To deal with the large number of malicious mobile applications (e.g. mobile
malware), a number of malware detection systems have been proposed in the
literature. In this paper, we propose a hybrid method to find the optimum
parameters that can be used to facilitate mobile malware identification.We also
present a multi agent system architecture comprising three system agents (i.e.
sniffer, extraction and selection agent) to capture and manage the pcap file for
data preparation phase. In our hybrid approach, we combine an adaptive neuro
fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations
using data captured on a real-world Android device and the MalGenome dataset
demonstrate the effectiveness of our approach, in comparison to two hybrid
optimization methods which are differential evolution (ANFIS-DE) and ant colony
optimization (ANFIS-ACO).

제 1편 : SIMULINK 기본편
1.1 SIMULINK의 시작 1
블록의 연결 5
블록 파라미터의 설정 7
시뮬레이션 파라미터 (Configuration Parameters)의 설정 8
시뮬레이션의 수행 9
블록 파라미터의 표시 9
복수 데이터의 표시 11
2.2 동적 시뮬레이션 13
이차 미분방정식 17
선형 상태변수 모델 23
DC 모터의 시뮬레이션 24
함수 블록의 사용 29
차분방정식(difference equation)의 모델링 34
Subsystem(부시스템)의 구성 37

제 2편 : 연구논문
DyHAP: Dynamic Hybrid ANFIS-PSO Approach for
Predicting Mobile Malware

1. Introduction 41
2. RelatedWork 42
3. Research Methodology 44
4. Feature Selection, Extraction and Labelling 45
5. Proposed Approach 46
6. Evaluations 53
7. Conclusion 55
8. References 58