Towards Sophisticated Air Traffic Control System Using Formal Methods

We propose a general formal modeling and verification of the air traffic control system (ATC). This study is based on the International Civil Aviation Organization (ICAO), Federal Aviation Administration (FAA), and National Aeronautics and Space Administration (NASA) standards and recommendations. It provides a sophisticated assistance system that helps in visualizing aircrafts and presents automatic bugs detection. In such a critical safety system, the use of robust formal methods that assure bugs absence is highly required. Therefore, this work suggests a formalism of discrete transition systems based on abstraction and refinement along proofs. These ensure the consistency of the system by means of invariants preservation and deadlock freedom. Hence, all invariants hold permanently providing a handy solution for bugs absence verification. It follows that the said deadlock freedom ensures a continuous running of a given system. This specification and modeling technique enable the system to be corrected by construction. Document type: Articl

Formal Specification of QoS Negotiation in ODP System

The future of Open Distributed Processing systems (ODP) will see an increasing of components number, these components are sharing resources. In general, these resources are offering some kind of services. Due to the huge number of components, it is very difficult to offer the optimum Quality of service (QoS). This encourages us to develop a model for QoS negotiation process to optimize the QoS in an ODP system. In such system, there is a High risk of software or hardware failure. To ensure good performance of a system based on our model, we develop it using a formal method. In our case, we will use Event-B to get in the end of our development a system correct by construction

Towards Sophisticated Air Traffic Control System Using Formal Methods

We propose a general formal modeling and verification of the air traffic control system (ATC). This study is based on the International Civil Aviation Organization (ICAO), Federal Aviation Administration (FAA), and National Aeronautics and Space Administration (NASA) standards and recommendations. It provides a sophisticated assistance system that helps in visualizing aircrafts and presents automatic bugs detection. In such a critical safety system, the use of robust formal methods that assure bugs absence is highly required. Therefore, this work suggests a formalism of discrete transition systems based on abstraction and refinement along proofs. These ensure the consistency of the system by means of invariants preservation and deadlock freedom. Hence, all invariants hold permanently providing a handy solution for bugs absence verification. It follows that the said deadlock freedom ensures a continuous running of a given system. This specification and modeling technique enable the system to be corrected by construction

A New Centralized Detection-Based Process for Evaluating Anomalies and Analyzing the First Causes Using Machine Learning and Web Semantic

In the last decades, many works have been done to enhance data performances in the computer field. Data performance consists to describe all improvements which can be added to data traffic. More precisely, we are talking about techniques allowing improving the evaluation of big data using machine learning. Data evaluation is composed of several variables such as security, quality of service, data synchronization, scalability, and data structuring. In this work, we complete our proceedings done to supervise the continuity of technological evolution in terms of big data and safety. In other words, we aim to add brick to our previous processes to take into consideration the enhancement of the analysis of the causes generating frauds and intrusions preventing data traffic. To achieve this end, we increase current machine learning techniques with prior knowledge based on data thresholds set by experts in the first place. We also aim to integrate knowledge facilitating the interpretation of the causes causing all kinds of anomalies in the second place. Finally, our process will be endowed with the requirements to improve the rate of detection of anomalies and reduce human involvement operation

Intrusion Detection System Using machine learning Algorithms

The world has experienced a radical change due to the internet. As a matter of fact, it assists people in maintaining their social networks and links them to other members of their social networks when they require assistance. In effect sharing professional and personal data comes with several risks to individuals and organizations. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. For this reason, IDS plays a major role in protecting internet users against any malicious network attacks. (IDS) Intrusion Detection System is a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. In this paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, the second one is used to process our database with the most efficient algorithm. Two different datasets will be operated in our experiments to evaluate the model performance. NSL-KDD and UNSW-NB15 datasets are used to measure the performance of the proposed approach in order to guarantee its efficiency

A comparative study of Machine learning Algorithms on the UNSW-NB 15 Dataset

The world has experienced a radical change due to the internet. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. This safety is handled using systems to detect network intrusion called Intrusion Detection Systems (IDS). Machine learning techniques are being implemented to improve these systems. In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a good one for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. Nevertheless, most researchers have focused on the confusion matrix as measurements of classification performance. Therefore, many papers use this matrix to present a detailed comparison with the dataset, data preprocessing, feature selection technique, algorithms classification and performance evaluation. The goal of this paper is to present a comparison of application of different Machine Learning algorithms used to build and improve intrusion detection systems in terms of confusion matrix, accuracy, recall, precision, FAR, specificity and sensitivity using the UNSW-NB15 Dataset. Furthermore, we introduce some lesson learnt to shoot more researchers in their future works