Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

Table 2

Malware detection techniques.

Ref. no.	Description
[41]	It uses four ways to detect malwares. It divides the applications into four types like malicious, benign, aggressive, and risky applications
[42]	Android analysis techniques for evaluating the effectiveness of Android intense
[43]	It uses ADA GRAD optimize algorithm for detecting malware pattern without manual intervention
[44]	It detects malware by using ensemble classifier for malware detection
[45]	It uses the machine learning algorithm which was presented by Waikato environment for knowledge analysis (WEKA)
[46]	It uses machine learning method for Android malware detection
[47]	It detects application features and decides whether malicious or not
[48]	It uses multiflow detection algorithm based on information flow analysis