Port scanning attack is a common cyber-attack where an attacker directs packets with diverse port numbers to scan accessible services aiming to discover open/weak ports in a network. Hence, several detection/prevention techniques were developed to frustrate such cyber-attacks. In this paper, we propose a new inclusive discovery scheme that evaluate five supervised machine learning classifiers, including logistic regression, decision trees, linear/quadratic discriminant, naive Bayes, and ensemble boosted trees. We compared the performance of these models via detection accuracy using a contemporary dataset for port scanning attacks (PSA-2017). As a result, the best performance results have recorded for logistic regression based detection scheme with 99.4%, 99.9%, 99.4%, 99.7%, and 0.454 mu Sec registered for accuracy, precision, recall, F-score, and detection overhead. Lastly, the comparison with existing models exhibited the proficiency and advantage of our model with enhanced attack discovery speed.
Energy efficiency in industry provides some promising solutions for industrial decarbonization and reduction of negative environ-mental impacts. Nowadays, the digitalization of the industry offers an intelligent industrial work network, which allows the use of learning algorithms for the prediction of energy consumption in order to lower the energy bill. This paper investigates different approaches used to predict energy consumption in industry, including Multiple Linear Regression (MLR), Decision Tree (DT), Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) based on data collected of meteorological conditions, energy consumption, and lighting in the industry. The review results indicate that the MLR approach is the best forecasting method.
This study investigates a Fuzzy-Sliding mode control (Fuzzy-SMC) scheme for a single-phase Voltage Source Inverter (VSI) interfacing a photovoltaic (PV) system. The suggested controller is developed to control the real and reactive power in a robust and smooth way and deal with the system parameter uncertainties and external disturbances. The stability of the obtained control laws is proved according to Lyapunov stability criterion. The effectiveness of the adopted strategy is validated through an experimental verification using a Dspace 1104 real-time implementation board. The experimental results, under different load levels, demonstrate that the Fuzzy-SMC provides enhanced transient performance and lower Total Harmonic Distortion (THD) of the output current and voltage in comparison with SMC.
The tremendous demand for water in the agricultural sector in Saudi Arabia poses a great challenge to the government. Bluntly regulating the water supply to farms as a solution for this challenge is not an option, because farming is one of the most important businesses, and it should not be threatened. A substantial percentage of that demanded water is wasted due to manual or inefficient farming, which does not utilize sensors and new technologies to conserve water consumption in the farming process. This project introduces the conspicuous solution, which is an automated, data logging farming system, to achieve efficient, water-conserving farming process. This project combines sensors and automation that introduce efficiency and convenience to a scalable system that will help not only the government and the industries, but also individuals who are interested in farming or simply want to have quick and inexpensive access to fresh crops. The project utilizes a soil moisture sensor to continuously measure the water content of the soil in the system, and supplies the plants with the exact amount of water they need, only when the moisture level is low. The system has been implemented and tested using an Arduino microcontroller that is connected to a webserver to record and retrieve data, into and from a database that is easy to access from anywhere in the world at any time.
In this work, a multiwall carbon nanotube time delay section (MWCNTDS) is presented. For this purpose transmission line model (TLM) of a multiwall carbon nanotube (MWCNT) is used. The results of the analysis of a MWCNTDS are examined and discussed in detail. The results show that while the distance from the ground plane of the outer shell increases, time delay of a MWCNTDS increases. Time delay decreases while the value of inner radius or the total number of shells increases. Since the diameter increases with the increasing values of inner radius and the total number of shells, time delay decreases with increasing diameter. While time delay increases with increasing length of MWCNTDS, it decreases with increasing frequency.
