An overview of cyber threats generated by AI
DOI:
10.47709/ijmdsa.v3i4.4753Keywords:
Key words: Cyber security, incident response, disinformation campaigns, machine learning, phishing, ransom ware, deep fake technologies, security assessments, and ethical AI practices are some of the issues that are brought about by AIDimension Badge Record
Abstract
Artificial intelligence's (AI) quick development has drastically changed cyber security by bringing in both sophisticated cyber threats and cutting-edge defenses. This research paper offers a thorough analysis of AI-generated cyber threats, including their mechanics, noteworthy case examples, and countermeasures. The report demonstrates how attackers carry out high-impact assaults, such as automated phishing, ransom ware, and misinformation campaigns, by utilizing AI tools like machine learning, natural language processing, and deep fake technologies. Important case studies highlight the necessity for enterprises to implement proactive and comprehensive security measures by illuminating the practical effects of these risks. Trends suggest that as AI-generated threats develop, they will become more sophisticated and automated due to the rise of autonomous systems that can carry out assaults without the need for human interaction. Organizations are urged to make investments in cutting-edge AI-powered security solutions, promote a cyber-security-aware culture among staff members, and create strong incident response strategies in order to address these changing issues. Enhancing collective defenses against AI-generated cyber threats requires stakeholder collaboration and information sharing, as well as frequent security assessments and adherence to ethical AI principles. This research indicates that a proactive and adaptive strategy to cyber security will be critical in guaranteeing resilience against the increasingly complex threat landscape posed by AI as enterprises traverse the intricacies of the digital era. In an interconnected world, stakeholders can cooperate to protect their assets and uphold public trust by cultivating a culture of continual development and cooperation.
Abstract viewed = 129 times
References
Handa, A. Sharma, and S. K. Shukla, ‘‘Machine learning in cybersecurity: A review,’’ Wiley Interdiscipl. Rev., Data Mining Knowl. Discovery, vol. 9, no. 4, 2019, Art. no. e1306.
M. I. Alghamdi, ‘‘Survey on applications of deep learning and machine learning techniques for cyber security,’’ Int. J. Interact. Mobile Technol. (iJIM), vol. 14, no. 16, p. 210, Sep. 2020.
P. Suresh, K. Logeswaran, P. Keerthika, R. M. Devi, K. Sentamilselvan, G. Kamalam, and H. Muthukrishnan, ‘‘Contemporary survey on effectiveness of machine and deep learning techniques for cyber security,’’ in Machine Learning for Biometrics. Amsterdam, the Netherlands: Elsevier, 2022, pp. 177–200
D. Dasgupta, Z. Akhtar, and S. Sen, ‘‘Machine learning in cybersecurity: A comprehensive survey,’’ J. Defense Model. Simul., Appl., Methodol., Technol., vol. 19, no. 1, pp. 57–106, Jan. 2022.
M. C. Belavagi and B. Muniyal, ‘‘Performance evaluation of supervised machine learning algorithms for intrusion detection,’’ Proc. Comput. Sci., vol. 89, pp. 117–123, Jan. 2016.
H. Singh, ‘‘Performance analysis of unsupervised machine learning techniques for network traffic classification,’’ in Proc. 5th Int. Conf. Adv. Comput. Commun. Technol., Feb. 2015, pp. 401–404.
J. Camacho, G. Maciá-Fernández, N. M. Fuentes-García, and E. Saccenti, ‘‘Semi-supervised multivariate statistical network monitoring for learning security threats,’’ IEEE Trans. Inf. Forensics Security, vol. 14, no. 8, pp. 2179–2189, Aug. 2019.
Verma and V. Ranga, ‘‘Statistical analysis of CIDDS-001 dataset for network intrusion detection systems using distance-based machine learning,’’ Proc. Comput. Sci., vol. 125, pp. 709–716, Jan. 2018.
X. gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, ‘‘an adaptive ensemble machine learning model for intrusion detection,’’ IEEE Access, vol. 7, pp. 82512–82521, 2019.
V. Ford and A. Siraj, ‘‘Applications of machine learning in cyber security,’’ in Proc. 27th Int. Conf. Comput. Appl. Ind. Eng., vol. 118, 2014, pp. 1–6.
Y. Lin, X. Zhu, Z. Zheng, Z. Dou, and R. Zhou, ‘‘The individual identification method of wireless device based on dimensionality reduction and machine learning,’’ J. Supercomput., vol. 75, no. 6, pp. 3010–3027, Jun. 2019.
S. K. Gunturi and D. Sarkar, ‘‘Ensemble machine learning models for the detection of energy theft,’’ Electr. Power Syst. Res., vol. 192, Mar. 2021, Art. No. 106904
Z. He, T. Zhang, and R. B. Lee, ‘‘Machine learning based DDoS attack detection from source side in cloud,’’ in Proc. IEEE 4th Int. Conf. Cyber Secur. Cloud Comput. (CSCloud), Jun. 2017, pp. 114–120.
J. Alsamiri and K. Alsubhi, ‘‘Internet of Things cyber-attacks detection using machine learning,’’ Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 12, pp. 627–634, 2019.
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, p. 754, May 2020.
Shaukat, S. Luo, S. Chen, and D. Liu, ‘‘Cyber threat detection using machine learning techniques: A performance evaluation perspective,’’ in Proc. Int. Conf. Cyber Warfare Secur. (ICCWS), Oct. 2020, pp. 1–6.
T. A. Tuan, H. V. Long, L. H. Son, R. Kumar, I. Priyadarshini, and N. T. K. Son, ‘‘Performance evaluation of botnet DDoS attack detection using machine learning,’’ Evol. Intell., vol. 13, no. 2, pp. 283–294, Jun. 2020.
M. Ozkan-Okay, Ö. Aslan, R. Eryigit, and R. Samet, ‘‘SABADT: Hybrid intrusion detection approach for cyber-attacks identification in WLAN,’’ IEEE Access, vol. 9, pp. 157639–157653, 2021.
Z. A. El Houda, A. S. Hafid, and L. Khoukhi, ‘‘A novel machine learning framework for advanced attack detection using SDN,’’ in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2021, pp. 1–6.
Mihoub, O. B. Fredj, O. Cheikhrouhou, A. Derhab, and M. Krichen, ‘‘Denial of service attack detection and mitigation for Internet of Things using looking-back-enabled machine learning techniques,’’ Comput. Electr. Eng., vol. 98, Mar. 2022, Art. No. 107716.
Makkar and N. Kumar, ‘‘an efficient deep learning-based scheme for Web spam detection in IoT environment,’’ Future Gener. Comput. Syst., vol. 108, pp. 467–487, Jul. 2020.
M. Waqas, K. Kumar, A. A. Laghari, U. Saeed, M. M. Rind, A. A. Shaikh, F. Hussain, A. Rai, and A. Q. Qazi, ‘‘Botnet attack detection in Internet of Things devices over cloud environment via machine learning,’’ Concurrency Comput., Pract. Exper. vol. 34, no. 4, Feb. 2022, Art. no. e6662.
F. Alrowais, S. Althahabi, S. S. Alotaibi, A. Mohamed, M. A. Hamza, and R. Marzouk, ‘‘Automated machine learning enabled cyber security threat detection in Internet of Things environment,’’ Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 687–700, 2023.
M. Roopak, G. Yun Tian, and J. Chambers, ‘‘Deep learning models for cyber security in IoT networks,’’ in Proc. IEEE 9th Annu. Comput. Commun. Workshop Conf. (CCWC), Jan. 2019, pp. 0452–0457.
R. Bernard. (2019). Deep Learning to the Rescue. [Online]. Available: https://www.go-rbcs.com/columns/deep-learning-to-the-rescue
Ö. Aslan and A. A. Yilmaz, ‘‘A new malware classification framework based on deep learning algorithms,’’ IEEE Access, vol. 9, pp. 87936–87951, 2021.
Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan, ‘‘Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,’’ J. Big Data, vol. 8, no. 1, pp. 1–74, Mar. 2021. [
Canziani, A. Paszke, and E. Culurciello, ‘‘an analysis of deep neural network models for practical applications,’’ 2016, arXiv: 1605.07678
Gulli and S. Pal, Deep Learning With Keras. Mumbai, India: Packt Publishing Ltd, 2017.
Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, ‘‘Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,’’ J. Inf. Secur. Appl., vol. 50, Feb. 2020, Art. No. 102419.
Fischer and C. Igel, ‘‘an introduction to restricted Boltzmann machines,’’ in Proc. Iberoamer. Congr. Pattern Recognit. Cham, Switzerland: Springer, 2012, pp. 14–36.
Krizhevsky, I. Sutskever, and G. E. Hinton, ‘‘ImageNet classification with deep convolutional neural networks,’’ Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017.
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, ‘‘Recent advances in convolutional neural networks,’’ Pattern Recognit., vol. 77, pp. 354–377, May 2018.
G. Li, M. Zhang, J. Li, F. Lv, and G. Tong, ‘‘Efficient densely connected convolutional neural networks,’’ Pattern Recognit., vol. 109, Jan. 2021, Art. No. 107610
Yu, T. Quan, Q. Peng, X. Yu, and L. Liu, ‘‘A model-based collaborate filtering algorithm based on stacked AutoEncoder,’’ Neural Comput. Appl., vol. 34, no. 4, pp. 2503–2511, Feb. 2022.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, ‘‘Improved techniques for training GANs,’’ in Proc. Adv. Neural Inf. Process. Syst., vol. 29, 2016, pp. 2234–2242.
J. Ho and S. Ermon, ‘‘Generative adversarial imitation learning,’’ in Proc. Adv. Neural Inf. Process. Syst., vol. 29, 2016, pp. 4565–4573. [76] Q. Wang, Y. Ji, Y. Hao, and J. Cao, ‘‘GRL: Knowledge graph completion with GAN-based reinforcement learning,’’ Knowl.-Based Syst., vol. 209, Dec. 2020, Art. No. 106421.
G. Zhang, Y. Pan, and L. Zhang, ‘‘Semi-supervised learning with GAN for automatic defect detection from images,’’ Autom. Construct. vol. 128, Aug. 2021, Art. No. 103764
H. Sadr, M. M. Pedram, and M. Teshnehlab, ‘‘A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks,’’ Neural Process. Lett., vol. 50, no. 3, pp. 2745–2761, Dec. 2019
R. Socher, C. C. Lin, A. Y. Ng, and C. D. Manning, ‘‘Parsing natural scenes and natural language with recursive neural networks,’’ in Proc. ICML, 2011, pp. 129–136.
D. Akgun, S. Hizal, and U. Cavusoglu, ‘‘A new DDoS attacks intrusion detection model based on deep learning for cybersecurity,’’ Comput. Secur., vol. 118, Jul. 2022, Art. No. 102748
Ferrag, O. Friha, L. Maglaras, H. Janicke, and L. Shu, ‘‘Federated deep learning for cyber security in the Internet of Things: Concepts, applications, and experimental analysis,’’ IEEE Access, vol. 9, pp. 138509–138542, 2021.
H. Suryotrisongko and Y. Musashi, ‘‘Evaluating hybrid quantum-classical deep learning for cybersecurity botnet DGA detection,’’ Proc. Comput. Sci., vol. 197, pp. 223–229, Jan. 2022. VOLUME 12, 2024 12253 M. Ozkan-Okay et al.: Comprehensive Survey: Evaluating the Efficiency of AI and ML Techniques
T. H. H. Aldhyani and H. Alkahtani, ‘‘Attacks to automatous vehicles: A deep learning algorithm for cybersecurity,’’ Sensors, vol. 22, no. 1, p. 360, Jan. 2022
Ben Fredj, A. Mihoub, M. Krichen, O. Cheikhrouhou, and A. Derhab, ‘‘CyberSecurity attack prediction: A deep learning approach,’’ in Proc. 13th Int. Conf. Secur. Inf. Netw., Nov. 2020, pp. 1–6.
Ö. Aslan, ‘‘Separating malicious from benign software using deep learning algorithm,’’ Electronics, vol. 12, no. 8, p. 1861, Apr. 2023
Ö. A. Aslan and R. Samet, ‘‘A comprehensive review on malware detection approaches,’’ IEEE Access, vol. 8, pp. 6249–6271, 2020.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2018.
K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, ‘‘Deep reinforcement learning: A brief survey,’’ IEEE Signal Process. Mag., vol. 34, no. 6, pp. 26–38, Nov. 2017.
Dayan and Y. Niv, ‘‘Reinforcement learning: The good, the bad and the ugly,’’ Current Opinion Neurobiol., vol. 18, no. 2, pp. 185–196, Apr. 2008
V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, ‘‘an introduction to deep reinforcement learning,’’ Found. Trends Mach. Learn., vol. 11, nos. 3–4, pp. 219–354, 2018.
Downloads
ARTICLE Published HISTORY
Accepted Date: 2024-09-30
Published Date: 2024-10-01
Issue
Section
License
Copyright (c) 2024 Aftab Arif, Muhammad Ismaeel Khan, Ali Khan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
