RANCANG BANGUN APLIKASI DETEKSI MALWARE BERBASIS COLLECTIVE INTELLIGENCE FRAMEWORK (CIF) PADA HONEYPOT

Restu Pratama, Nila Feby Puspitasari
UNIVERSITAS AMIKOM YOGYAKARTA.2018

A B S T R A C T

The development of malware is increasing and varies from year to year. Not only targeting computer systems as a victim, malware also attacks smartphones and IoT devices. According to a survey conducted by Symantec, by 2016 there are 357 million new types of malware that actively attack computer and smartphone systems. Some precautions have been done in various instances including by installing a honeypot to capture the malware that is attacking through a computer network.

The use of honeypot to capture malware on computer networks is considered quite effective because in addition to getting a copy of malware, honeypot can also record information about the sender of malware. However, the ability of honeypot in detecting malware is still very simple so that the application needed to improve the ability of honeypot in detecting malware.

Applications created in this study are able to improve the ability of honeypot in detecting malware by utilizing collective intelligence framework (CIF). CIF not only detects malware hash but also the sender ip address so the attack can be prevented. The results obtained are malware attacks can be detected and prevented even with a small percentage of detection.

Keyword: malware, honeypot, threat intelligence, dionaea, CIF.

CategoryUndergraduate Thesis
Posted Date29 Maret 2018
Modified Date29 Maret 2018
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