuracy of current methods while addressing their weaknesses. The findings are consistent with the effectiveness of CNN in brute-force attack detection frameworks as a more accurate and faster alternative, increasing the capability of detecting intrusions on a network in real-time.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
J. Li, M. S. Herdem, J. Nathwani, and J. Z. Wen, ‘Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management’, Energy AI, vol. 11, p. 100208, 2023. DOI: https://doi.org/10.1016/j.egyai.2022.100208
W. S. Admass, Y. Y. Munaye, and A. Diro, ‘Cyber security: State of the art, challenges, and future directions’, Cyber Secur. Appl., p. 100031, 2023. DOI: https://doi.org/10.1016/j.csa.2023.100031
M. F. K. Shah, M. Md-Arshad, A. A. Samad, and F. A. Ghaleb, ‘Comparing ftp and ssh password brute force attack detection using k-nearest neighbor (k-nn) and decision tree in cloud computing’, Int. J. Innov. Comput., vol. 13, no. 1, pp. 29–35, 2023. DOI: https://doi.org/10.11113/ijic.v13n1.386
P. K. Sadhu, V. P. Yanambaka, and A. Abdelgawad, ‘Internet of things: Security and solutions survey’, Sensors, vol. 22, no. 19, p. 7433, 2022. DOI: https://doi.org/10.3390/s22197433
S. Kumar, S. Gupta, and S. Arora, ‘Research trends in network-based intrusion detection systems: A review’, Ieee Access, vol. 9, pp. 157761–157779, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3129775
Z. Azam, M. M. Islam, and M. N. Huda, ‘Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree’, IEEE Access, 2023, Accessed: Sep. 29, 2024. DOI: https://doi.org/10.1109/ACCESS.2023.3296444
Z. A. E. Houda, B. Brik, and L. Khoukhi, ‘“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in the Internet of Things Networks’, IEEE Open J. Commun. Soc., vol. 3, pp. 1164–1176, 2022, DOI: https://doi.org/10.1109/OJCOMS.2022.3188750
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, ‘Network intrusion detection system: A systematic study of machine learning and deep learning approaches’, Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, p. e4150, Jan. 2021, DOI: https://doi.org/10.1002/ett.4150
T. Srinivas, G. Aditya Sai, and R. Mahalaxmi, ‘A comprehensive survey of techniques, applications, and challenges in deep learning: A revolution in machine learning’, Int. J. Mech. Eng., vol. 7, no. 5, pp. 286–296, 2022. DOI: https://doi.org/10.70593/978-81-981367-4-9_2
M. Z. Gunduz and R. Das, ‘Cyber-security on smart grid: Threats and potential solutions’, Comput. Netw., vol. 169, p. 107094, 2020. DOI: https://doi.org/10.1016/j.comnet.2019.107094
A. A. Hagar and B. W. Gawali, ‘Deep Learning for Improving Attack Detection System Using CSE-CICIDS2018’, NeuroQuantology, vol. 20, no. 6, p. 3064, 2022. https://www.researchgate.net/publication/362619753_Deep_Learning_for_Improving_Attack_Detection_System_Using_CSE-CICIDS2018
J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, ‘A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions’, Electronics, vol. 9, no. 7, p. 1177, Jul. 2020. DOI: https://doi.org/10.3390/electronics9071177
M. Paramesha, N. L. Rane, and J. Rane, ‘Artificial Intelligence, Machine Learning, and Deep Learning for Cybersecurity Solutions: A Review of Emerging Technologies and Applications’, Partn. Univers. Multidiscip. Res. J., vol. 1, no. 2, Art. no. 2, Jul. 2024, DOI: https://doi.org/10.5281/zenodo.12827076
Z. T. Pritee, M. H. Anik, S. B. Alam, J. R. Jim, M. M. Kabir, and M. F. Mridha, ‘Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review’, Comput. Secur., vol. 140, p. 103747, May 2024, DOI: https://doi.org/10.1016/j.cose.2024.103747
M. Abdel-Rahman, ‘Advanced Cybersecurity Measures in IT Service Operations and Their Crucial Role in Safeguarding Enterprise Data in a Connected World’, Eig. Rev. Sci. Technol., vol. 7, no. 1, pp. 138–158, 2023. DOI: https://doi.org/10.3390/electronics9071177
A. Nagisetty and G. P. Gupta, ‘Framework for detection of malicious activities in IoT networks using keras deep learning library’, in 2019 3rd international conference on computing methodologies and communication (ICCMC), IEEE, 2019, pp. 633–637. DOI: https://doi.org/10.1109/ICCMC.2019.8819688
S. M. Albladi and G. R. Weir, ‘User characteristics that influence judgment of social engineering attacks in social networks’, Hum.-Centric Comput. Inf. Sci., vol. 8, no. 1, pp. 1–24, 2018. DOI: https://doi.org/10.1177/0093650215627483
I. H. Sarker, Y. B. Abushark, F. Alsolami, and A. I. Khan, ‘IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model’, Symmetry, vol. 12, no. 5, Art. no. 5, May 2020, DOI: https://doi.org/10.3390/sym12050754
F. Pascale, E. A. Adinolfi, S. Coppola, and E. Santonicola, ‘Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles’, Electronics, vol. 10, no. 15, Art. no. 15, Jan. 2021, DOI: https://doi.org/10.3390/electronics10151765
T. Kim and W. Pak, ‘Robust network intrusion detection system based on machine-learning with early classification’, IEEE Access, vol. 10, pp. 10754–10767, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3145002
M. N. Alatawi et al., ‘[Retracted] Cyber Security against Intrusion Detection Using Ensemble-Based Approaches’, Secur. Commun. Netw., vol. 2023, no. 1, p. 8048311, 2023, DOI: https://doi.org/10.1155/2023/8048311
İ. Avcı and M. Koca, ‘Cybersecurity Attack Detection Model, Using Machine Learning Techniques’, Acta Polytech. Hung., vol. 20, no. 7, pp. 29–44, 2023. DOI: https://doi.org/10.12700/APH.20.7.2023.7.2
A. Khan and C. Cotton, ‘Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning’, ArXiv Prepr. ArXiv230103532, 2023. DOI: https://doi.org/10.48550/arXiv.2301.03532
M. A. Alsoufi et al., ‘Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review’, Appl. Sci., vol. 11, no. 18, Art. no. 18, 2021. DOI: https://doi.org/10.3390/app11188383
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, ‘Deep learning approach for intelligent intrusion detection system’, Ieee Access, vol. 7, pp. 41525–41550, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2895334
A. Dey, ‘Deep IDS: A deep learning approach for Intrusion detection based on IDS 2018’, in 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), IEEE, 2020, pp. 1–5. Accessed: Sep. 30, 2024. [Online]. Available: DOI: https://doi.org/10.1109/STI50764.2020.9350411
Z. Wang, Y. Liu, D. He, and S. Chan, ‘Intrusion detection methods based on integrated deep learning model’, Comput. Secur., vol. 103, p. 102177, 2021. DOI: https://doi.org/10.1016/j.cose.2021.102177
Y. Otoum, D. Liu, and A. Nayak, ‘DL‐IDS: a deep learning–based intrusion detection framework for securing IoT’, Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, p. e3803, Mar. 2022, DOI: https://doi.org/10.1002/ett.3803
V. Hnamte and J. Hussain, ‘DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system’, Telemat. Inform. Rep., vol. 10, p. 100053, 2023. DOI: https://doi.org/10.1016/j.teler.2023.100053
A. Henry et al., ‘Composition of hybrid deep learning model and feature optimization for intrusion detection system’, Sensors, vol. 23, no. 2, p. 890, 2023. DOI: https://doi.org/10.3390/s23020890
E. U. H. Qazi, M. H. Faheem, and T. Zia, ‘HDLNIDS: hybrid deep-learning-based network intrusion detection system’, Appl. Sci., vol. 13, no. 8, p. 4921, 2023. DOI: https://doi.org/10.3390/app13084921
V. Hnamte, H. Nhung-Nguyen, J. Hussain, and Y. Hwa-Kim, ‘A novel two-stage deep learning model for network intrusion detection: LSTM-AE’, Ieee Access, vol. 11, pp. 37131–37148, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3266979
H. Gonaygunta, G. S. Nadella, P. Pramod Pawar, and D. Kumar, ‘Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection’, in 2024 Systems and Information Engineering Design Symposium (SIEDS), May 2024, pp. 79–84. DOI: https://doi.org/10.1109/SIEDS61124.2024.10534661
F. Al-Quayed, Z. Ahmad, and M. Humayun, ‘A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0’, IEEE Access, vol. 12, pp. 34800–34819, 2024, DOI: https://doi.org/10.1109/ACCESS.2024.3372187
R. Devendiran and A. V. Turukmane, ‘Dugat-LSTM: Deep learning-based network intrusion detection system using chaotic optimization strategy’, Expert Syst. Appl., vol. 245, p. 123027, Jul. 2024, DOI: https://doi.org/10.1016/j.eswa.2023.123027
Z. K. Maseer, R. Yusof, N. Bahaman, S. A. Mostafa, and C. F. M. Foozy, ‘Benchmarking of machine learning for anomaly-based intrusion detection systems in the CICIDS2017
dataset’, IEEE Access, vol. 9, pp. 22351–22370, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3056614
L. Ashiku and C. Dagli, ‘Network Intrusion Detection System using Deep Learning’, Procedia Comput. Sci., vol. 185, pp. 239–247, Jan. 2021, DOI: https://doi.org/10.1016/j.procs.2021.05.025
M. Asif, S. Abbas, M. A. Khan, A. Fatima, M. A. Khan, and S.-W. Lee, ‘MapReduce based intelligent model for intrusion detection using machine learning technique’, J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, Part B, pp. 9723–9731, Nov. 2022, DOI: https://doi.org/10.1016/j.jksuci.2021.12.008
M. A. Kristyanto et al., ‘Ssh bruteforce attack classification using machine learning’, in 2022 10th International Conference on Information and Communication Technology (ICoICT), IEEE, 2022, pp. 116–119. Accessed: Sep. 30, 2024. [Online]. DOI: https://doi.org/10.1109/ICoICT55009.2022.9914864
B. Karthiga, D. Durairaj, N. Nawaz, T. K. Venkatasamy, G. Ramasamy, and A. Hariharasudan, ‘Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches’, Wirel. Commun. Mob. Comput., vol. 2022, pp. 1–13, Oct. 2022, DOI: https://doi.org/10.1155/2022/5069104
M. U. Ilyas and S. A. Alharbi, ‘Machine learning approaches to network intrusion detection for contemporary internet traffic’, Computing, vol. 104, no. 5, pp. 1061–1076, May 2022, DOI: https://doi.org/10.1007/s00607-021-01050-5
O. Elnakib, E. Shaaban, M. Mahmoud, and K. Emara, ‘EIDM: deep learning model for IoT intrusion detection systems’, J. Supercomput., vol. 79, no. 12, pp. 13241–13261, Aug. 2023, DOI: https://doi.org/10.1007/s11227-023-05197-0
R. Lazzarini, H. Tianfield, and V. Charissis, ‘A stacking ensemble of deep learning models for IoT intrusion detection’, Knowl.-Based Syst., vol. 279, p. 110941, 2023. DOI: https://doi.org/10.1016/j.knosys.2023.110941
B. Sharma, L. Sharma, C. Lal, and S. Roy, ‘Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach’, Expert Syst. Appl., vol. 238, p. 121751, Mar. 2024, DOI: https://doi.org/10.1016/j.eswa.2023.121751
F. Zhao, H. Li, K. Niu, J. Shi, and R. Song, ‘Application of deep learning-based intrusion detection system (IDS) in network anomaly traffic detection’, 2024. Accessed: Sep. 30, 2024. [Online]. Available: DOI: https://doi.org/10.20944/preprints202407.0595.v1
B. Hade Variant Wahono, Asfihani, I. Mahfud, B. Y. I. Exshadi, and A. M. Shiddiqi, ‘Brute Force Detection System Based on Machine Learning Classifier Algorithm in Cloud-Based Infrastructure’, in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Jan. 2024, pp. 939–943. DOI: https://doi.org/10.1109/ICETSIS61505.2024.10459370
D. Jim Solomon Raja, R. Sriranjani, P. Arulmozhi, and N. Hemavathi, ‘Unified Random Forest and Hybrid Bat Optimization Based Man-in-the-Middle Attack Detection in Advanced Metering Infrastructure’, IEEE Trans. Instrum. Meas., vol. 73, pp. 1–12, 2024, DOI: https://doi.org/10.1109/TIM.2024.3420375
A. Raj et al., ‘Brute forcing on secured shell servers emphasising the role of cyber forensics – a quali-quantitative study’, Med. Leg. J., p. 00258172241236269, Jun. 2024, DOI: https://doi.org/10.1177/00258172241236269
F. Rustam, A. Raza, M. Qasim, S. K. Posa, and A. D. Jurcut, ‘A Novel Approach for Real-Time Server-Based Attack Detection Using Meta-Learning’, IEEE Access, vol. 12, pp. 39614–39627, 2024, DOI: https://doi.org/10.1109/ACCESS.2024.3375878
Z. e Huma, J. Ahmad, H. A. Hamadi, B. Ghaleb, W. J. Buchanan, and S. U. Jan, ‘ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT’, in 2024 2nd International Conference on Cyber Resilience (ICCR), Feb. 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICCR61006.2024.10532958
D. Shou et al., ‘An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model’, Comput. J., vol. 67, no. 5, pp. 1851–1865, May 2024, DOI: https://doi.org/10.1093/comjnl/bxad105
S. Alzughaibi and S. El Khediri, ‘A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset’, Appl. Sci., vol. 13, no. 4, p. 2276, Jan. 2023, DOI: https://doi.org/10.3390/app13042276
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, ‘Toward generating a new intrusion detection dataset and intrusion traffic characterization.’, ICISSp, vol. 1, pp. 108–116, 2018. DOI: https://doi.org/10.5220/0006639801080116
E. Bisong, ‘Google Colaboratory’, in Building Machine Learning and Deep Learning Models on Google Cloud Platform, Berkeley, CA: Apress, 2019, pp. 59–64. DOI: https://doi.org/10.1007/978-1-4842-4470-8_7
M. A. Shyaa, Z. Zainol, R. Abdullah, M. Anbar, L. Alzubaidi, and J. Santamaría, ‘Enhanced intrusion detection with data stream classification and concept drift guided by the incremental learning genetic programming combiner’, Sensors, vol. 23, no. 7, p. 3736, 2023. DOI: https://doi.org/10.3390/s23073736
P. Dini et al., ‘Design and testing novel one-class classifier based on polynomial interpolation with application to networking security’, IEEE Access, vol. 10, pp. 67910–67924, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3186026
Yoo, B. Min, S. Kim, D. Shin, and D. Shin, ‘Study on network intrusion detection method using discrete pre-processing method and convolution neural network’, IEEE Access, vol. 9, pp. 142348–142361, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3120839
S. Seth, K. K. Chahal, and G. Singh, ‘A novel ensemble framework for an intelligent intrusion detection system’, IEEE Access, vol. 9, pp. 138451–138467, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3116219
A. Jumabek, S. Yang, and Y. Noh, ‘CatBoost-based network intrusion detection on imbalanced CIC-IDS-2018 dataset’, 한국통신학회논문지, vol. 46, no. 12, pp. 2191–2197, 2021. DOI: https://doi.org/10.7840/kics.2021.46.12.2191
H. Najafi Mohsenabad and M. A. Tut, ‘Optimizing cybersecurity attack detection in computer networks: A comparative analysis of bio-inspired optimization algorithms using the CSE-CIC-IDS 2018 dataset’, Appl. Sci., vol. 14, no. 3, p. 1044, 2024. DOI: https://doi.org/10.3390/app14031044