While arming AI and machine learning for cybersecurity is still in its early stages, large-scale firms and organizations continuously develop autonomous defense capabilities that include AI and machine learning to protect their security systems and applications. In addition, intelligent cyber attackers have started to use independent AI algorithms, continuously developing their capabilities due to the advantages of automatically uncovering new security vulnerabilities for achieving their illegal goals. For this reason, attack tools that learn by themselves, automatically scan vulnerabilities, discover proper techniques to exploit and disable firewalls and attack directly have become very sophisticated. On the other hand, AI can play a critical role in risk management and providing more effective and agile service by automatically detecting risks and control vulnerabilities in the dynamic IT environment and reporting their probability and impact degrees. Therefore, while risk management can become more effective with AI, the risks exposed through AI have also become more sophisticated. This study investigates from literature and sectoral reports the role of AI in cybercrime and cybersecurity and the manageability of risks in the cyber area through AI algorithms. The study explores how serious the AI-based dangers and threats are and how they can help improve an organization's security posture and risk appetite against AI-powered advanced persistent threats (APT).
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Yapay zekayı (YZ) ve makine öğrenimini siber güvenlik için silahlandırmak hala erken aşamalarda olsa da büyük ölçekli firmalar ve kuruluşlar, güvenlik sistemlerini ve uygulamalarını korumak için YZ ve makine öğrenimini içeren özerk savunma yeteneklerini geliştirmeye çalışmaktadırlar. Bunun yanı sıra, siber saldırganlar da yetenek ve araçlarını sürekli geliştirirken yeni güvenlik açıklarını ortaya çıkarmak ve yasa dışı amaçlarına ulaşmak için sağladığı avantajlardan dolayı otonom YZ algoritmalarını kullanmaya başlamışlardır. Bu nedenle kendi kendisine öğrenen, zafiyetleri otomatik olarak tarayarak hangi tekniklerle suiistimal yapılmasının ve güvenlik duvarlarının etkisiz hale getirilebileceğinin nasıl olanaklı olduğunu raporlayan ve/veya doğrudan saldırıya geçebilen otonom saldırı araçları büyük bir risk olarak çok sofistike hale gelmiştir. Buna karşın dinamik BT ortamındaki riskleri ve kontrol zafiyetlerini otomatik olarak algılayarak ve bunların olasılık ve etki derecelerini raporlayarak risk yönetiminin de daha etkili olarak güvenlik ve savunma hizmetine destek sağlamasında da YZ kritik roller oynayabilmektedir. Dolayısıyla YZ ile risk yönetimi daha etkin hale gelebilirken YZ üzerinden maruz kalınan riskler de daha sofistike hale gelmiştir. Bu çalışma, YZ’ nin siber suç ve siber güvenlikteki rolünü, bu alandaki risklerin YZ üzerinden yönetilebilirliğini literatür ve sektörel raporların incelenmesi yoluyla araştırmaktadır. Çalışmada, YZ tabanlı risk ve tehditlerin ne kadar ciddi olduğu yanı sıra, bir kuruluşun YZ destekli gelişmiş kalıcı tehditlere (APT) karşı güvenlik duruşunu ve risk iştahını iyileştirmeye nasıl yardımcı olunabileceği teknik olarak ortaya konulmaktadır.
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Blanco-Filgueira B, Garcia-Lesta D, Fernandez-Sanjurjo M, Brea VM, Lopez P (2019) Deep learning-based multiple object visual tracking on embedded system for IoT and mobile edge computing applications. IEEE Internet Things J 6(3):5423–5431. https://doi.org/10.1109/JIOT.2019. 2902141
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Cui Q, Gong Z, Ni W, Hou Y, Chen X, Tao X, Zhang P (2019) Stochastic online learning for mobile edge computing: learning from changes. IEEE Commun Mag 57(3):63–69. https://doi.org/10.1109/ MCOM. 2019.1800644
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Demertzis K., Iliadis L. (2015) A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security. In: Daras N., Rassias M. (eds) Computation, Cryptography, and Network Security. Springer, Cham. https://doi.org/10.1007/978-3-319-18275-9_7
Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for Internet of Things. Futur Gener Comput Syst 82:761–768. https://doi.org/10.1016/ j.future.2017.08. 043
FAIR (2017) What is a cyber value-at-risk model? http://www.fairinstitute.org /blog/what-is-a-cyber-value-at-risk-model
Ganti, V. (2018). How Machine Learning and AI in Cybersecurity is Shaping IT, Biztech Magazine, https://biztechmagazine.com/article/2018/06/role-artificial-intelligence-cybersecurity
Gebremariam, A. A., Usman, M., & Qaraqe, M. (2019) Applications of artificial intelligence and machine learning in the area of SDN and NFV: a survey. 16th international multi-conference on systems, sig nals and devices, SSD 2019, 545–549. https://doi.org/10.1109/SSD. 2019.8893244
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Guo Y., Cao H., Han S., Sun Y., Bai Y. (2018) Spectral-spatial hyperspectral image classification with K-nearest neighbor and guided filter. IEEE Access 6:18582–18591. https://doi.org/10.1109/ ACCESS.2018. 2820043
Hu R., Wen S., Zeng Z., Huang T. (2017) A short-term power load fore casting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31. https://doi.org/10.1016/j.neucom. 2016.09.027
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Intercept, (2020) Stop Unknown Threats, Sophos, https://www.sophos.com/en-us/medialibrary/PDFs/factsheets/sophos-intercept-x-dsna.pdf
Kaloudi N. & Li J., (2020). The AI-Based Cyber Threat Landscape: A Survey. ACM Comput. Surv. 53, 1, Article 20 (May 2020), 34 pages. DOI: https://doi.org/10.1145/3372823
Küçük, D, Arıcı, N . (2018). Doğal dil işlemede derin öğrenme uygulamaları üzerine bir literatür çalışması. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 2 (2) , 76-86 . Retrieved from https://dergipark.org.tr/tr/pub/uybisbbd/issue/41787/443574
Li H., Ota K. & Dong M. (2018) Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Netw 32(1):96–101. https://doi.org/10.1109/ MNET.2018.1700202
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Malhotra Y. (2018) Cognitive computing for anticipatory risk analytics in intelligence, surveillance, & reconnaissance (ISR): model risk management in artificial intelligence & machine learning (presentation slides). SSRN Electron J. https://doi.org/10.2139/ssrn.3111837
Newman, L. H., (2018) AI Can Help Cybersecurity—If It Can Fight Through the Hype, Wired, https://www.wired.com/story/ai-machine-learning-cybersecurity/
Nguyen T.G., Phan TV, Nguyen BT, So-In C, Baig ZA, Sanguanpong S (2019) SeArch: a collaborative and intelligent NIDS architecture for SDN-based cloud IoT networks. IEEE Access 7:107678–107694. https://doi.org/10.1109/ACCESS.2019.2932438
Park D., Kim S., An Y., Jung J-Y. (2018) LiReD: a light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks. Sensors 18(7):2110. https://doi.org/ 10.3390/s18072110
Porambage, P., Kumar, T., Liyanage, M., Partala, J., Lovén, L., Ylianttila, M., & Seppänen, T. (2019) Sec-edgeAI: AI for edge security vs. security for edge AI BrainICU-measuring brain function during intensive care view project ECG-based emotion recognition view project Sec-EdgeAI. https://www.researchgate.net/publication/330838792
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