Iraqi Journal of Information and communication technology

This paper is concerned with the experimental realization of an Arduino-based quantum cryptography system, in which a setup of five laser diodes is constructed in the transmitter that randomly emits photons to encode information consisting of a sequence of \\"0\\" and \\"1\\" bits. The receiver contains five photoresistor sensors that detect the presence or absence of light as well as determine the intensity of the light. Laser light is used to securely transmit data, such as text or numbers, in this technology

The early detection of pipeline breaches in the pipeline monitoring system is a crucial concern for oil management firms. Diverse technologies have been created to identify pipeline leaks. However, they need development as the current systems take a long time to detect leaks. As a result, it can contribute to oil waste and natural disasters. This paper provides a system for detecting pipeline leaks using pressure, water-flow, Accelerometer, and voltage sensors coupled to an Arduino mega microcontroller with BLYNK IoT platform real-time monitoring. It is obvious from the data that the Oil Pipe Monitoring System is capable of displaying pipeline flowrates, pressures, and acceleration. When a leak occurs or an event occurs during operation, a message is delivered to the responsible technical individual indicating which pipe is leaking. This project successfully established an Internet of Things(IoT) platform to monitor pipeline events in real-time.

Graphical passwords, as opposed to textual passwords, require the user to pick pictures or draw symbols rather than input written letters. They are an option that may be explored in order to get over the issues that are caused by the system of passwords that are based on text. It has been hypothesized that graphical passwords are more difficult to crack using a brute force technique or to figure out through guessing. This paper proposes an authentication system based on a graphical password method. The proposed system computes the password points using histogram arithmetic and encrypts the chosen password points using SHA512. The envisioned system has bee n realized as an android application and evaluated with existing research considering multiple measurements such as required login time, password space, and entropy. The findings reveal that the new suggested system out performs the reference work by more than 85% in terms of login latency and more than 72% in terms of entropy results

Due to the importance of the intrusion detection system, which is considered supportive of enhancing network security. Therefore, we seek to increase the efficiency of intrusion detection systems through the use of deep learning mechanisms. However, intrusion detection algorithms still suffer from problems in the process of classification and determining the presence and type of attack, which causes a decrease in the detection rate, an increase in the number of false alarms, and reduces system performance. This is due to a large number of redundant features that are not relevant to the dataset. To solve this problem, we propose a hybrid algorithm based on the use of the feature selection technique, which helps in reaching the goal optimally by choosing the best and most important features. It works by integrating three ways to reduce the number of features by deleting the static features that do not have much value from the information gained. This is done before the training stage by the deep learning model of LSTM as preprocessing for the CSE-CIC-IDS data set, which helps in improving the performance of the system by reducing the processing time and increasing the detection rate and accuracy. The results of the experiment showed a high accuracy of 99%

Over 4 billion individuals are projected to own a mobile device, with smartphone use continuing to rise around the world. Associated with this trend is the collection and usage of location information provided by these devices. The procedure for tracking and evaluating a mobile phone number is crucial for crime and terrorist attacks preventing concerns, especially in countries with deteriorated security conditions. The locational tracking information can enable real-time operational awareness and can save people’s lives. In this aspect, the Geographic Information System (GIS) system is frequently used to provide intensive spatial information along the tracking process. This research aims to examine, follow up on, and trace a certain person’s phone number inside a certain area using a combination of ArcGIS and Google Maps application software they are programs used to track and determine locations through the use of various types of coordinates, including latitude and longitude. In the ArcGIS program, there are several methods and algorithms used in various fields, including (locating people, determining tourist places, determining water and sewage networks, forecasting in the event of storms or floods, and many other areas), an example of these methods in the program is (Kriging method, Idw method, Spline method) and many other methods, each method is used for a specific purpose and field. In this research, the kriging method was used, which is a design algorithm that is used in many analysis and tracking processes and gives clear and accurate results. The process of determining locations in the maps used in the search depends on taking the longitude and latitude coordinates and downloading them to those maps. The maps that have been worked on in the research provide a set of basic concepts adopted in the process of tracking a particular personâs phone number, such as(the personâs location, the numbers connected to him, and the duration of a call to numbers in his contact list).The tracking system used is tested by creating a virtual contact list for a specific personâs phone number and making use of all the data in the contact list and applying it in the ArcGIS program, knowing that the duration of the contact list used is 9 months (from January to October 2021). The obtained results are analyzed and sorted in different ways to provide a wide model of the locational communication and movement activity of the tracked person. They showed that the system is efficient and can be applied to enhance the security in Iraq for tracking suspected people in crimes like terrorism, kidnapping, drugs, extortion ⊠etc.

Face detection in secure places is an important application merging with machine vision. This paper presents a system for performing face detection and recognition using the existing architecture of Google-Net and transfer learning to make the network learn images based on pre-trained architecture. The design of the network leads to an architecture that maximizes the system’s accuracy, and accurately detects faces that are saved to the database, and specifies the effect of the weights that were used within the nodes of the hidden layer, which is considered to be the most time-consuming task within the architecture. The main characteristics are explained, and the architecture and data set have been explained in detail with the design of a network, which provides an architecture that leads to maximizing the accuracy of the system, accurately detecting faces that are saved to the database, and specifying the effect of weights used within the nodes of the hidden layer, which is considered the most time-consuming task within the architecture. Experimental results show that using epochs of 10 and 100samples implies 98.37% training accuracy, whereas other epochs either provide less accuracy or consume more time, and the number of the epochs and training samples can be modified according to the system requirements. Other factors like illumination, the color of the background, and face rotation or scaling were discussed as impact factors.