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.

Lapindo Embankment Security Monitoring System Based on IoT

Since 2006 Lapindo mudflow caused by natural gas drilling in Sidoarjo. Nowdays the mudflow still can't be stopped, and to prevent it from resident's houses, embankments were built. Eventough the embankments and guardrails has been built but sometimes the mud flowing into resident's houses while its raining or the embankments were subsidence or reep. Severity, the distance between the embankment and the residents' houses is about 500 m. So far, the handling action while embankments ware subsidence is residents report the accidents to related parties, namely PPLS. But the response is too late and take a long time to occur the accident, so the mud has been flow in to resident's houses. because so far it is still completely manual and there is no monitoring system can detect the accident in the embankments. Therefore with monitoring the embankments with vibration and overflow of the lapindo puddle using wirelless sensor network spread over a number of dike-prone points, then its send to smartphone via Blynk. When the accident happend system monitoring aplication will be warning and send message easly via smartphone. So the existence of this monitoring tool can provide a sense of security to the citizens.