Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.

The information leakage problem presents one big challenge in the Wi-Fi networks which are widely used nowadays. The War-Driving attacks that target the widely used wireless network hosts and resulted in information leakage present a real challenge. In this paper, a solution that provides a tailored tool based on the open source software is proposed to prevent the information leakage in Wi-Fi networks specially when using the War-Driving attack. It aims to prevent the information leakage on the node machine connected to a Wi-Fi network. It also provides statistical reports that include useful data about the file access on the user-machine. The statistical reports provide the machine-user with complete information about who, what and where in the user-machine a remote user tries to have an access. It also provides the required user permissions to allow/block access of the files on the user-machine that used a client-version of MS-Windows which do not provide user permissions on the shared files. The experiments include the War-Driving attack are maintained through an attack scenario to test the effectiveness of the proposed tools. The host that is protected with the proposed tool success in detecting the war-driving attack through the defense scenario experiment. It also success in preventing the information leakage from the protected host as well.

No abstract

No abstract

No abstract