MESURE Tool to benchmark Java Card platforms

The advent of the Java Card standard has been a major turning point in smart card technology. With the growing acceptance of this standard, understanding the performance behavior of these platforms is becoming crucial. To meet this need, we present in this paper a novel benchmarking framework to test and evaluate the performance of Java Card platforms. MESURE tool is the first framework which accuracy and effectiveness are independent from the particular Java Card platform tested and CAD used.Comment: International Journal of Computer Science Issues, Volume 1, pp49-57, August 200

A lightweight forwarding strategy for Named Data Networking in low-end IoT

International audienc

A near-autonomous and incremental intrusion detection system through active learning of known and unknown attacks

Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is composed of supervised and unsupervised modules, namely, a Deep Neural Network (DNN) and the K-Nearest Neighbors (KNN) algorithm, respectively. The proposed system is near-autonomous since the intervention of the expert is minimized through the active learning (AL) approach. A query strategy for the labeling process is presented, it aims at teaching the supervised module to detect unknown attacks and improve the detection of the already-known attacks. This teaching is achieved through sliding windows (SW) in an incremental fashion where the DNN is retrained when the data is available over time, thus rendering the IDS adaptive to cope with the evolutionary aspect of the network traffic. A set of experiments was conducted on the CICIDS2017 dataset in order to evaluate the performance of the IDS, promising results were obtained.Comment: 6 pages, 3 figures, 32 references, conferenc

Middleware to Integrate Mobile Devices, Sensors and Cloud Computing

International audienc

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

International audienceSoil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture

Synchronous Intersection Management to reduce Time Loss

Conventional intersection management that allows multiple vehicles from one road at a time, e.g., Round-Robin (RR), may constitute bottlenecks in urban traffic management. Consequently, new intelligent intersection management (IIM) approaches were proposed to reduce time loss, fuel wastage, and ecological damage. IIM is also suited to take advantage of the new communication capabilities of autonomous vehicles that are gaining relevance, though still co-existing with human-driven vehicles. This paper extends the analysis of a recently proposed synchronous IIM system, the Synchronous Intersection Management Protocol (SIMP), that is compared with the RR scheme in a four-way single-lane intersection as those found in urban residential areas, under maximum vehicle speeds of 30Km/h and 50Km/h and various traffic arrival rates. We characterize performance by measuring time loss, i.e., the additional trip delay due to forced slowdown, and fuel consumption using a model for standard vehicles with internal combustion engines. The experimental results obtained with the SUMO simulation framework indicate an advantage for SIMP in both metrics, approximately halving the values achieved with the best RR approaches and with high traffic rates.info:eu-repo/semantics/publishedVersio

Modeling and Improving Named Data Networking over IEEE 802.15.4

International audienc

Predicting transmission success with Machine-Learning and Support Vector Machine in VANETs

International audienceIn this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a succcessful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET

Predicting Vehicles' Positions using Roadside Units: a Machine-Learning Approach

International audienc

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

International audienceSoil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture