Abstract
Cybercrimes are becoming increasingly more sophisticated and dangerous as we rely more on technology in all aspects of our lives. Crimes, such as data breaches, cyber extortion, and identity theft are more common than ever. It is estimated to cost the world billions of dollars and no country is immune to it. This paper aims to investigate the possibility of using various machine learning techniques, such as stochastic gradient descent and random forest in order to forecast potential cyberattacks. This is done by training the chosen machine learning model using the UNSW-NB15 dataset. This dataset contains nine types of network-based cyberattacks along with normal network activities. Information Gain Attribute Evaluation (IGAE) is used for feature selection with a rank cutoff 0.15. For the cross-validation task, 10-fold cross-validation is used. Results show that applying feature selection marginally increased the accuracy of all models used. The accuracy of the models ranged between 92.4% and 99.9%. The highest accuracy is obtained when using the random forest algorithm and a combination of random forest and logistic regression.
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References
G. Alshehabi, Bahrain News: Cyber attacks increase 23pc, Gdnonline.com (2021) [Online]. Available: https://www.gdnonline.com/Details/940409/Cyber-attacks-increase-23pc. Accessed 23 Oct 2021
“Cybercrime will cost the world US$6 trillion by the end of the year: Study”, CISO MAG | Cyber Security Magazine, 2021. [Online]. Available: https://cisomag.eccouncil.org/cybercrime-will-cost-theworld-us6-trillion-by-the-end-of-the-year-study/. Accessed 23 Oct 2021
“Data security: How a proactive C-suite can reduce cyber-risk for the enterprise”, Perspectives from The Economist Intelligence Unit (EIU), 2021. [Online]. Available: https://impact.economist.com/ perspectives/technology-innovation/data-security-how-proactive-csuite-can-reduce-cyber-risk-enterprise. Accessed 23 Oct 2021
“Early Warning”, Ncsc.gov.uk, 2021. [Online]. Available: https://www.ncsc.gov.uk/information/early-warning-service. Accessed 23 Oct 2021
P. Goyal, K.S.M. Hossain, A. Deb, N. Tavabi, N. Bartley, A.E. Abeliuk, et al., Discovering signals from web sources to predict cyber attacks. ar**v preprint ar**v:1806.03342 (2018)
G. Werner, A. Okutan, S. Yang, K. McConky, Forecasting cyberattacks as time series with different aggregation granularity, in 2018 IEEE International Symposium on Technologies for Homeland Security (HST), (2018), pp. 1–7. https://doi.org/10.1109/THS.2018.8574185
A. Okutan, G. Werner, K. McConky, S.J. Yang, POSTER: Cyber attack prediction of threats from unconventional resources (CAPTURE), in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, (2017, Oct), pp. 2563–2565
J.G. De Gooijer, R.J. Hyndman, 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)
N.R. Pokhrel, H. Rodrigo, C.P. Tsokos, Cybersecurity: Time series predictive modeling of vulnerabilities of desktop operating system using linear and non-linear approach. J. Inf. Secur. 8, 362–382 (2017). https://doi.org/10.4236/jis.2017.84023
O. Ben Fredj, A. Mihoub, M. Krichen, O. Cheikhrouhou, A. Derhab, CyberSecurity attack prediction: A deep learning approach, in 13th International Conference on Security of Information and Networks, (2020, Nov), pp. 1–6
M. Al-Qurishi, M. Alrubaian, S.M.M. Rahman, A. Alamri, M.M. Hassan, A prediction system of Sybil attack in social network using deep-regression model. Futur. Gener. Comput. Syst. 87, 743–753 (2018)
X. Fang, M. Xu, S. Xu, P. Zhao, A deep learning framework for predicting cyber attacks rates. EURASIP J. Inf. Secur. 2019(1), 1–11 (2019)
A.E. Ibor, F.A. Oladeji, O.B. Okunoye, O.O. Ekabua, Conceptualisation of cyberattack prediction with deep learning. Cybersecurity 3(1), 1–14 (2020)
R.M. Alguliyev, R.M. Aliguliyev, F.J. Abdullayeva, Deep learning method for prediction of DDoS attacks on social media. Adv. Data Sci. Adap. Anal 11(01n02), 1950002 (2019)
X. Huang, G.C. Fox, S. Serebryakov, A. Mohan, P. Morkisz, D. Dutta, Benchmarking deep learning for time series: Challenges and directions, in 2019 IEEE International Conference on Big Data (Big Data), (IEEE, 2019, Dec), pp. 5679–5682
P. Chronis, G. Giannopoulos, S. Athanasiou, Open issues and challenges on time series forecasting for water consumption, in EDBT/ICDT Workshops, (2016)
G.H. Oliveira, R.C. Cavalcante, G.G. Cabral, L.L. Minku, A.L. Oliveira, Time series forecasting in the presence of concept drift: A pso-based approach, in 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), (IEEE, 2017, Nov), pp. 239–246
P.A. Sánchez-Sánchez, J.R. García-González, L.H.P. Coronell, Encountered problems of time series with neural networks: Models and architectures, in Recent Trends in Artificial Neural Networks-From Training to Prediction, (IntechOpen, 2019)
Z. Liu et al., Forecast methods for time series data: A survey. IEEE Access 9, 91896–91912 (2021)
N. Moustafa, J. Slay, The UNSW-NB15 Dataset. The UNSW-NB15 Dataset | UNSW Research (n.d.). Retrieved 15 Oct 2021 from https://research.unsw.edu.au/projects/unsw-nb15-dataset
G. Holmes, A. Donkin, I.H. Witten, WEKA: A machine learning workbench, in Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference, (Brisbane, QLD, Australia, 1994), pp. 357–361. https://doi.org/10.1109/ANZIIS.1994.396988
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Hammad, M., Altarawneh, K., Almahmood, A. (2024). Cyber Attack Intensity Prediction Using Feature Selection and Machine Learning Models. In: Latifi, S. (eds) ITNG 2024: 21st International Conference on Information Technology-New Generations. ITNG 2024. Advances in Intelligent Systems and Computing, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-031-56599-1_25
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