PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)

The number of cyber-attacks is increasing daily, and attackers are coming up with new ways to harm their target by disseminating viruses other malware. With inventions technologies appearing there a chance that system might be attacked its weaknesses taken advantage of. Malware distributed through Portable Document Format (PDF) files, among methods. These files’ adaptability makes them prime for who can quickly insert malware into PDF files. This study proposes model based on the Fuzzy Unordered Rule Induction Algorithm (FURIA) detect proposed outperforms currently used methods in terms reducing error rates accuracy. Other models, such as Naïve Bayes (NB), Decision Tree (J48), Hoeffding (HT), Quadratic Discriminant Analysis (QDA), were compared model. accuracy achieved 99.81%, an rate 0.0022.