With the rapid growth in technology, which is
improving every day and becoming more pervasive, smartphone
users also are increasing. These intelligent devices and gadgets
are now being used in automated vehicles, IoT enabled
industries, surveillance, education, entertainment, etc. Android,
Linux kernel based mobile operating system, with its largest
market share is now being used in almost every device that is
capable to do some computation. These devices may include
smartwatches, digital cameras, smart glasses, smart mirrors,
Home Automation System (HAS), Internet of Things (IoT),
Internet of Vehicles (IoV), and many more. Parallel to these
developments, the android based systems also are being targeted
by the cyber attacker by developing more advanced malwares.
Such attacks may harm to the system as well as human life. The
android malwares are evolving day by day and are capable to
escape the traditional security solutions. Therefore, security is
the primary issue of android based system, which requires to be
re-investigated. In this paper, we analyze the pertinence of
machine learning based solutions to detect android malware,
particularly Adware. Logistic Regression (LR), Linear
Discriminant Analysis (LDA), K-Nearest Neighbors (KNN),
Classification And Regression Trees (CART), and Naive Bayes
(NB) machine-learning algorithms are trained and tested for two
scenarios. Scenario A for binary classification and Scenario B is
for multi-class classification. The 60% of the dataset is used to
train the ML algorithms and the remaining 40% is reserved for
the testing. The algorithms are evaluated by using 10-fold crossvalidation method.
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms
