.png){kind=icon}
返回
Android Ransomware Detection From Traffic Analysis Using Metaheuristic Feature Selection
.png){kind=icon}
PRE
AI
DOI：10.1109/ACCESS.2022.3227579.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Among the prevalent cyberattacks on Android devices, a ransomware attack is the most common and damaging. Although there are many solutions for detecting Android ransomware attacks, existing solutions have limited detection accuracy and high computational complexity. This paper proposes a new Android ransomware detection method based on traffic analysis to address the limitations. We exploit particle swarm optimization (PSO) to select traffic characteristics. Then, based on the selected traffic features, we classify the data traffic using decision tree and random forest classifiers. We examine ransomware cyberattacks in two distinct circumstances. In the first case, we find ransomware traffic; in the second, we locate a specific form of malware traffic among benign traffic. The proposed PSO-assisted feature selection enables the classifier to improve the detection accuracy significantly. The random forest is found to achieve the highest performance in detecting ransomware, whereas the decision tree is the best for detecting the types of ransomware. The accuracy improvements are 2.26% and 3.7% in the first and second scenarios, respectively. The proposed method removes 56.01% to 91.95% of the features. The proposed method convergences quickly as the optimization reaches an optimum value of about ten iterations.
Keyword:
Feature selection
malware
machine learning
particle swarm optimization
ransomware
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏A Review of Android Malware Detection Approaches Based on Machine Learning基于机器学习的Android恶意软件检测方法综述.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data高度不平衡数据背景下基于混合进化方法的Android勒索软件检测IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容