| Title | Static Malware Analysis using PE Header files API |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Aggarwal, Naman, Aggarwal, Pradyuman, Gupta, Rahul |
| Conference Name | 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) |
| Keywords | Analytical models, API, composability, Computational modeling, Human Behavior, logistic regression, Malware, Market research, Neural Network, Neural networks, phishing, pubcrawl, Resiliency, static analysis, Support vector machines, SVM |
| Abstract | In today's fast pacing world, cybercrimes have time and again proved to be one of the biggest hindrances in national development. According to recent trends, most of the times the victim's data is breached by trapping it in a phishing attack. Security and privacy of user's data has become a matter of tremendous concern. In order to address this problem and to protect the naive user's data, a tool which may help to identify whether a window executable is malicious or not by doing static analysis on it has been proposed. As well as a comparative study has been performed by implementing different classification models like Logistic Regression, Neural Network, SVM. The static analysis approach used takes into parameters of the executables, analysis of properties obtained from PE Section Headers i.e. API calls. Comparing different model will provide the best model to be used for static malware analysis |
| DOI | 10.1109/ICCMC53470.2022.9753899 |
| Citation Key | aggarwal_static_2022 |
Static Malware Analysis using PE Header files API