… IoT malware (Khraisat, 2019). The specific problem addressed in this study is the detection
of IoT malware using decision tree-… ) by developing a decision tree model that can accurately …

… data sharing in IoT environment which is based on distributed computing. With an intention
to detect the malware in this smart IoT era, this paper enhanced a novel malware detection …

… the application of decision trees to detect IoT malware, addressing the increasing vulnerability
of IoT devices to such attacks. The proliferation and autonomous operation of IoT devices …

… of IoT, cybersecurity threats, particularly malware, have also risen. This research focuses on
detecting malware attacks in IoT … algorithms, specifically Decision Tree and Random Forest. …

… malware prediction [6] for our work. To demonstrate the effectiveness of our approach, we
adopt the decision tree … The results show that ML algorithms in Decision Trees methods could …

… Forest and Decision Tree alongside fuzzy and fast fuzzy pattern tree for malware classification
and … Based on performed experiments, fuzzy tree-based classification algorithms outcome …

… Malware Detection for IoT devices by comparing the performance of different classifiers.
Malware is … Random Forest Algorithm (RFA) and Decision Tree Algorithm (DTA) are two types of …

… (CPS), personal devices such as Internet of Things (IoT) devices, laptop, desktop and … called
as malware. To detect various forms of malware attacks, the effective malware detection (MD…

… In this paper, the preliminary results of the malware detection system using decision tree-…
the malware activity captured by means of network traffic data at the edge of the IoT network …

… [18] proposed an edge malware detection method using a Fuzzy and Fast Fuzzy pattern tree
with SVM, KNN Random Forests, and Decision Tree classifiers. The datasets utilized by the …