TrafficPSSF: A Fast and An Effective Malware Detection Under Online and Offline The use of Android phones is becoming more and more widespread, and Android malware is also entering everyonexe2x80x99s field of vision. In this paper, we propose TrafficPSSF as a fast and an effective method for traffic detection and classification under online and offline detection. The traffic collection platform collects traffic data of application. Especially, we design an online detection and offline detection. One of the features of TCP session is the packet size, which is used for online detection. We can detect malicious traffic without waiting for all traffic packets to arrive, which can improve efficiency. Whatxe2x80x99s more, we use combination classifier model for our server to increase the accuracy of malicious detection. In the offline detection, we use seven statistical features of TCP as our model input and random forest algorithm for model training. The experiment shows that the online detection has 98.35% of the malicious detection rate, and the offline detection and classification accuracy rate reach 99.98%.

Network TrafficBased Hybrid Malware Detection for Smartphone and Traditional Networked Systems With the widespread use of the Internet in recent times, security remains one of the major concerns. Malware poses security threats to smartphones, computers, and networks. These threats require an urgent need to build an efficient hybrid intrusion detection system, which can detect malware from smartphone and traditional systems, and ensure minimal damage to the resources of an organization. In this paper, we propose an intelligent and selflearning network trafficbased hybrid malware detection approach (HMDA) for smartphones and traditional systems considering features that show a similar trend in the network traffic. The system could be used by an organizational network to detect and mitigate any occurrence of malwarebased malicious activity inside the network. The proposed HMDA is implemented using machine learning algorithms. We have used ensemble learners to train the model for the HMDA and achieved an accuracy of 95.7% using XGBoost algorithm. The Android traffic captures collected by running the malware dataset have been made publicly available upon request to authors.