Implementasi Metode Unsupervised Learning Pada Sistem Keamanan Dengan Optimalisasi Penyimpanan Kamera IP

Security monitoring systems using face recognition can be applied to CCTV or IP cameras. This is intended to improve the security system and make it easier for users to track criminals is theft. The experiment was carried out by detecting human faces for 24 hours using different cameras, namely an HD camera that was active during the day and a Night Vision camera that was active at night. The application of Unsupervised Learning method with the concept of an image cluster, aims to distinguish the faces of known or unknown people according to the dataset built in the Raspberry Pi 4. The user interface media of this system is a web-based application built with Python Flask and Python MySQL. This application can be accessed using the domain provided by the IP Forwarding device which can be accessed anywhere. According to the test results on optimization of storage, the system is able to save files only when a face is detected with an average file size of ± 2.28 MB for 1x24 hours of streaming. So that this storage process becomes more efficient and economical compared to the storage process for CCTV or IP cameras in general.

On an application of node MCU Esp8266 in opening and closing the laboratory door - online practice

This paper presents an application of ESP8266 node MCU in building a networked laboratory door opening and closing system (NLDOC system). The article mentions the function of the ESP8266 node MCU [1-3], and the Arduino application to serve as the basis for the development of an application that connects computers and smartphones [5-6], allowing to control the door latch of the practical labs remotely via Wi-Fi combined with security monitoring through the camera system [4].

The Need for Production Volumes of Main Agricultural Products as an Integral Part of the Russian Agrarian Policy

The agrarian policy pursued in Russia is primarily aimed at ensuring food security, the main components of which are the economic and physical availability of food to the population. However, the indicators approved in the regulatory legal acts on food security monitoring do not contribute to its objective assessment. They can be used to assess the overall state of food security. A more in-depth analysis is needed for a more objective assessment of the economic availability of food to the population of the Russian Federation, which requires, among other things, changes in the methods of calculating food independence, taking into account the share of household disposable resources used to purchase food. It is also necessary to take into account the energy value of food, the quality of food consumed, etc. To do this, it is necessary to significantly expand the information base on the food consumed, including taking into account its quality and energy value. The paper attempts, based on the available analytical data, to assess the economic availability of food for households depending on their income, as well as in the context of socio-demographic groups, to identify the main factors constraining bringing the level of food consumption to rational norms.

Model Pemantauan Keamanan Jaringan Melalui Aplikasi Telegram Dengan Snort

Network security is the main of the development of today's technology. The ease in accessing the internet also requires protection on users is required. The ease of accessing the internet by people can also cause the occurrence of cyber crime. Cyber crime can be done by all internet users, without exception, to earn a profit. Security monitoring system server through the app messenger Telegram can help administrators in the work because always be on standby in front of the server computer. Notice of Snort as IDS via Telegram also quicked and can be accepted anywhere. In taking action when the server something happened not too late. Target cyber crime also can attack anyone without exception. A system should be a strength, with the protection of a secure network that will be difficult to hack by hackers. The server is the main target in the conduct of cyber crime. The use of the server must maintain to secure all the data is not misused by persons who are not responsible. Each server is a system that should be an administrator as a guard on duty watching and taking action when something happens on the server. To monitor a server, an administrator should always standby in front of the server computer so as not to late take action when the server is about to happen something.

Key Concepts of Systemological Approach to CPS Adaptive Information Security Monitoring

Modern cyber-physical systems (CPS) use digital control of physical processes. This allows attackers to conduct various cyberattacks on these systems. According to the current trends, an information security monitoring system (ISMS) becomes part of a security management system of CPS. It provides information to make a decision and generate a response. A large number of new methods are aimed at CPS security, including security assessment, intrusion detection, and ensuring sustainability. However, as a cyber-physical system operates over time, its structure and requirements may change. The datasets available for the protection object (CPS) and the security requirements have become dynamic. This dynamic effect causes asymmetry between the monitoring data collection and processing subsystem and the presented security tasks. The problem herein is the choice of the most appropriate set of methods in order to solve the security problems of a particular CPS configuration from a particular bank of the available methods. To solve this problem, the authors present a method for the management of an adaptive information security monitoring system. The method consists of solving a multicriteria discrete optimization problem under Pareto-optimality conditions when the available data, methods or external requirements change. The experimental study was performed on an example of smart home intrusion detection. In the study, the introduction of a constraint (a change in requirements) led to the revision of the monitoring scheme and a different recommendation of the monitoring method. As a result, the information security monitoring system gains the property of adaptability to changes in tasks and the available data. An important result from the study is the fact that the monitoring scheme obtained using the proposed management method has a proven optimality under the given conditions. Therefore, the asymmetry between the information security monitoring data collection and processing subsystem and the set of security requirements in cyber-physical systems can be overcome.

Network Security Monitoring System Via Notification Alert

The development of information technology nowadays has become faster, and this makes network security become important. A huge increasing number of computers that are connected makes many gaps in a network. An administrator has an important role in protecting the security of the network. The problem comes when an administrator has human problems such as pain, negligence, and tiredness while needing rapid information when there is an intrusion on the network. This problem can be solved by adding a data traffic detection system known as Intrusion Detection System (IDS). IDS will be connected to Mail Gateway until that administrator can receive notifications such as alerts during an intrusion to the network anytime and anywhere. Snort as one of the network security systems should be developed as a security detection system and network security. A security intrusion prevention system or an Intrusion Prevented System (IPS). The author tries to do analysis and testing on the subjects above to produce a system capable of detecting the intruder in a network that is mobile and also makes it easy for administrators to open data anywhere and anytime using any device.

Ensemble Learning for Threat Classification in Network Intrusion Detection on a Security Monitoring System for Renewable Energy

Most approaches for detecting network attacks involve threat analyses to match the attack to potential malicious profiles using behavioral analysis techniques in conjunction with packet collection, filtering, and feature comparison. Experts in information security are often required to study these threats, and judging new types of threats accurately in real time is often impossible. Detecting legitimate or malicious connections using protocol analysis is difficult; therefore, machine learning-based function modules can be added to intrusion detection systems to assist experts in accurately judging threat categories by analyzing the threat and learning its characteristics. In this paper, an ensemble learning scheme based on a revised random forest algorithm is proposed for a security monitoring system in the domain of renewable energy to categorize network threats in a network intrusion detection system. To reduce classification error for minority classes of experimental data in model training, the synthetic minority oversampling technique scheme (SMOTE) was formulated to re-balance the original data sets by altering the number of data points for minority class to imbue the experimental data set. The classification performance of the proposed classifier in threat classification when the data set is unbalanced was experimentally verified in terms of accuracy, precision, recall, and F1-score on the UNSW-NB15 and CSE-CIC-IDS 2018 data sets. A cross-validation scheme featuring support vector machines was used to compare classification accuracies.