Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)

Predicting Fraud in Mobile Phone Usage Using Artificial Neural Networks

Mobile phone usage involves the use of wireless communication devices that can be carried anywhere, as they require no physical connection to any external wires to work. However, mobile technology is not without its own problems. Fraud is prevalent in both fixed and mobile networks of all technologies. Frauds have plagued the telecommunication industries, financial institutions and other organizations for a long time. The aim of this research work and research publication is to apply 3 different neural network models (Fuzzy, Radial Basis and the Feedforward) to the prediction of fraud in real-life data of phone usage and also analyze and evaluate their performances with respect to their predicting capability. From the analysis and model predictability experiment carried out in this scientific research work, it was discovered that the fuzzy network model had the minimum error generated in its fraud predicting capability. Thus, its performance in terms of the error generated in this fraud prediction experiment showed that its NMSE (Normalized mean squared error) for the fraud predicted was 1.98264609. The mean absolute error (M AE = 15.00987244) for its fraud prediction was also the least; this showed that the fuzzy model fraud predictability was much better than the other two models

Study of the Application of Neural Networks in Internet Traffic Engineering

In this study, we showed various approachs implemented in Artificial Neural Networks for network resources management and Internet congestion control. Through a training process, Neural Networks can determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns. Therefore, the application of these networks to Traffic Engineering can help achieve its general objective: “intelligent” agents or systems capable of adapting dataflow according to available resources. In this article, we analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to Artificial Intelligence and Computer Networks respectively

A Mechatronics System based on Feature Selection and AI for IoT Intrusion Detection Applications

In today's rapid development of the Internet, people's daily life has become easier, but on the other hand, people's privacy is also faced with potential threats if necessary security measures are not taken. To detect or stop cyberattacks in this area, network intrusion detection systems (IDS) can be equipped with machine learning algorithms to improve accuracy and speed. Recent research on intrusion and anomaly detection has shown that machine learning (ML) algorithms are widely used to detect malicious web traffic, using neural networks to learn models to visualize the sequence of connections between computers on a network. By analyzing and selecting the correct features, dense attacks can be detected more accurately, ultimately reducing misclassification rates and improving accuracy. In this study, we propose a Teacher-Student Feature Selection (TSFS) method that first uses the Isomap method to extract and select features in low dimensions and the best display, and then perform classification and the artificial neural MLP-Net for classification is used to minimize diagnostic errors. Although the teacher-student scheme is not new, to our knowledge, this is the first time this scheme has been used to select features in an intruder alert system. The proposed method can be used to select monitored and unmonitored features. The method is evaluated on different datasets and compared with the state-of-the-art feature selection methods available. The results show that the method performs better in classification, clustering and error detection. Furthermore, experimental evaluations show that the method is less sensitive to parameter selection

A Mechatronics System based on Feature Selection and AI for IoT Intrusion Detection Applications

In today's rapid development of the Internet, people's daily life has become easier, but on the other hand, people's privacy is also faced with potential threats if necessary security measures are not taken. To detect or stop cyberattacks in this area, network intrusion detection systems (IDS) can be equipped with machine learning algorithms to improve accuracy and speed. Recent research on intrusion and anomaly detection has shown that machine learning (ML) algorithms are widely used to detect malicious web traffic, using neural networks to learn models to visualize the sequence of connections between computers on a network. By analyzing and selecting the correct features, dense attacks can be detected more accurately, ultimately reducing misclassification rates and improving accuracy. In this study, we propose a Teacher-Student Feature Selection (TSFS) method that first uses the Isomap method to extract and select features in low dimensions and the best display, and then perform classification and the artificial neural MLP-Net for classification is used to minimize diagnostic errors. Although the teacher-student scheme is not new, to our knowledge, this is the first time this scheme has been used to select features in an intruder alert system. The proposed method can be used to select monitored and unmonitored features. The method is evaluated on different datasets and compared with the state-of-the-art feature selection methods available. The results show that the method performs better in classification, clustering and error detection. Furthermore, experimental evaluations show that the method is less sensitive to parameter selection

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201