An Enhanced AODV Protocol for Avoiding Black Holes in MANET

© 2018 The Authors. Published by Elsevier Ltd. Black hole attack is one of the well-known attacks on Mobile Ad hoc Networks, MANET. This paper discusses this problem and proposes a new approach based on building a global reputation system that helps AODV protocol in selecting the best path to destination, when there is more than one possible route. The proposed protocol enhances the using of watchdogs in AODV by collecting the observations and broadcasting them to all nodes in the network using a low overhead approach. Moreover, the proposed protocol takes into account the detection challenge when a black hole continuously moves

A Context-Aware Android Malware Detection Approach Using Machine Learning

The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike many prior research that focused exclusively on API Calls and permissions features to improve detection efficiency and accuracy, this paper incorporates applications’ contextual features with API Calls and permissions features. Moreover, the proposed approach extracted a new dataset of static API Calls and permission features using a large dataset of malicious and benign Android APK samples. Furthermore, the proposed approach used the Information Gain algorithm to reduce the API and permission feature space from 527 to the most relevant 50 features only. Several combinations of API Calls, permissions, and contextual features were used. These combinations were fed into different machine-learning algorithms to show the significance of using the selected contextual features in detecting Android malware. The experiments show that the proposed model achieved a very high accuracy of about 99.4% when using contextual features in comparison to 97.2% without using contextual features. Moreover, the paper shows that the proposed approach outperformed the state-of-the-art models considered in this work