The Use of AI to Analyze Social Media Attacks for Predictive Analytics
DOI:
https://doi.org/10.62411/jcta.10120Keywords:
Artificial Neural Network, Cybersecurity, Machine Learning, Random Forest Classifier, Social Engineering AttackAbstract
Social engineering (SE) presents weaknesses that are difficult to quantify in penetration testing directly. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. SE in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes various posts, including text, images, and videos. It was compiled over a period of several months. Then carefully curated to ensure that it is representative of the types of content that are typically posted on social media. First, using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Thus, the proposed SEA model yielded a classification precision of 0.82 and a recall of 0.79.References
H. Aldawood and G. Skinner, “Educating and Raising Awareness on Cyber Security Social Engineering: A Literature Review,” no. December, pp. 62–68, 2019, doi: 10.1109/tale.2018.8615293.
S. Tanwar, T. Paul, K. Singh, M. Joshi, and A. Rana, “Classification and Imapct of Cyber Threats in India: A review,” in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Jun. 2020, pp. 129–135. doi: 10.1109/ICRITO48877.2020.9198024.
T. O. Akande, O. O. Alabi, and J. B. Oyinloye, “A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis,” J. Comput. Theor. Appl., vol. 2, no. 2, pp. 148–168, Mar. 2024, doi: 10.62411/jcta.10125.
S. A. Ajagbe and M. O. Adigun, “Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review,” Multimed. Tools Appl., vol. 83, no. 2, pp. 5893–5927, Jan. 2024, doi: 10.1007/s11042-023-15805-z.
S. Dasgupta, A. Piplai, A. Kotal, and A. Joshi, “A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity,” in 2020 IEEE International Conference on Big Data (Big Data), Dec. 2020, pp. 2596–2604. doi: 10.1109/BigData50022.2020.9378482.
C. Lorenzen, R. Agrawal, and J. King, “Determining Viability of Deep Learning on Cybersecurity Log Analytics,” in 2018 IEEE International Conference on Big Data (Big Data), Dec. 2018, no. April, pp. 4806–4811. doi: 10.1109/BigData.2018.8622165.
D. Gumusbas, T. Yldrm, A. Genovese, and F. Scotti, “A Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems,” IEEE Syst. J., vol. 15, no. 2, pp. 1717–1731, Jun. 2021, doi: 10.1109/JSYST.2020.2992966.
K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, and M. Xu, “A Survey on Machine Learning Techniques for Cyber Security in the Last Decade,” IEEE Access, vol. 8, pp. 222310–222354, 2020, doi: 10.1109/ACCESS.2020.3041951.
Temitope S. Adekunle; Morolake O. Lawrence; Oluwaseyi O. Alabi; Adenrele A. Afolorunso; Godwin N. Ebong; Matthew A. Oladipupo, “Deep Learning for Plant Disease Detection,” Comput. Sci. Inf. Technol., vol. 5, no. 1, pp. 49–56, 2023, doi: 10.11591/csit.v5i1.pp49-56.
A. Algarni, Y. Xu, and T. Chan, “An empirical study on the susceptibility to social engineering in social networking sites: the case of Facebook,” Eur. J. Inf. Syst., vol. 26, no. 6, pp. 661–687, Nov. 2017, doi: 10.1057/s41303-017-0057-y.
T. Bakhshi, “Social engineering: Revisiting end-user awareness and susceptibility to classic attack vectors,” in 2017 13th International Conference on Emerging Technologies (ICET), Dec. 2017, vol. 2018-Janua, pp. 1–6. doi: 10.1109/ICET.2017.8281653.
R. Naidoo, “A multi-level influence model of COVID-19 themed cybercrime,” Eur. J. Inf. Syst., vol. 29, no. 3, pp. 306–321, May 2020, doi: 10.1080/0960085X.2020.1771222.
A. Dan and S. Gupta, “Social Engineering Attack Detection and Data Protection Model (SEADDPM),” in Advances in Intelligent Systems and Computing, vol. 811, no. January, Springer Singapore, 2019, pp. 15–24. doi: 10.1007/978-981-13-1544-2_2.
A. de Coning and F. Mouton, “Water Distribution Network Leak Detection Management,” in Proceedings of the 19th European Conference on Cyber Warfare, Jun. 2020, vol. 2020-June, no. June, pp. 89–97. doi: 10.34190/EWS.20.088.
D. Shafiei, S. A. Mostafavi, and S. J. Mehrabadi, “Geometrical optimization of city gate station’s water bath indirect heater to minimization of fuel consumption,” J. Therm. Eng., vol. 9, no. 4, pp. 841–860, Aug. 2023, doi: 10.18186/thermal.1325287.
J. S. Giboney, R. M. Schuetzler, and G. M. Grimes, “Developing a measure of adversarial thinking in social engineering scenarios,” in Proceedings of the 16th Pre-ICIS Workshop on Information Security and Privacy, 2021, pp. 1–15.
D. Berman, A. Buczak, J. Chavis, and C. Corbett, “A Survey of Deep Learning Methods for Cyber Security,” Information, vol. 10, no. 4, p. 122, Apr. 2019, doi: 10.3390/info10040122.
Y. Wu, D. Wei, and J. Feng, “Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey,” Secur. Commun. Networks, vol. 2020, pp. 1–17, Aug. 2020, doi: 10.1155/2020/8872923.
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, no. c, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.
T.-T.-H. Le, J. Kim, and H. Kim, “An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization,” in 2017 International Conference on Platform Technology and Service (PlatCon), Feb. 2017, no. February, pp. 1–6. doi: 10.1109/PlatCon.2017.7883684.
T. Zhang et al., “Winglet design for vertical axis wind turbines based on a design of experiment and CFD approach,” Energy Convers. Manag., vol. 195, no. February, pp. 712–726, Sep. 2019, doi: 10.1016/j.enconman.2019.05.055.
W. Alexan, E. Mamdouh, M. Elbeltagy, A. Ashraf, M. Moustafa, and H. Al-Qurashi, “Social Engineering and Technical Security Fusion,” Int. Telecommun. Conf. ITC-Egypt 2022 - Proc., no. August, 2022, doi: 10.1109/ITC-Egypt55520.2022.9855761.
Y. Aun, M.-L. Gan, N. Haliza Binti Abdul Wahab, and G. Hock Guan, “Social Engineering Attack Classifications on Social Media Using Deep Learning,” Comput. Mater. Contin., vol. 74, no. 3, pp. 4917–4931, 2023, doi: 10.32604/cmc.2023.032373.
A. Aljuhani and A. Alhubaishy, “Incorporating a Decision Support Approach within the Agile Mobile Application Development Process,” in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Mar. 2020, pp. 1–6. doi: 10.1109/ICCAIS48893.2020.9096751.
Z. Luo, W. Cai, Y. Li, and D. Peng, “The correlation between social tie and reciprocity in social media,” in Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, Aug. 2011, vol. 8, pp. 3909–3911. doi: 10.1109/EMEIT.2011.6023913.
O. S. Ojo, M. O. Oyediran, B. J. Bamgbade, A. E. Adeniyi, G. N. Ebong, and S. A. Ajagbe, “Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System,” ParadigmPlus, vol. 4, no. 1, pp. 18–28, Apr. 2023, doi: 10.55969/paradigmplus.v4n1a2.
S. A. Ajagbe, A. A. Adegun, A. B. Olanrewaju, J. B. Oladosu, and M. O. Adigun, “Performance investigation of two-stage detection techniques using traffic light detection dataset,” IAES Int. J. Artif. Intell., vol. 12, no. 4, p. 1909, Dec. 2023, doi: 10.11591/ijai.v12.i4.pp1909-1919.
S. A. Ajagbe, O. A. Adeaga, O. O. Alabi, G. O. Ogunsiji, I. O. Oladejo, and M. O. Adigun, “An Alcohol Driver Detection System Examination Using Virtual Instruments,” J. Hunan Univ. Nat. Sci., vol. 50, no. 11, 2023, doi: 10.55463/issn.1674-2974.50.11.4.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Oluwaseyi Omotayo Alabi
This work is licensed under a Creative Commons Attribution 4.0 International License.
