Toward Eradication of Phishing Attacks in E-government Systems

Musa Midilia Ahmed

Abstract


E-government has revolutionized activities of societies making human life easier. However, despite the numerous benefits of E-government system, the main challenge that accompanied the adoption of this novel innovation is phishing attack, a subset of social engineering attack (SEA). Phishing attackers psychologically manipulate citizens to disclose confidential information. The purpose of this study is to propose solution for eradication of phishing attacks in E-government system. To analyse business activities on the E-government system; information, communication, distribution and transaction (ICDT) model was used to systematically acquire sound knowledge and understanding of internet business activities. This study identified seven types of phishing attacks; standard email phishing, spear phishing, clone phishing, whaling, voice phishing, text-message phishing and angler phishing. in addition, gainful employment of citizens, legislative enactment for punishing phishing scammers as well as enforcement of the law to compel compliance with the law are among the recommendations for eradication of phishing attacks in E-government systems. The author abridged security awareness education, accurate citizens’ authentication, phishing filter, MALWARE detection and prevention. Others are artificial intelligence, machine learning and deep learning methods of anti-phishing attacks in E-government systems. Recommendations for eradication of phishing attacks in E-government system include compliance with the suggested anti-phishing attacks.


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References


Alshaher, A. (2021), "IT Capabilities as a Fundamental of Electronic Government System Success in Developing Countries from Users Perspectives", Transforming Government: People, Process and Policy, 15 (1), 129-149.

Burlacu, S., Patarlageanu, S. R., Diaconu, A., & Ciobanu, G. (2021). E-government in the Era of Globalization and The Health Crisis Caused by the Covid-19 Pandemic, Between Standards And Innovation. In SHS Web of Conferences, EDP Sciences. 92(1), 1-8.

Ahmed, M. M. (2022). Social Engineering Attacks in E-Government System: Detection and Prevention. International Journal of Applied Engineering and Management Letters (IJAEML), 6(1), 6.

Salahdine, F., & Kaabouch, N. (2019). Social Engineering Attacks: A Survey. Future Internt, 11(4), 89-106.

Rathee, D., & Mann, S. (2022). Detection of E-mail Phishing Attacks–Using Machine Learning and Deep Learning. International Journal of Computer Applications, 183(47), 1-7.

Abroshan, H., Devos, J., Poels, G., & Laermans, E. (2021). COVID-19 and Phishing: Effects of Human Emotions, Behavior, and Demographics on the Success of Phishing Attempts during the Pandemic. IEEE Access, 9(1), 121916-121929.

Aljeaid, D., Alzhrani, A., Alrougi, M., & Almalki, O. (2020). Assessment of End-User Susceptibility to Cybersecurity Threats in Saudi Arabia by Simulating Phishing Attacks. Information, 11(12), 547.

Rahim, F. A., & Azman, F. (2020, August). Phishing Attack Simulation: Measuring Susceptibility among Undergraduate Students. In 2020 8th International Conference on Information Technology and Multimedia (ICIMU) IEEE, (pp. 132-137).

Alguliyev, R., Aliguliyev, R., & Yusifov, F. (2018). Role of social networks in E-government: Risks and security threats. Online Journal of Communication and Media Technologies, 8(4), 363-376.

Rastenis, J., Ramanauskaitė, S., Janulevičius, J., Čenys, A., Slotkienė, A., & Pakrijauskas, K. (2020). E-mail-based phishing attack taxonomy. Applied Sciences, 10(7), 1-15.

Basit, A., Zafar, M., Javed, A. R., & Jalil, Z. (2020, November). A novel ensemble machine learning method to detect phishing attack. In 2020 IEEE 23rd International Multitopic Conference (INMIC) IEEE. 1(1). 1-5.

Zabihimayvan, M., & Doran, D. (2019, June). Fuzzy rough set feature selection to enhance phishing attack detection. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1(1), 1-6.

Odeh, A., Keshta, I., & Abdelfattah, E. (2021). PHIBOOST-a novel phishing detection model using Adaptive boosting approach. Jordanian Journal of Computers and Information Technology (JJCIT), 7(1), 64-73.

Rahim, R., Murugan, S., Mostafa, R. R., Dubey, A. K., Regin, R., Kulkarni, V., & Dhanalakshmi, K. S. (2020). Detecting the Phishing Attack Using Collaborative Approach and Secure Login through Dynamic Virtual Passwords. Webology, 17(2), 524-535.

Ravi, R. (2020). A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA). Computer Communications, 153(1), 375-381.

Kara, I. (2021). Don't bite the bait: phishing attack for internet banking (e-banking). The Journal of Digital Forensics, Security and Law: JDFSL, 16, 1-12.

Yao, W., Ding, Y., & Li, X. (2018, December). Deep learning for phishing detection. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom, IEEE, 1(1), 645-650.

Nemane, M. B. S., & Pahurkar, M. R. D. (2021). An Anti-Phishing Strategy Based on Visual Cryptography. International Research Journal of Engineering and Technology (IRJET), 8(2), 1035-1038.

Chen, L. (2019, October). Research on Anti-phishing Strategy of Smart Phone. In Journal of Physics: Conference Series, IOP Publishing, 1314(1), 1-4.

Angehrn, A. (1997). Designing Mature Internet Business Strategies: the ICDT Model. European Management Journal, 15(4), 361-369.

Maurya, S., & Jain, A. (2020). Deep learning to combat phishing. Journal of Statistics and Management Systems,23(6), 945-957.

Deshpande, A., Pedamkar, O., Chaudhary, N., & Borde, S. (2021). Detection of phishing websites using Machine Learning. International Journal of Engineering Research & Technology (IJERT), 10(05), 430-434.

Wang, Z., Ren, Y., Zhu, H., & Sun, L. (2022). Threat detection for general social engineering attack using machine learning techniques. arXiv preprint arXiv:2203.07933, 1(1), 1-16.

Peng, T., Harris, I., & Sawa, Y. (2018, January). Detecting phishing attacks using natural language processing and machine learning. In 2018 ieee 12th international conference on semantic computing (icsc) IEEE. 1(1), 300-301.

Alam, M. N., Sarma, D., Lima, F. F., Saha, I., & Hossain, S. (2020, August). Phishing attacks detection using machine learning approach. In 2020 third international conference on smart systems and inventive technology (ICSSIT) IEEE, 1(1), 1173-1179.

Garvés, I. O., Cazares, M. F., & Andrade, R. O. (2019, December). Detection of phishing attacks with machine learning techniques in cognitive security architecture. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) IEEE, 1(1), 366-370.

Ripa, S. P., Islam, F., & Arifuzzaman, M. (2021, July). The emergence threat of phishing attack and the detection techniques using machine learning models. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) IEEE, (11). 1-6.

Hussain, S., Sarma, D., & Chakma, R. J. (2020). Machine learning-based phishing attack detection. International Journal of Advanced Computer Science and Applications, 11(9), 378-388.

Saha, I., Sarma, D., Chakma, R. J., Alam, M. N., Sultana, A., & Hossain, S. (2020, August). Phishing attacks detection using deep learning approach. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, I (1), 1180-1185.

Maurya, S., & Jain, A. (2020). Deep learning to combat phishing. Journal of Statistics and Management Systems, 23(6), 945-957.

Burita, L., Matoulek, P., Halouzka, K., & Kozak, P. (2021). Analysis of phishing emails. AIMS Electronics and Electrical Engineering, 5(1), 93-116.


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