XGBoost Regression Classifier (XRC) Model for Cyber Attack Detection and Classification Using Inception V4
Keywords:Cybersecurity, XGBoost Regression Classifier (XRC), Inception V4, Error rate, Cyber Security
Massive reliance on practical systems has resulted in several security concerns. The ability to identify anomalies is a critical safety feature enabled by anomaly diagnostic techniques. The construction of a data system faces a significant issue in cyber security. Because of the exploitation of valuable data, cybersecurity impacts the privacy of such data. Attack incidents must be examined using an appropriate analytics approach in elevating the safety level. Design of advanced analytical, conceptual model creation gives practical guidance and prioritizes threats/attacks across the network system. There is now substantial effectiveness in attack categorization, and evaluation through Convolution Neural Network (CNN) based classifiers. In light of the drawbacks of previous approaches, this research proposes an approach relying on the Deep Learning (DL) strategies for cyberattacks detection and categorization in the context of cyberspace incidents. Likewise, this article presents an XGBoost Regression Classifier (XRC) using Inception V4 to address those restrictions. XGBoost refers to Extreme Gradient Boosting, a decentralized gradient-boosted decision tree (GBDT) supervised learning framework that is robust and can be used in a decentralized context. XGBoost is a well-known machine learning technique because of its ability to produce outstanding accuracy. The concepts of both XGBoost and Regression classifiers are integrated and represented as a suggested hybridized classifier, which is implemented in Inception V4 to further train and test the model. The proposed XRC categorizes and forecasts several common types of network cyberattacks that includes Distributed Denial of Service (DDoS), Phishing, Cross-site Scripting (CS), Internet of Things (IoT). The sigmoidal function is used as a supportive activator to the hybridized classifier to lower the erroneous ratio and increase the effectiveness. Research shows that training and testing errors were substantially decreased when using XRC. In 9 out of 13 instances, over 97% of threats are detected by the XRC, and over 75% of threats are detected in its most challenging datasets.
Z. Wang, L. Chen, S. Song, P. X. Cong, and Q. Ruan, “Automatic cyber security risk assessment based on fuzzy fractional ordinary differential equations,” Alexandria Engineering Journal, vol. 59, no. 4, pp. 2725–2731, 2020.
Van Staalduinen M. A, Khan F, Gadag V and Reniers G, “Functional quantitative security risk analysis (QSRA) to assist in protecting critical process infrastructure”, Reliability Engineering & System Safety, vol. 157, pp. 23–34, 2017.
A. Tantawy, S. Abdelwahed, A. Erradi, and K. Shaban, “Model-based risk assessment for cyber physical systems security,” Computers & Security, vol. 96, p. 101864, 2020.
C. Schmitz and S. Pape, “LiSRA: Lightweight Security Risk Assessment for decision support in information security,” Computers & Security, vol. 90, pp. 101656, 2020.
Venkatachary S. K, Prasad J and Samikannu R, “Cybersecurity and cyber terrorism-in energy sector–a review”, Journal of Cyber Security Technology, vol. 2, no. 3, pp. 111–130, 2018.
Kumar V. S, Prasad J and Samikannu R, “A critical review of cyber security and cyber terrorism–threats to critical infrastructure in the energy sector”, International Journal of Critical Infrastructures, vol. 14, no. 2, pp. 101–119, 2018.
Venkatachary S. K, Prasad J and Samikannu R, “Economic impacts of cyber security in energy sector: a review”, International Journal of Energy Economics and Policy, vol. 7, no. 5, pp. 250–262, 2017.
Venkatachary S. K, Prasad J and Samikannu R, Alagappan A and Andrews L. J. B, “Cybersecurity infrastructure challenges in IoT based virtual power plants”, Journal of Statistics and Management Systems, vol. 23, no. 2, pp. 263–276, 2020.
Benaroch M, “Real options models for proactive uncertainty-reducing mitigations and applications in cybersecurity investment decision making”, Information Systems Research, vol. 29, no. 2, pp. 315–340, 2018.
A. Nhlabatsi et al., “Threat-Specific Security Risk Evaluation in the Cloud,” in IEEE Transactions on Cloud Computing, vol. 9, no. 2, pp. 793–806, 2021.
Khidzir N. Z, Daud K. A. M, Ismail A. R, Ghani M. S. A. A and Ibrahim M. A. H, “Information Security Requirement: The Relationship Between Cybersecurity Risk Confidentiality, Integrity and Availability in Digital Social Media”, Regional Conference on Science, Technology and Social Sciences (RCSTSS), pp. 229–237, 2018.
Kusyk J, Uyar M. U and Sahin C. S, “Survey on evolutionary computation methods for cybersecurity of mobile ad hoc networks”, Evolutionary Intelligence, vol. 10, no. 3, pp. 95–117, 2018.
Sampathkumar, A., and Vivekanandan, P, Gene Selection Using Parallel Lion Optimization Method in Microarray Data for Cancer Classification. Journal of Medical Imaging and Health Informatics, vol. 9, no. 6, pp. 1294–1300, 2019.
Ashibani Y and Mahmoud Q. H, “Cyber physical systems security: Analysis, challenges and solutions”, Computers & Security, vol. 68, pp. 81–97, 2017.
Sampathkumar, A., Maheswar, P& Hashvardhan, “Majority Voting based Hybrid Ensemble Classification Approach for Predicting Parking Availability in Smart City based on IoT”, 11th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–8, 2020.
Abdo H, Kaouk M, Flaus J. M and Masse F, “A safety/security risk analysis approach of Industrial Control Systems: A cyber bowtie–combining new version of attack tree with bowtie analysis”, Computers & Security, vol. 72, pp. 175–195, 2018.
Urbina D. I, Giraldo J. A, Cardenas A. A, Tippenhauer N. O, Valente J, Faisal M and Sandberg H, “Limiting the impact of stealthy attacks on industrial control systems”, Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 1092–1105, 2016.
A. Gupta, A. Anpalagan, G. H. S. Carvalho, A. S. Khwaja, L. Guan, and I. Woungang, “RETRACTED: Prevailing and emerging cyber threats and security practices in IoT-Enabled smart grids: A survey,” Journal of Network and Computer Applications, vol. 132, pp. 118–148, 2019.
Januário F, Cardoso A and Gil P, “A distributed multi-agent framework for resilience enhancement in cyber-physical systems”, IEEE Access, vol. 7, pp. 31342–31357, 2019.
Durand L, “Cyber security: a risky business”, 2018. https://studenttheses.universiteitleiden.nl/access/item%3A2666281/view
Wu Z, Albalawi F, Zhang J, Zhang Z, Durand H and Christofides P. D, “Detecting and handling cyber-attacks in model predictive control of chemical processes”, Mathematics, vol. 6, no. 10, 2018.
Sándor H, Genge B, Szántó Z, Márton L and Haller P, “Cyber attack detection and mitigation: Software defined survivable industrial control systems”, International Journal of Critical Infrastructure Protection, vol. 25, pp. 152–168, 2019.
Paoletti N, Jiang Z, Islam M. A, Abbas H, Mangharam R, Lin S and Smolka S. A, “Synthesizing stealthy reprogramming attacks on cardiac devices”, Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, pp. 13–22, 2019.
Liu L, De Vel O, Han Q. L, Zhang J and Xiang Y, “Detecting and preventing cyber insider threats: A survey”, IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1397–1417, 2018.
Dataset of UHN, EMBER: https://csr.lanl.gov/data/2017/
Dataset of CSE-CIC-IDS 2018, https://www.kaggle.com/solarmainframe/ids-intrusion-csv
L. Lorenzi, “Analytical Methods for Kolmogorov Equations,” Oct. 2016.
J. Milosevic, H. Sandberg, and K. H. Johansson, “Estimating the Impact of Cyber-Attack Strategies for Stochastic Networked Control Systems,” IEEE Transactions on Control of Network Systems, vol. 7, no. 2, pp. 747–757, Jun. 2020.
R. Hoffman, “The General Cyber-Attack Life Cycle And Its Continuous-Time Markov Chain Model,” Ekonomiczne Problemy Usług, vol. 131, pp. 121–130, 2018.
H. Om and T. K. Sarkar, “Designing Intrusion Detection System for Web Documents Using Neural Network,” Communications and Network, vol. 02, no. 01, pp. 54–61, 2010.
M. E. Haque and T. M. Alkharobi, “Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms,” International Journal of Machine Learning and Computing, vol. 5, no. 1, pp. 17–23, Feb. 2015.
G. R. Kumar, N. Mangathayaru, and G. Narsimha, “An approach for intrusion detection using fuzzy feature clustering,” 2016 International Conference on Engineering & MIS (ICEMIS), Sep. 2016.
C. Liu, J. Yang, and J. Wu, “Web intrusion detection system combined with feature analysis and SVM optimization,” EURASIP Journal on Wireless Communications and Networking, vol. 2020, no. 1, Feb. 2020.
S. S. Sivatha Sindhu, S. Geetha, and A. Kannan, “Decision tree based light weight intrusion detection using a wrapper approach,” Expert Systems with Applications, vol. 39, no. 1, pp. 129–141, Jan. 2012.
T. A. Deepak, “XGBoost Classification based Network Intrusion Detection System for Big Data using PySparkling Water,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1, pp. 377–382, Feb. 2020.
A.-C. Enache and V. Sgârciu, “Enhanced Intrusion Detection System Based on Bat Algorithm-support Vector Machine,” Proceedings of the 11th International Conference on Security and Cryptography, 2014.