A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress

The latest approach in cyber threat intelligence (CTI) is becoming increasingly crucial in detecting evolving cyber threats, including malware on Android devices. This problem is getting deeper when the number of Android users in Indonesia is increasing compared to other platforms. Therefore, it is necessary to develop a new method that integrates the CTI framework with the use of machine learning to improve the accuracy and speed in detecting Android malware. By utilizing intelligence data from structured threat reports, this method aims to identify suspicious patterns and abnormal behavior that may not be detected by conventional security systems so that with this updated security system it is proactive towards the development of new types of malware threats, with the hope of being able to make a significant contribution to strengthening especially mobile devices with the Android platform and being able to provide more effective solutions in reducing the risk of malware attacks on Android.

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Published
2024-12-12
DOI
https://doi.org/10.1109/icitsi65188.2024.10929125
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress

Abdiansah Abdiansah, Iwan Pahendra Anto Saputra, Mohd. Yazid Idris, Dian Palupi Rini et al.
Advanced Malware Detection Techniques
article

A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress

Abdiansah Abdiansah, Iwan Pahendra Anto Saputra, Mohd. Yazid Idris, Dian Palupi Rini, Bhakti Yudho Suprapto, Deris Stiawan, Rahmat Budiarto, Andri Wijaya, Sayfudin Sayfudin
article en

Abstract

The latest approach in cyber threat intelligence (CTI) is becoming increasingly crucial in detecting evolving cyber threats, including malware on Android devices. This problem is getting deeper when the number of Android users in Indonesia is increasing compared to other platforms. Therefore, it is necessary to develop a new method that integrates the CTI framework with the use of machine learning to improve the accuracy and speed in detecting Android malware. By utilizing intelligence data from structured threat reports, this method aims to identify suspicious patterns and abnormal behavior that may not be detected by conventional security systems so that with this updated security system it is proactive towards the development of new types of malware threats, with the hope of being able to make a significant contribution to strengthening especially mobile devices with the Android platform and being able to provide more effective solutions in reducing the risk of malware attacks on Android.

Sriwijaya University (ID), Universitas Muhammadiyah Palembang (ID), University of Technology Malaysia (MY), Al Baha University (SA)
Openalex Percentile: Top 68%
Advanced Malware Detection Techniques
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A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress — Abdiansah Abdiansah, Iwan Pahendra Anto Saputra, et al. · (2024) | TGRS Research Map | TGRS