This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-existed permissions and API calls at different levels of combinations, which are the second level, the third level, the fourth level and the fifth level. The extracted datasets of co-existed features at different levels were applied on permissions only, APIs only, permissions and APIs, and APIs and APIs frequencies. To extract the most relevant co-existed features, the frequent pattern growth (FP-growth) algorithm, which is an association rule mining technique, was used. The new datasets were extracted using Android APK samples from the Drebin, Malgenome and MalDroid2020 datasets. To evaluate the proposed model, several conventional machine learning algorithms were used. The results show that the model can successfully classify Android malware with a high accuracy using machine learning algorithms and the co-existence of features. Moreover, the results show that the achieved classification accuracy depends on the classifier and the type of co-existed features. The maximum accuracy, which is 98%, was achieved using the Random Forest algorithm and the co-existence of permissions features at the second combination level. Furthermore, the results show that the proposed approach outperforms the state-of-the-art model. Using Malgenome dataset, the proposed approach achieved an accuracy of about 98%, while the state-of-the-art achieved an accuracy of about 87%. In addition, the experiments show that using the Drebin dataset, the proposed approach achieved an accuracy of about 95%, while the state-of-the-art achieved an accuracy of about 93%.

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN). As the number of connected vehicles continues to grow, the importance of ensuring security and privacy within VANETs becomes paramount. Robust authentication protocols are essential to safeguard vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, given the vulnerability of the open wireless communication medium in VANETs to interception and manipulation. While researchers are actively developing authentication and privacy schemes to secure message exchanges, existing solutions have not fully satisfied all the security and privacy requirements, thereby fueling ongoing research in this field. A significant challenge in this context is balancing the conflicting goals of privacy—protecting sensitive information from unauthorized access—and traceability—ensuring accountability by identifying malicious actors. This paper presents a comprehensive overview of VANETs, covering their key components, prevalent attack types, and the security and privacy prerequisites essential for effective authentication and privacy mechanisms. Specifically, the paper delves into the realm of identity-based authentication schemes within VANETs, subjecting them to rigorous evaluation based on security, privacy, and scalability. The primary objective is to empower the research community to design and implement efficient and secure authentication schemes, minimizing communication and computational overhead while ensuring resilience against a multitude of potential attacks.

Intrusions detection is one of the major issues that worry organizations in wireless sensor networks (WSNs). Many researchers have dealt with this problem and have proposed many methods for detecting different kinds of intrusions such as selective forwarding, which is a serious attack that may obstruct communications in WSNs. However, as the applications of mobile computing, vehicular networks, and internet of things (IoT) are spreading immensely, selective forwarding detection in Mobile Wireless Sensor Networks (MWSNs) has become a key demand. This paper introduces the problem of selective forwarding in MWSNs, and discusses how available techniques for mitigation this problem in WSNs are not applicable in handling the problem in MWSNs due to sensors mobility. Therefore, the paper proposes a model that provides a global monitoring capability for tracing moving sensors and detecting malicious ones. The model leverages the infrastructure of Fog Computing to achieve this purpose. Furthermore, the paper provides a complete algorithm, a comprehensive discussion and experiments that show the correctness and importance of the proposed approach.

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.