Implementasi Intrusion Detection System (Ids)di Jaringan Universitas Bina Darma

Computer Security systems, in recent years has become a major focus in the world of computer networks, this is due to the high threat of suspicious (Suspicious Threat) and attacks from the internet. Bina Darma University is one of the agencies which activities using the internet network services, ranging from the processing of existing data, including the KRS online system, mail server and web portal in each unit and others. Bina Darma University network manager for this building system is a network Security by implementing a firewall and proxy server on each server in the network unit. To further optimize the network Security system at the University of Bina Darma, the author will implement a network Intrusion Detection System at the Bina Darma University as network Security solutions for both the intranet and internet network of Bina Darma University, where the author will build an IDS (Intrusion Detection System) using a snort. Keywords: Networking Security, Firewall, Proxy Server, IDS (Intrusion Detection System), Snort

An Implementation of Intrusion Detection System Using Genetic Algorithm

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate

K-nearst Neigbour (Knn) untuk Mendeteksi Gangguan Jaringan Komputer pada Intrusion Detection Dataset

Internet increasing is also exponentially increasing intrusion or attacks by crackers exploit vulnerabilitiesin Internet protocols, operating systems and software applications. Intrusion or attacks against computernet works, especially the Internet has increased from year to year. Intrusion detection systems into the main stream in the information security. The main purpose of intrusion detection system is a computer system to help deal with the attack. This study presents k-nearest neigbour algorithm to detect computer network intrusions. Performance is measured based on the level of accuracy, sensitivity, precision and spesificity. Dataset used in this study is a dataset KDD99 intrusion detection system. Dataset is composed of two training data and testing data. From the experimental results obtained by the accuracy of k-nearest neigbour algorithm is about 79,36%

Suitability of Blockchain for Collaborative Intrusion Detection Systems

© 2020 IEEE. Cyber-security is indispensable as malicious incidents are ubiquitous on the Internet. Intrusion Detection Systems have an important role in detecting and thwarting cyber-attacks. However, it is more effective in a centralized system but not in peer-to-peer networks which makes it subject to central point failure, especially in collaborated intrusion detection systems. The novel blockchain technology assures a fully distributed security system through its powerful features of transparency, immutability, decentralization, and provenance. Therefore, in this paper, we investigate and demonstrate several methods of collaborative intrusion detection with blockchain to analyze the suitability and security of blockchain for collaborative intrusion detection systems. We also studied the difference between the existing means of the integration of intrusion detection systems with blockchain and categorized the major vulnerabilities of blockchain with their potential losses and current enhancements for mitigation

Protecting the Security of Network Data

The purpose of this article is to present the major issues regarding threats to the security of network data, define internal/external intrusion and provide a list of the common methods of detection and significant established intrusion preventive practices for maximizing Internet network data security

A Frame Work for Geometrical Structure Anomaly Detection Model

The growth of Internet offers quality and convenience to human life, but at the same time provides a platform for hackers and criminals. The Internet security hence becomes an important issue. Intrusion Detection System (IDS) is designed to detect intrusion and also to prevent a system from being compromised. In this paper, we present a novel Geometrical Structure Anomaly Detection (GSAD) model. GSAD employs pattern recognition techniques previously used in human detection [2}

Anomaly Intrusion Detection based on Concept Drift

Nowadays, security on the internet is a vital issue and therefore, intrusion detection is one of the major research problems for networks that defend external attacks. Intrusion detection is a new approach for providing security in existing computers and data networks. An Intrusion Detection System is a software application that monitors the system for malicious activities and unauthorized access to the system. An easy accessibility condition causes computer networks vulnerable against the attack and several threats from attackers. Intrusion Detection System is used to analyze a network of interconnected systems for avoiding uncommon intrusion or chaos. The intrusion detection problem is becoming a challenging task due to the increase in computer networks since the increased connectivity of computer systems gives access to all and makes it easier for hackers to avoid their traces and identification. The goal of intrusion detection is to identify unauthorized use, misuse and abuse of computer systems. This project focuses on algorithms: (i) Concept Drift based ensemble Incremental Learning approach for anomaly intrusion detection, and (ii) Diversity and Transfer-based Ensemble Learning. These are highly ranked anomaly detection models. We study and compare both learning models. The Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD99) dataset have been used for training and to detect the misuse activities

Design of Web-based Security Management for Intrusion Detection

Electronic business is rapidly popularized and extended through Internet. Internet has many security weaknesses, so we need the security solution such Intrusion Detection System that minimizes the damage of hacking and responds the intrusion dynamically. It is difficult for legacy management system to process the security environments and electronic business, because legacy system lacks of security policies and integrated security methods. In order to resolve these problems, we need security management system that has standard security policy, consulting, diagnosis, maintenance, and repair function. In this paper, we design and implement Web-based security management for intrusion detection. Our security system consists of network nodes, general hosts and a management node. A management node manages a network node, that is a secure router, and general hosts by security policies. We design the channel between the management node and the network node using IPsec (IP Security). We have applied java and Web to implementing user interface of security system. As the proposed system makes use of Web, security management system is easily accessed through the Web