El-Hassane Aglzim

A secure intersection-based routing protocol for data collection in urban vehicular networks

Data routing has gained great intention since the appearance of Vehicular Networks (VANETs). However, in the presence of attackers, reliable and trustworthy operations in such networks become impossible without securing routing protocols. In this paper, we target to study and design a secure routing protocol S-GyTAR for vehicular environments. Several kinds of routing techniques are proposed in the literature and could be classified into topology-based or position-based strategies. Position-based is the most investigated strategy in vehicular networks due to the unique characteristics of such networks. For this reason, this work is based on the well-known intersection-based routing protocol GyTAR, which exploits the greedy forwarding technique to relay data. In fact, we benefit from GyTARs characteristics and reshape it to introduce a new distributed trust management strategy to secure routing. We design a cluster-based mechanism to monitor nodes and a reputation-based schema to evaluate the vehicles and classify them. We evaluate our proposal using NS3 simulator. Simulation results show high performances regarding the detection rate of malicious nodes and overhead with an amelioration of the end-to-end communication delay in the presence of malicious vehicles.

Dynamic Modeling and Driving Cycle Prediction for a Racing Series Hybrid Car

This paper presents Noao, a plug-in series hybrid racing car equipped with an engine/generator set as range extender. To determine the velocity profile, i.e., performance of the car and its power profile, a dynamic model for this car is developed using pedal position as input. This value is easy to measure, representative for race cycles, and presents a novelty. The model is validated with the results from experiments. An analysis based on the map of Magny-Cours racing circuit and drivers pedal action on certain zones of the circuit is formulated and is used as a prediction tool to determine drivers inputs on other racing circuits and generate driving schedules. The results obtained from this analysis are essential to predict energy consumption of the system, estimate driving range, and inspect battery state-of-charge evolution of the car during races. Moreover, the generated driving cycle provides an optimum compromise between drivetime and energy consumption of the system.

A new intrusion detection approach against lethal attacks in the smart grid:temporal and spatial based detections

The smart grid is the new vision of the traditional power grid, which is characterized by the integration of communication network between all its components beginning from the producers, going through the transmission and distribution units and finishing by the consumers and end users. The heterogeneity of its components imposes a sophisticated security architecture to protect the smart grid from any attacks attempt. In this paper, we propose an architecture in which Intrusion Detection System (IDS) agents are implemented with a distributed manner to monitor the consumer side, building appliances and smart meters. Those IDSs rely on rule-based detection policy, which consists in the combination of temporal and spatial detection rules. Simulation results prove that this combination enhances attacks detection rate and reduces false positive rate.

An efficient Intrusion Detection System against cyber-physical attacks in the smart grid

Abstract Without robust security mechanisms, the smart grid remains vulnerable to many attacks that can cause serious damages. Since state estimation is a critical entity to monitor and control electricity production and distribution, intruders are more attracted by this entity in order to disrupt the smart grid reliability. In this context, we propose an Intrusion Detection System (IDS) architecture to detect lethal attacks with a focus on two smart grid security issues: (i) Firstly, against integrity issue with price manipulation attack, we propose a Cumulative Sum (CUSUM) algorithm that detects this attack even with granular price changes; (ii) Secondly, the availability issue with Denial of Service (DoS) attack against which we develop an efficient method to monitor and detect any misbehaving node. Performance evaluations show the robustness of the proposed IDS system compared to existing mechanisms. The achieved detection rate is above 95% and the false positive rate is below 5%.

Towards using local energy to enhance smart meters privacy

Nowadays, smart meters play a primordial role in the Smart Grid (SG) in order to optimize electricity production, distribution as well as consumption. By exchanging more frequent information between the smart meter and Utility Provider (UP), SG will operate more efficiently with less loss. However, this frequent exchange poses a serious privacy issue which can invade individual privacy since the UP is able to collect a massive amount of smart meter-related data not necessary for billing purposes. In this paper, we propose an efficient sustainable model that uses a local energy to enhance the consumer privacy and reduce his electricity cost at the same time. Moreover, the proposed model is lightweight but also reliable to deal with the low computation capacity of the smart meter. Analytic results prove the efficiency of our model.

New optimization and security approaches to enhance the Smart Grid performance and reliability

Nowadays, the Smart Grid (SG) is becoming smarter thanks to the integration of different information and communication technologies to enhance the reliability and efficiency of the power grid. However, several issues should be met to ensure high SG performance. Among these issues, we cite the problem of electric vehicles (EVs) integration into the SG to avoid electricity intermittence due to the important load that EVs can create. Another issue is the SG communication network security that can be attempted by malicious intruders in order to create damages and make the power grid instable. In this context, we propose at a first level a Bayesian game-theory model that aims to integrate optimally EVs into the SG and maintain the equilibrium between the offer and the demand. At a second level, we propose an Intrusion Detection System (IDS) to protect the SG from attacks that aim to disturb the state estimation. Numerical results prove the efficiency and robustness of our proposed models.

Optimization of data harvesters deployment in an urban areas for an emergency scenario

Since its appearance in the VANETs research community, data collection where vehicles have to explore an area and collect various local data, brings various issues and challenges. Some architectures were proposed to meet data collection requirements. They can be classified into two categories: Decentralized and Centralized self-organizing where different components and techniques are used depending on the application type. In this paper, we treat time-constrained applications in the context of search and rescue missions. For this reason, we propose a centralized architecture where a central unit plans and manages a set of vehicles namely harvesters to get a clear overview about an affected area. But, choosing the optimal number of harvesters to be deployed and the corresponding area to explore for such time-constrained applications are a real issue. In this paper, we model the problem with its constraints, then we propose a heuristic algorithm called Variable Neighborhood Search (VNS) to get the optimal number of harvesters and define areas to be explored by each one. The proposed solution combines two algorithms: The first is a greedy Best Insertion heuristic reshaped to meet our problem definition to get an initial solution and the second is a 2-Opt merged with a String Exchange heuristics which defines neighborhoods and responsible for local search and global optimization of the initial solution. Finally, the solution is analyzed regarding its optimality and the CPU calculation cost.