Implementation Of Intrusion Detection System Research Articles

Mengoptimalkan Keamanan Jaringan Komputer Menggunakan Snort dan Telegram Bot yang Terintegrasi dengan Mikrotik

The implementation of IDS (Intrusion Detection System) snort and telegram bot that can be integrated with a microtic router on the Uppala Villa Nusa Dua computer network is used to detect attack activities and suspicious activity on the Uppala Villa Nusa Dua computer network and to provide notification in real-time logs of suspicious activity on computer network. The research method used is the SPDLC (Security Policy Development Life Cycle) method which has six stages: Identification, Analysis, Design, Implementation, Testing and Evaluation. The software used on the server computer is Snort, WinPcap, Xampp and BASE (Basic Analysis and Security Engine) while for testing a computer network security system using Nmap, Loic and Brutus tools. The results obtained by the implementation of the IDS (Intrusion Detection System) Snort and telegram bot have been successfully implemented and can be integrated with the microtic router. Based on testing conducted after the implementation of the new system, it was found that 95% of the use of snort and telegram bot can optimize the computer network security system at Uppala Villa Nusa Dua.

Model-Based Feature Selection for Developing Network Attack Detection and Alerting System

The use of the Intrusion Detection Systems (IDS) still has unresolved problems, namely the lack of accuracy in attack detection, resulting in false-positive problems and many false alarms. Machine learning is one way that is often utilized to overcome challenges that arise during the implementation of IDS.. We present a system that uses a machine learning approach to detect network attacks and send attack alerts in this study. The CSE-CICIDS2018 Dataset and Model-Based Feature Selection technique are used to assess the performance of eight classifier algorithms in identifying network attacks in order to determine the best algorithm. The resulting XGBoost Model is chosen as the model that provides the highest performance results in this comparison of machine learning models, with an accuracy rate of 99 percent for two-class classification and 98.4 percent for multi-class classification.

An improved deep bagging convolutional neural network classifier for efficient intrusion detection system

In the current trend, the network-based system has substantial jobs, and they have become the targets of attackers. When an intrusion occurs, the security of a computer system is compromised. As a result, we must seek out the best methods for ensuring frameworks. A crucial component of the security management architecture is the intrusion detection system (IDS). To maintain effective network security, the design and implementation of IDS remain an important assessment topic. For intrusion detection, the previous system created an enhanced relevance vector machine (ERVM) classifier. However, intrusion detection is not robust for large-scale intrusion datasets, resulting in a high attack rate. The suggested work developed an improved deep bagging based convolutional neural network (DBCNN) for intrusion detection to address this issue. Preprocessing, feature selection, and classification are three processes included in the proposed framework. The KDD dataset is preprocessed in this stage using the kalman filter method. The feature selection is then carried out using the inertia weight based dragonfly method (IWDA). Finally, the DBCNN classifier successfully identifies interruption assaults. The KDD dataset is used to test the new model. The test results show that the proposed work accomplishes better execution contrasted and the current framework as far as accuracy, precision, recall and f-measure.

Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years, researchers in academia and industry used machine learning (ML) techniques to design and implement Intrusion Detection Systems (IDSes) for computer networks. Many of these researchers used datasets collected by various organizations to train ML classifiers for detecting intrusions. In many of the datasets used in training ML classifiers in such systems, data are imbalanced (i.e., not all classes had equal number of samples). ML classifiers trained with such imbalanced datasets may produce unsatisfactory results. Traditionally, researchers used over-sampling and under-sampling for balancing data in datasets to overcome this problem. In this work, in addition to random over-sampling, we also used a synthetic data generation method, called Conditional Generative Adversarial Network (CTGAN), to balance data and study their effect on the performance of various widely used ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples to balance intrusion detection datasets. Based on extensive experiments using widely used datasets NSL-KDD and UNSW-NB15, we found that training ML classifiers on datasets balanced with synthetic samples generated by CTGAN increased their prediction accuracy by up to 8% and improved their MCC score by up to 13%, compared to training the same ML classifiers over imbalanced datasets. We also show that this approach consistently performs better than some of the recently proposed state-of-the-art IDSes on both datasets. Our experiments also demonstrate that the accuracy of some ML classifiers trained over datasets balanced with random over-sampling decline compared to the same ML classifiers trained over original imbalanced dataset.

Methodology for detecting network intrusions and signs of computer attacks based on an empirical approach

The article analyzes the characteristics of detection of network intrusions into the information system and detection of signs of computer attacks at the enterprise; analysis of possible actions by attackers, methods and principles of establishing an optimal network intrusion detection system were investigated; considered the possibilities of developing and using systems for detecting network intrusions and detecting signs of computer attacks at the enterprise in modern conditions; researched and developed recommendations for the implementation of intrusion detection systems and detection of signs of computer attacks for possible further installation in the information protection system of any organization.

Reduction of the Delays Within an Intrusion Detection System (IDS) Based on Software Defined Networking (SDN)

Software Defined Networking (SDN) is a very useful tool not only to manage networks but also to increase network security, in particular by implementing Intrusion Detection Systems (IDS) directly into the SDN architecture. The implementation of IDS within the SDN paradigm can simplify the implementation, speed up incident responses, and, in general, allow to promptly react to cyber attacks through proper countermeasures. Nevertheless, embedding IDS within SDN also introduces delays that cannot be tolerated in specific network environments, like industrial control systems. This paper focuses on the implementation of an IDS based on Machine Learning (ML) algorithms into an SDN architecture and proposes a very practical approach to reduce the delay by using the sequential implementation of prototypes of increasing software and hardware complexity so allowing quick tests to highlight the main problems, solve them and pass to the next operative step. A fully validated performance evaluation is then shown by exploiting all the presented solutions and by using further improved hardware features. The overall performance is very good and compliant with most, even if not yet all, industrial control systems constraints. Results show how the proposed solutions provide a significant improvement of the latency so opening the door to a real implementation in the field.

Intrusion Detection System with Remote Signalling for Vehicles Using an Arduino Controller and Radio-Frequency Technology

Malicious activities or policy violations have been a concern for the past years. For example, many people have been victims of robbery on vehicles. A conceptual diagram of an Intrusion Detection System (IDS) [1] [2] for vehicles with remote signaling using an Arduino controller and radio-frequency technology is proposed in this paper. To address malicious activities on vehicles, two aspects are considered here, namely: notifier and detector. Firstly, an object-oriented C module that puts on and off a controller (installed inside the vehicle) and an anti-theft electronic editing that powered using an alternator and supported by a back-up battery are implemented. Secondly, a magnetic intrusion sensor, controlled by a proximity detector using radio-frequency technology, has been installed on each vehicle door. To enable IDS, a user needs to activate the monitoring system when leaving their vehicle. This is done using a remote system. In case the user does not activate the monitoring system while leaving the vehicle, a 5-meter-proximity detector will automatically lock the system and set off the monitoring system whenever the user is outside the detection zone. The detection zone is a 5-meter radius area centered at the controller. Here, monitoring consists of geolocating any intruders within the detection zone. This means, if any of the vehicle doors is opened while the system is still locked, the controller will activate the vehicle alarm for a few seconds, thereafter send an SMS notification to the owner. The system automatically unlocks as soon as the proximity detector is within the detection zone. The contribution of this paper, as compared to other similar work, is to reinforce the electronic implementation of IDS.

Progress of Machine Learning in the Field of Intrusion Detection Systems

With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.

GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

Implementation of Intrusion Detection System (IDS) and Snort Community Rules to Detect Types of Network Attacks

Implementation of Intrusion Detection System (IDS) and Snort Community Rules to Detect Types of Network Attacks

Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyberattacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement intrusion detection systems (IDSs) to help mitigate these unfortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms, both of which are symmetry learning attributes. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, One-Class SVM, DBSCAN, and Expectation-Maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, and UNSW-NB15 datasets and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced FAR and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current approaches.

Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors

Recently, the use of Internet is increased for digital communication to share a lot of sensitive information between computers and mobile devices. For secure communication, data or information must be protected from adversaries. There are many methods of safeties like encryption, firewalls and access control. Intrusion detection system is mainly used to detect internal attacks in organization. Machine leaning techniques are mostly used to implement intrusion detection system. Ensemble method of machine learning gives high accuracy in which moderately accurate classifiers are combined. Ensemble classifier also provides less false positive rates. In this paper, a novel ensemble classifier using rule combination method has proposed for intrusion detection system. Ensemble classifier is designed using three rule learners as base classifiers. The benefits and feasibility of the proposed ensemble classifier have demonstrated by means of KDD’98 datasets. The main novelty of the proposed approach is based on three rule learner combination using rule of combination method of ensemble and feature selector. These three base classifiers are separately trained and combined using average probabilities rule combination. Base classifier’s accuracies have compared with the proposed ensemble classifier. Best First search algorithm has used to select relevant features from training dataset. This algorithm also helped to reduce dimension of training and testing dataset which benefits in reduction of training time. Several comparative experiments are conducted for evaluating performances of classifiers in term of accuracy and false positive rates. Experimental results show that the proposed ensemble classifier provide significant improvement of accuracy compared to individual classifiers with less positive rates.

A bidirectional LSTM deep learning approach for intrusion detection

The rise in computer networks and internet attacks has become alarming for most service providers. It has triggered the need for the development and implementation of intrusion detection systems (IDSs) to help prevent and or mitigate the challenges posed by network intruders. Over the years, intrusion detection systems have played and continue to play a very significant role in spotting network attacks and anomalies. Numerous researchers around the globe have proposed many IDSs to combat the threat of network invaders. However, most of the previously proposed IDSs have high rates of raising false alarms. Additionally, most existing models suffer the difficulty of detecting the different attack types, especially User-to-Root (U2R) and Remote-to-Local (R2L) attacks. These two types of attacks often appear to have lower detection accuracy for the existing models. Hence, in this paper, we propose a bidirectional Long-Short-Term-Memory (BiDLSTM) based intrusion detection system to handle the challenges mentioned above. To train and measure our model’s performance, we use the NSL-KDD dataset, a benchmark dataset for most IDSs. Experimental results show and validate the effectiveness of the BiDLSTM approach. It outperforms conventional LSTM and other state-of-the-art models in terms of accuracy, precision, recall, and F-score values. It also has a much more reduced false alarm rate than the existing models. Furthermore, the BiDLSTM model achieves a higher detection accuracy for U2R and R2L attacks than the conventional LSTM.

Dynamic malware attack dataset leveraging virtual machine monitor audit data for the detection of intrusions in cloud

AbstractIn this new era of cloud computing, Intrusion Detection System (IDS) is very essential for the continual monitoring of computing resources for signs of compromise since the number of attack vectors and malware are in increase. Only few IDS datasets are publicly available and those available are outdated, lack cloud‐specific attacks. This article presents a novel dataset based on Virtual Machine Introspected data for the implementation of IDS in cloud. The dataset was generated from the behavioral characteristics of malware and benign sample execution traces on virtual machines using Virtual Machine Introspection (VMI) technique. A vector space model based on system call approach is applied to analyze the behavioral characteristics for the generation of proposed dataset. The purpose of this study is to compare the proposed dataset with existing datasets and evaluate the effectiveness of these datasets by applying Machine Learning (ML) algorithms with 10‐fold cross‐validation. The ML algorithms used in the experiments are C4.5, Random Forest, JRip, NaiveBayes, K‐Nearest Neighbors (KNN), and Support Vector Machine (SVM). The effectiveness of detecting intrusions using proposed dataset is promising compared with other datasets in‐terms of intrusion detection accuracy, recall value, precision, and F1‐score metrics. For example, the intrusion detection accuracy in proposed dataset is 0.11% improved than UNM dataset, 6.28% higher than ADFA dataset, and 1.88% higher than LID dataset with C4.5 algorithm. Therefore, the proposed dataset is best suitable for implementing IDS for cloud.

A novel workload scheduling framework for intrusion detection system in NFV scenario

Compared with traditional Intrusion Detection System (IDS) solutions, deploying IDS in Network Function Virtualization (NFV) environment can have better scalability and flexibility. Existing research works in this area do not consider many IDS features to design IDS-specific workload scheduling approaches. Thus, there is space further to promote the performance of IDS deployment in the NFV scenario. In this paper, we find some critical IDS features by analyzing packet processing procedures, software implementation, and rulesets of typical IDS. Combining these features with the flexibility of NFV, we propose a novel workload scheduling framework for IDS deployment in the NFV scenario. Our framework contains two parts: 1) a novel protocol & destination port based traffic migration strategy which can promote the detection performance and reduce the memory usage compared with the traditional 5-tuple hash based strategy; 2) an auto-configuration algorithm to find a better-than-default configuration for each Virtual Network Function (VNF) instance. We evaluate our framework with real network traffic and benchmark traffic datasets for IDS. Experimental results show that our framework can always have better detection performance and lower memory usage than the 5-tuple hash based migration strategy and the default configuration.

A Review of the cluster based Mobile Adhoc Network Intrusion Detection System

The Mobile Ad-hoc Network is decentralized and consisting of numerous different communication devices. Its distributed design and lack of infrastructure are the means of numerous network assaults. For personal computer users, companies, and the military, network security has become more important. Safety becomes a significant issue with the rise of the internet, and the past of security enables a better understanding of the evolution of security technology. Via the audit and monitoring phase, the implementation of Intrusion Detection Systems (IDS) in ad-hoc node securities was improved. This framework is made up of clustering protocols that are extremely efficient in finding intrusions with low resource and overhead computing costs. Current protocols have been related to routes that are not popular in intrusion detection. The cluster is barely impacted by the weak road layout and route renewal. The cluster is unpredictable and results in processing maximization together with network traffic. In general, battery-based ad hoc networks are organized and dependent on power constraints. To detect and react rapidly against intrusions, an active monitoring node is required. Only if the clusters are strong and extensive maintaining capabilities can it be accomplished. The routes also shift as the cluster shifts and it would not be feasible to prominently process the achievement of intrusion detection. This raises the need for a better clustering algorithm that addresses these disadvantages and guarantees the protection of the network in any way. A powerful clustering algorithm that is ahead of the current routing protocol is the cluster-based Intrusion Detection Method. Regardless of routes that perfectly track the intrusion, it is permanent. This streamlined technique of clustering achieves strong intrusion detection speeds with low processing as well as memory overhead. It also overcomes the other limitations of traffic, connections, and node mobility on the network, regardless of the routes. In detecting the attack or malicious node, the individual nodes in the network are not active.

Intruder Detection Systems on Computer Networks Using Host Based Intrusion Detection System Techniques

The openness of access to information raises various problems, including maintaining the validity and integrity of data, so a network security system is needed that can deal with potential threats that can occur quickly and accurately by utilizing an IDS (intrusion detection system). One of the IDS tools that are often used is Snort which works in real-time to monitor and detect the ongoing network by providing warnings and information on potential threats in the form of DoS attacks. DoS attacks run to exhaust the packet path by requesting packets to a target in large and continuous ways which results in increased usage of CPU (central processing unit), memory, and ethernet or WiFi networks. The snort IDS implementation can help provide accurate information on network security that you want to monitor because every communication that takes place in a network, every event that occurs and potential attacks that can paralyze the internet network are monitored by snort.

Input splits design techniques for network intrusion detection on Hadoop cluster

Intrusion detection system (IDS) is one of the most important components being used to monitor network for possible cyber-attacks. However, the amount of data that should be inspected imposes a great challenge to IDSs. With recent emerge of various big data technologies, there are ways for overcoming the problem of the increased amount of data. Nevertheless, some of this technologies inherit data distribution techniques that can be a problem when splitting a sensitive data such as network data frames across a cluster nodes. The goal of this paper is design and implementation of Hadoop based IDS. In this paper we propose different input split techniques suitable for network data distribution across cloud nodes and test the performances of their Apache Hadoop implementation. Four different data split techniques will be proposed and analysed. The techniques will be described in detail. The system will be evaluated on Apache Hadoop cluster with 17 slave nodes. We will show that processing speed can differ for more than 30% depending on chosen input split design strategy. Additionally, we?ll show that malicious level of network traffic can slow down the processing time, in our case, for nearly 20%. The scalability of the system will al so be discussed.

On the analysis of open source datasets: validating IDS implementation for well-known and zero day attack detection

This paper presents the implementation of an anomaly-based Intrusion Detection System (IDS), capable to detect well-known and zero-day attacks. First, we extend our previous work by generating the Machine Learning (ML) predictors based on KDD99, NSL-KDD and CIC-IDS2018 datasets, and providing the programming language evaluation and the final validation platform. We have built IDS detection solution in two phases. The first Training phase explores available datasets to generate the predictors. The second phase is composed of two processes. Extraction generates the statistical network traffic metrics from the PCAP files and processes them into commma separated values (CSV) files. The Prediction loads predictors in main memory and feeds them with CSV files to predict the well-known and zero-day attacks. The aforementioned initial datasets contain the statistical network traffic metrics of the well-known attacks, collected at runtime execution of the malicious software. Zero day attacks can generate a statistical network traffic metrics similar to well-known attacks. Therefore, to showcase the zero-day anomaly detection, we realise a validation platform. Six attacks (three Denial of Service (DoS) and three scanning), not recorded in the initial datasets, are executed in an isolated environment. The achieved result indicates a misclassification prediction error that inhibits the application of the automatic attack responses, although the misclassification errors were minimised, during the Training phase.

A Review on Intrusion Detection Systems and Techniques

An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one’s disposal.