Slim Rekhis

Bio: Slim Rekhis is an academic researcher from Carthage University. The author has contributed to research in topics: Wireless sensor network & Cloud computing. The author has an hindex of 13, co-authored 84 publications receiving 532 citations. Previous affiliations of Slim Rekhis include University of the Western Cape & École Normale Supérieure.

Border surveillance monitoring using Quadcopter UAV-Aided Wireless Sensor Networks

Abstract: In this paper we propose a novel cooperative border surveillance solution, composed of a Wireless Sensor Network (WSN) deployed terrestrially to detect and track trespassers, and a set of lightweight unmanned aircraft vehicles (UAVs) in the form of quadcopters that interact with the deployed WSN to improve the border surveillance, the detection and investigation of network failures, the maintenance of the sensor network, the tracking of trespasser, the capture and transmission of realtime video of the intrusion scene, and the response to hostage situations. A heuristic-based scheduling algorithm is described to optimize the tracking mission by increasing the rate of detected trespassers spotted by the quadcopters. Together with the design of the electrical, mechanical and software architecture of the proposed VTail quadcopter, we develop in this paper powerless techniques to accurately localize terrestrial sensors using RFID technology, compute the optimal positions of the new sensors to drop, relay data between isolated islands of nodes, and wake up sensors to track intruders. The developed VTail prototype is tested to provide valid and accurate parameters’ values to the simulation. The latter is conducted to evaluate the performance of the proposed WSN-based surveillance solution.

Cooperative data muling from ground sensors to base stations using UAVs

Abstract: Data muling using UAVs/drones is currently emerging as an alternative to the traditional traffic engineering techniques used in wireless sensor networks, when wireless communication is not an option or the least cost-efficient solution. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located into an environment and their delivery to base stations where further processing is performed. We consider a persistent path planning and UAV allocation model, where a team of UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised and proven to be NP-hard. We propose a heuristic solution for the problem and evaluate its relative efficiency through simulation.

A System for Formal Digital Forensic Investigation Aware of Anti-Forensic Attacks

Abstract: To defeat the process of investigation and make the analysis and reconstruction of attack scenarios difficult, challenging, or even impossible, attackers are motivated by conducting anti-forensic attacks. Several methods were proposed by the literature to formally reconstruct the sequence of events executed during the incident using theoretical and scientifically proven methods. However, these methods are not tailored to cope with anti-forensic attacks, as they assume that the collected evidence is trusted, do not model anti-forensic actions, and do not characterize provable anti-forensic attacks based on the knowledge of attacks, security solutions, and forms of evidence expected to be generated. We develop in this work a theoretical approach of digital investigation aware of anti-forensic attacks. After describing an investigation process which is able to address these attacks, we develop a state-based logic to describe the investigated system, the deployed security solution, the evidence they provide, and the library of attacks. An inference system is proposed to mitigate anti-forensic attacks and generate potential scenarios starting from traces that were targeted by these attacks. To exemplify the proposal, we provide a case study related to the investigation of an incident that exhibited anti-forensic attacks.

Digital Investigation of Wormhole Attacks in Wireless Sensor Networks

Abstract: Several solutions were proposed by the literature to detect wormhole attacks in wireless sensor networks (WSN)but, to the best of our knowledge, none of them has taken interest to the problem of digital investigation. We propose in this paper a solution for digital investigation of wormhole attacks in WSN. An observed WSN is defined to support generation and secure forwarding of evidences regarding sensor nodes behavior in the network. A set of investigator nodes, called observers, are distributed over the network in charge of monitoring the network topology and datagram forwarding by sensor nodes. A set of algorithms are proposed to aggregate the collected evidences, identify colluding nodes, and reconstruct the potential scenarios of wormholes attacks.

Internet-of-Things in Motion: A Cooperative Data Muling Model for Public Safety

Abstract: In emerging Internet-of-Things (IoT) scenarios, a team of drones can be tasked to collect data at specific locations of interest, ferry it to remote places where a cloud infrastructure is used for sharing the collected data as a service to the community. In such an "Internet-of-Things in motion" scenario, terrain modeling, path finding, fair task allocation are key parameters upon which a successful mission depends. This paper describes a model developed to control a team of drones in order to cover irregular areas of uneven terrain. The model is based on an auction-based algorithm to coordinate the team of drones, relies on a 3D landscape model for path allocation. Experiments were conducted to assess the field readiness of the drone technology, simulated experiments were carried out to determine the relative eciency, the fairness of work allocation of the proposed model.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Abstract: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Abstract: More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

Sybil Attacks and Their Defenses in the Internet of Things

Abstract: The emerging Internet-of-Things (IoT) are vulnerable to Sybil attacks where attackers can manipulate fake identities or abuse pseudoidentities to compromise the effectiveness of the IoT and even disseminate spam. In this paper, we survey Sybil attacks and defense schemes in IoT. Specifically, we first define three types Sybil attacks: SA-1, SA-2, and SA-3 according to the Sybil attacker’s capabilities. We then present some Sybil defense schemes, including social graph-based Sybil detection (SGSD), behavior classification-based Sybil detection (BCSD), and mobile Sybil detection with the comprehensive comparisons. Finally, we discuss the challenging research issues and future directions for Sybil defense in IoT.

Intrusion detection and Big Heterogeneous Data: a Survey

Abstract: Intrusion Detection has been heavily studied in both industry and academia, but cybersecurity analysts still desire much more alert accuracy and overall threat analysis in order to secure their systems within cyberspace. Improvements to Intrusion Detection could be achieved by embracing a more comprehensive approach in monitoring security events from many different heterogeneous sources. Correlating security events from heterogeneous sources can grant a more holistic view and greater situational awareness of cyber threats. One problem with this approach is that currently, even a single event source (e.g., network traffic) can experience Big Data challenges when considered alone. Attempts to use more heterogeneous data sources pose an even greater Big Data challenge. Big Data technologies for Intrusion Detection can help solve these Big Heterogeneous Data challenges. In this paper, we review the scope of works considering the problem of heterogeneous data and in particular Big Heterogeneous Data. We discuss the specific issues of Data Fusion, Heterogeneous Intrusion Detection Architectures, and Security Information and Event Management (SIEM) systems, as well as presenting areas where more research opportunities exist. Overall, both cyber threat analysis and cyber intelligence could be enhanced by correlating security events across many diverse heterogeneous sources.