Security Awareness Strategy for Phishing Email Scams: A Case Study One of a Company in Singapore

Widia Febriyani - Telkom University,Bandung, 40257, Indonesia
Dhiya Fathia - Telkom University,Bandung, 40257, Indonesia
Adityas Widjajarto - Telkom University,Bandung, 40257, Indonesia
Muharman Lubis - Telkom University,Bandung, 40257, Indonesia

Citation Format:



Social Engineering Procedures and phishing are some of the standard procedures and problems today, mainly through sophisticated media such as email, the official means of communication companies use. Phishing emails are usually associated with Social Designing. They can be sent via joins and connections in this email, but they are not secure. Proliferation can be hacked into private/confidential data or total control over the computer/Email without the client's knowledge. The method used in this research is a cycle that will run continuously in a life cycle, starting from problem identification, then generating ideas and evaluating the Implementation of solutions. At each stage, a thorough checking process is needed to obtain results. Follow what you want. Achieved. The results of this study provide recommendations and some suggestions that companies can make; this aims to be one of the doors that provides restrictions for access from parties who are not entitled to access the application. Some thought has shown that this attack is growing and affecting the population. The evaluation stages in this study consist of 5 phases. Each phase is a step used to prevent both the system and the behavior in the company. Awareness is critical at the start considering this is the basis for the organization to determine who will take care of the personnel's knowledge related to information security. It thinks about using survey writing strategies and recommendations that can be made in anticipation of an attack, such as setting up representation or attention as early and often as possible.


Social Engineering; Awareness; Problems; Control; Email Phishing; Prevention

Full Text:



B. Cao, J. Zhao, Y. Gu, S. Fan, and P. Yang, “Security-Aware Industrial Wireless Sensor Network Deployment Optimization,” IEEE Trans. Ind. Informatics, vol. 16, no. 8, pp. 5309–5316, 2020, doi: 10.1109/TII.2019.2961340.

A. A. Al Shamsi, “Effectiveness of Cyber Security Awareness Program for young children: A Case Study in UAE Effectiveness of Cyber Security Awareness Program for young children View project Sentiment Analysis for Arabic Dialects View project Effectiveness of Cyber Security Awareness Program for young children: A Case Study in UAE,” Int. J. Inf. Technol. Lang. Stud., vol. 3, no. 2, pp. 8–29, 2019, doi: 10.13140/RG.2.2.28488.14083.

M. Sas, G. Reniers, K. Ponnet, and W. Hardyns, “The impact of training sessions on physical security awareness: Measuring employees’ knowledge, attitude and self-reported behaviour,” Saf. Sci., vol. 144, p. 105447, 2021, doi:

A. Wiley, A. McCormac, and D. Calic, “More than the individual: Examining the relationship between culture and Information Security Awareness,” Comput. Secur., vol. 88, p. 101640, 2020, doi:

H. Aldawood and G. Skinner, “Educating and Raising Awareness on Cyber Security Social Engineering: A Literature Review,” Proc. 2018 IEEE Int. Conf. Teaching, Assessment, Learn. Eng. TALE 2018, no. December, pp. 62–68, 2019, doi: 10.1109/TALE.2018.8615162.

F. Salahdine and N. Kaabouch, “Social engineering attacks: A survey,” Futur. Internet, vol. 11, no. 4, 2019, doi: 10.3390/FI11040089.

N. N. Pokrovskaia and S. O. Snisarenko, “Social engineering and digital technologies for the security of the social capital’ development,” Proc. 2017 Int. Conf. "Quality Manag. Transp. Inf. Secur. Inf. Technol. IT QM IS 2017, pp. 16–18, 2017, doi: 10.1109/ITMQIS.2017.8085750.

A. M. Aroyo, F. Rea, G. Sandini, and A. Sciutti, “Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble?,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3701–3708, 2018, doi: 10.1109/LRA.2018.2856272.

M. Chargo, “You’ve Been Hacked: How to Better Incentivize Corporations to Protect Consumers’ Data Michael,” Tennessee J. Bus. Law, vol. 20, pp. 6–23, 2004.

J. Tan, W. X. Tee, A. Parsons, and A. Radlett, “Asean cyberthreat assessment 2021,” Interpol, p. 5, 2021, [Online]. Available: Cyberthreat Assessment 2021 - final.pdf.

M. Zwilling, G. Klien, D. Lesjak, Ł. Wiechetek, F. Cetin, and H. N. Basim, “Cyber Security Awareness, Knowledge and Behavior: A Comparative Study,” J. Comput. Inf. Syst., vol. 62, no. 1, pp. 82–97, Jan. 2022, doi: 10.1080/08874417.2020.1712269.

G. Costantino, A. La Marra, F. Martinelli, and I. Matteucci, “CANDY: A social engineering attack to leak information from infotainment system,” IEEE Veh. Technol. Conf., vol. 2018-June, pp. 1–5, 2018, doi: 10.1109/VTCSpring.2018.8417879.

A. Birajdar and T. N. N., “APPEARS Framework for evaluating Gamified Cyber Security Awareness Training,” in 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), 2022, pp. 1–8, doi: 10.1109/IC3SIS54991.2022.9885399.

I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00318-5.

A. Ključnikov, L. Mura, and D. Sklenár, “Information security management in smes: Factors of success,” Entrep. Sustain. Issues, vol. 6, no. 4, pp. 2081–2094, 2019, doi: 10.9770/jesi.2019.6.4(37).

K. Khando, S. Gao, S. M. Islam, and A. Salman, “Enhancing employees information security awareness in private and public organisations: A systematic literature review,” Comput. Secur., vol. 106, p. 102267, 2021, doi:

S. AlGhamdi, K. T. Win, and E. Vlahu-Gjorgievska, “Information security governance challenges and critical success factors: Systematic review,” Comput. Secur., vol. 99, p. 102030, 2020, doi:

B. Ghimire and D. B. Rawat, “Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things,” IEEE Internet Things J., vol. 9, no. 11, pp. 8229–8249, 2022, doi: 10.1109/JIOT.2022.3150363.

A. Corallo, M. Lazoi, and M. Lezzi, “Cybersecurity in the context of industry 4.0: A structured classification of critical assets and business impacts,” Comput. Ind., vol. 114, p. 103165, 2020, doi:

A. K. Jain and B. B. Gupta, “A survey of phishing attack techniques, defence mechanisms and open research challenges,” Enterp. Inf. Syst., vol. 16, no. 4, pp. 527–565, Apr. 2022, doi: 10.1080/17517575.2021.1896786.

Y. A. Alsariera, V. E. Adeyemo, A. O. Balogun, and A. K. Alazzawi, “AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites,” IEEE Access, vol. 8, pp. 142532–142542, 2020, doi: 10.1109/ACCESS.2020.3013699.

Z. Alkhalil, C. Hewage, L. Nawaf, and I. Khan, “Phishing Attacks: A Recent Comprehensive Study and a New Anatomy,” Front. Comput. Sci., vol. 3, no. March, pp. 1–23, 2021, doi: 10.3389/fcomp.2021.563060.

R. Alabdan, “Phishing attacks survey: Types, vectors, and technical approaches,” Futur. Internet, vol. 12, no. 10, pp. 1–39, 2020, doi: 10.3390/fi12100168.

A. Basit, M. Zafar, X. Liu, A. R. Javed, Z. Jalil, and K. Kifayat, “A comprehensive survey of AI-enabled phishing attacks detection techniques,” Telecommun. Syst., vol. 76, no. 1, pp. 139–154, 2021, doi: 10.1007/s11235-020-00733-2.

R. Wash, “How Experts Detect Phishing Scam Emails,” Proc. ACM Human-Computer Interact., vol. 4, no. CSCW2, 2020, doi: 10.1145/3415231.

J. Wu et al., “Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 52, no. 2, pp. 1156–1166, 2022, doi: 10.1109/TSMC.2020.3016821.

G. Egozi, “Phishing Email Detection Using Robust NLP Techniques,” 2018 IEEE Int. Conf. Data Min. Work., pp. 7–12, 2018, doi: 10.1109/ICDMW.2018.00009.

P. Xia et al., “Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange,” Proc. ACM Meas. Anal. Comput. Syst., vol. 5, no. 3, 2021, doi: 10.1145/3491051.

M. A. Mendoza, “Cibersegurança ou segurança da informação? Explicando a diferença,” 2017.

H. Ahmadian and A. Sabri, “Teknik Penyerangan Phishing Pada Social,” vol. 2, no. 1, 2021.

C. M. R. da Silva, E. L. Feitosa, and V. C. Garcia, “Heuristic-based strategy for Phishing prediction: A survey of URL-based approach,” Comput. Secur., vol. 88, p. 101613, 2020, doi:

F. Mouton, L. Leenen, and H. S. Venter, “Social engineering attack examples, templates and scenarios,” Comput. Secur., vol. 59, pp. 186–209, 2016, doi: 10.1016/j.cose.2016.03.004.

H. Abroshan, J. Devos, G. Poels, and E. Laermans, “Phishing Happens Beyond Technology: The Effects of Human Behaviors and Demographics on Each Step of a Phishing Process,” IEEE Access, vol. 9, pp. 44928–44949, 2021, doi: 10.1109/ACCESS.2021.3066383.

P. L. Gallegos-Segovia, P. E. Vintimilla-Tapia, J. F. Bravo-Torres, I. F. Yuquilima-Albarado, V. M. Larios-Rosillo, and J. D. Jara-Saltos, “Social engineering as an attack vector for ransomware,” 2017 Chil. Conf. Electr. Electron. Eng. Inf. Commun. Technol. CHILECON 2017 - Proc., vol. 2017-Janua, pp. 1–6, 2017, doi: 10.1109/CHILECON.2017.8229528.

C. Technologies, “Feature selection for phishing detection : a review of Hiba Zuhair * Ali Selamat Mazleena Salleh,” vol. 15, no. 2, 2016.

G. Varshney, M. Misra, and P. K. Atrey, “A survey and classi fi cation of web phishing detection schemes,” 2016, doi: 10.1002/sec.

S. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey,” Procedia Comput. Sci., vol. 189, pp. 19–28, 2021, doi:

T. Lin et al., “Susceptibility to Spear-Phishing Emails,” ACM Trans. Comput. Interact., vol. 26, no. 5, pp. 1–28, 2019, doi: 10.1145/3336141.

I. H. Sarker, M. H. Furhad, and R. Nowrozy, “AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions,” SN Comput. Sci., vol. 2, no. 3, pp. 1–18, 2021, doi: 10.1007/s42979-021-00557-0.

A. K. Jain and B. B. Gupta, “A survey of phishing attack techniques, defence mechanisms and open research challenges,” Enterp. Inf. Syst., vol. 00, no. 00, pp. 1–39, 2021, doi: 10.1080/17517575.2021.1896786.

X. Chen, X. Liu, L. Zhang, and C. Tang, “Optimal Defense Strategy Selection for Spear-Phishing Attack Based on a Multistage Signaling Game,” IEEE Access, vol. 7, pp. 19907–19921, 2019, doi: 10.1109/ACCESS.2019.2897724.

I. Vayansky and S. Kumar, “Phishing – challenges and solutions,” Comput. Fraud Secur., vol. 2018, no. 1, pp. 15–20, 2018, doi: 10.1016/S1361-3723(18)30007-1.

N. Farhana, M. Zaharon, and M. M. Ali, “Factors Affecting Awareness of Phishing Among Generation Y,” Asia-Pacific Manag. Account. J., no. April 2021, 2021, [Online]. Available:

S. S. Lin, S. L. Shen, A. Zhou, and Y. S. Xu, “Risk Assessment and Management of Excavation System Based On Fuzzy Set Theory and Machine Learning Methods,” Autom. Constr., vol. 122, no. November 2020, p. 103490, 2021, doi: 10.1016/j.autcon.2020.103490.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.