In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

With the growing era of the Internet of Things (IoT), more and more devices are connecting with the Internet using android applications to provide various services. The IoT devices are used for sensing, controlling and monitoring of different processes. Most of IoT devices use Android applications for communication and data exchange. Therefore, a secure Android permission privileged mechanism is required to increase the security of apps. According to a recent study, a malicious Android application is developed almost every 10 s. To resist this serious malware campaign, we need effective malware detection approaches to identify malware applications effectively and efficiently. Most of the studies focused on detecting malware based on static and dynamic analysis of the applications. However, to analyse the risky permission at runtime is a challenging task. In this study, first, we proposed a novel approach to distinguish between malware and benign applications based on permission ranking, similarity-based permission feature selection, and association rule for permission mining. Secondly, the proposed methodology also includes the enhancement of the random forest algorithm to improve the accuracy for malware detection. The experimental outcomes demonstrate high proficiency of the accuracy for malware detection, which is pivotal for android apps aiming for secure data exchange between IoT devices.