An Autoencoder-Based Network Intrusion Detection System for the SCADA System

The intrusion detection system (IDS) is the main tool to do security monitoring that is one of the security strategies for the supervisory control and data acquisition (SCADA) system. In this paper, we develop an IDS based on the autoencoder deep learning model (AE-IDS) for the SCADA system. The target SCADA communication protocol of the detection model is the Distributed Network Protocol 3 (DNP3), which is currently the most commonly utilized communication protocol in the power substation. Cyberattacks that we consider are data injection or modification attacks, which are the most critical attacks in the SCADA systems. In this paper, we extracted 17 data features from DNP3 communication, and use them to train the autoencoder network. We measure accuracy and loss of detection and compare them with different supervised deep learning algorithms. The unsupervised AE-IDS model shows better performance than the other deep learning IDS models.

A Framework of Uplink-Downlink NOMA Protocol for Multiple Access in IoT-Oriented Networks

With the fast development of wireless systems and internet of things (IoT), non-orthogonal multiple access (NOMA) has been studied as one of effective schemes to meet increasing demands of massive users. Two types of NOMA transmission, i.e., uplink (UL) and downlink (DL), have been explored in term of mathematical analysis. The first one is derivation of outage probability for UL, DL. The second, we find parameters to adjust system performance to meet requirement in design of NOMA in practice.

An Internet Platform to Monitor Plant Pathogens Spread: The Italian Case of Xylella

The numerous species of harmful organisms of tropical origin represent real phytosanitary emergencies that are bringing sectors of agri-food production to their knees, determining negative economic consequences for many farms and significant repercussions on the management of the territories on which production activities insist. For some years, a phytosanitary emergency affects tree species of the Mediterranean scrub, especially the olive trees in the south of Italy: Xylella Fastidiosa (Xf) infection. In this scenario, the paper aims to describe a proposed internet platform, developed in order to support farmers in pathogen monitoring, providing information about the crop and the cropping environment. It is able to provide information about Plant Sap Flow Density, Normalized Difference Vegetation Index and Vapor Pressure Deficit. Information was represented using interactive maps able to tell the overtime parameters trend. Stakeholder, can access to the olive oil trees maps, obtaining in a single view the risk derived from the Xf contagion. The platform could benefit farmers and other stakeholders interested in analysis and territorial sustainability (decision makers, researchers, citizens and biologists).

PAPR Reduction in MIMO-OFDM Systems Using Low- Complexity Additive Signal Mixing

A MIMO-OFDM wireless communication technique possesses several advantages accrued from combining MIMO and OFDM techniques such as increased channel capacity and improved BER performance. This has made the technique very amiable to current and future generations of communication systems for high data-rate transmission. However, the technique also inherits the high PAPR problem associated with OFDM signals—a problem still requiring a practical solution. This work proposes a PAPR reduction algorithm for solving the problem of high PAPR in MIMO-OFDM systems. The proposed method uses a low-complexity signal mixing concept to combine the original transmit signal and a generated peak-cancelling signal. The computational complexity of the proposed method is O(M) , which is very much less than O(N log2 N) of the FFT algorithms. This is because M, which denotes the number of nonzero peakcancelling samples, is much less than N, the FFT window size. The proposed method was found to achieve high PAPR reductions while utilizing only a few nonzero peak-cancelling samples and it does not significantly change the power of the transmitted signal. For example, with M=5% of 256-point IFFT samples, corresponding to a data rate loss of 4.8%, a large PAPR reduction of 5.9 dB could be achieved at a small power loss of 0.09 dB. Compared with other methods proposed in literature, the proposed method was found to outperform them in terms of PAPR reductions and BER performance.

Performance Comparison of Non-Linear Median Filter Built on MLP-ANN and Conventional MLP-ANN: Using Improved Dataset Training in Micro-Cell Environment

This research work explores the Levenberg- Marquardt training algorithm used for Artificial Neural Network (ANN) optimization during training and the Bayesian Regularization algorithm for the enhanced generalized trained network in training a designed non-linear vector median filter built on Multi-Layer Perceptron (MLP) ANN called model-1 and a conventional MLP ANN called model-2. The model-1 employed in the design helps in dataset de-noising to ensure the removal of unwanted signals for the improved training dataset. An early stopping method in the ratio of 80:10:10 for training, testing, and validation to overcome the problem of over-fitting during network training was employed. First-order statistical indices, the standard deviation, root mean squared error, mean absolute error, and correlation coefficient were adopted for network training analysis and comparative analysis of the designed model-1 and model-2, respectively. Two locations, Line-of-sight (location-1) and non-Line-of-Sight (location-2), were considered where the dataset was captured. The training results from the two locations for the two models demonstrated improved prediction of signal power loss using model-1 in comparison to model-2. For instance, the correlation coefficient, which shows the strength of the predicted value to the measured values (closer to 1) establishing a strong connection, gives 0.990 and 0.995 using model-1 for location-1, training with Lavenberg-Marquardt and Bayesian Regularization algorithm, respectively and 0.965 and 0.980 for model-2 using the same algorithms. It is seen that the Bayesian regularization algorithm, which optimizes the network in accordance with the Levenberg- Marquardt algorithm, gave better prediction results. The same sequence of improved perditions using designed model-1 in comparison to model-2 were seen with training results in location-2 while also adopting other employed 1st order statistical indices.

An Architecture Design of Auto Channel Switching Unit for Hybrid Visible Light Communication System

The revolution in multimedia devices has promoted indoor wireless communication in the last decades. Wireless fidelity (Wi-Fi) connections have expanded rapidly, and more than 5 billion devices have been connected to Wi-Fi each day since 2013, which causes system overloading. This bountiful usage of wireless devices has consumed an excessive amount of the radio spectrum, and the current standard for wireless communication is not able to provide enough capacity for indoor wireless traffic in the next decade. Wi-Fi currently holds 60% of the global traffic; however, secure communication for the internet of things (IoT) and spectrum congestion are two significant challenges for future communication development. Therefore, an affordable, secure, and fast medium for wireless communication is in urgent demand. It should be noted that the spectrum provided by visible light communication (VLC) can be thousands of times wider than the radio frequency (RF) spectrum. The challenge of spectrum congestion and the urgent demand for a high-speed medium can be solved through the application of visible light for indoor communication. It has been proved that each user can achieve a data rate of hundreds of Mbps in the congestion scenario through the application of VLC. But beside these advantages Li-Fi also have some limitations like unavailability in excess and in low light and also limited coverage due to walls and etc. To overcome these limitations the Wi-Fi and VLC hybrid network can be a good solution to continue using privileges of both technologies. A lot of research has been done to introduce a numerous techniques for such hybrid network but we are proposing Auto channel switching unit in this system which will be responsible for shifting and sharing data traffic on both Li-Fi and Wi-Fi channel.

Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

AODV-MSA: A Security Routing Protocol on Mobile Ad Hoc Network

Network security is an important problem, which attracts more attention because recent network attacks caused huge consequences such as data lose, reduce network performance and increase routing load. In this article, we show network attack forms in MANET and propose Multiple Signature Authenticate (MSA) mechanism using digital signature based on asymmetric encryption RSA. Moreover, we describe a new security routing protocol named AODV-MSA by integrating MSA into AODV. Using NS2 simulator system, we implement and examine the efficiency of the AODV-MSA protocol with the 32-bit keys.

Development of Microstrip Antenna for Satellite Application at Ku/Ka Band

Antennas are the most important unit in almost all wireless communications, which played the key of transmitting the radiating electromagnetic waves after converting it from electrical signal. In this paper, designed an antenna that is capable to operate at frequencies between 12 GHz – 18 GHz for Ku band, and 26.5 GHz – 40 GHz for Ka band. This antenna was designed to overcome the narrow bandwidth, low gain, and large size of most satellite application antennas. By using a square shaped patch on a 4.3 dielectric constant substrate, modified and optimized the dimensions of the patch element, this antenna operated on dual-band frequencies between 12 GHz – 18 GHz and 26.5 GHz – 40 GHz, which satisfies the required bandwidth for satellite application. The antenna design was simulated using CST Microwave Studio software to analyze and evaluate the performance of the antenna design visibility.

A New System for Real-time Video Surveillance in Smart Cities Based on Wireless Visual Sensor Networks and Fog Computing

Nowadays, public security is becoming an increasingly serious issue in our society and its requirements have been extended from urban centers to all remote areas. Therefore, surveillance and security cameras are being deployed worldwide. Wireless Visual Sensor Networks nodes can be employed as camera nodes to monitor in the city without the need for any cables installation. However, these cameras are constrained in processing, memory, and energy resources. Also, they generate a massive amount of data that must be analyzed in real-time to ensure public safety and deal with emergency situations. As a result, data processing, information fusion, and decision making have to be executed on-site (near to the data collection location). Besides, surveillance cameras are directional sensors, which makes the coverage problem another issue to deal with. Therefore, we present a new system for real-time video surveillance in a smart city, in which transportations equipped with camera nodes are used as the mobile part of the system and an architecture based on fog computing and wireless visual sensor networks is adopted. Furthermore, we propose an approach for selecting the camera nodes that will participate in the tracking process and we simulated three different use cases to test the effectiveness of our system in terms of target detection. The simulation results show that our system is a promising solution for smart city surveillance applications.