Evaluation Of Intrusion Detection Systems Research Articles

Implementation and Evaluation of Intrusion Detection Systems using Machine Learning Classifiers on Network Traffic Data

Strong Intrusion Detection Systems (IDS) are now essential given how much more crucial services and communication are being reliant on digital networks. Through the use of machine learning classifiers on network traffic data, this research shows the deployment and thorough evaluation of IDS. The first step of the study is to gather and preprocess a wide dataset of network traffic, which includes both legitimate and criminal operations. A high-dimensional feature set is produced when important information is extracted from the raw data using feature engineering techniques. In order to simulate the patterns of network traffic, a variety of machine learning methods are used, such as Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. The models are also put to the test in a variety of situations, such as those with changing levels of network traffic, different kinds of attacks, and false-positive rates. Results show that machine learning-based IDS is more accurate than conventional rule-based systems at identifying and categorising network intrusions. Assessments are made of the models' ability to scale up and change to accommodate new threats. A thorough examination of IDS utilising machine learning classifiers on actual network traffic data is provided in this research, which, in turn, advances network security. The results highlight the value of machine learning in improving the precision and sturdiness of intrusion detection systems and protecting crucial network infrastructures from new cyber threats.

“Intrusion Detection System Evaluation “A Comparative Study of Machine Learning Algorithms

The need for cheaper and faster delivery in the electronics industry has increased as a result of information technology advancements. The quick development of technology not only makes life simpler but also raises several security concerns. The number of attacks conducted online has increased as the Internet has developed through time. One of the supporting layers that can be used for information security is the intrusion detection system (IDS). IDS offers a clean atmosphere for conducting business and steers clear of shady network activity. The security on the user's end of web transactions is the most difficult task in the construction of an e-commerce system. This study examined intrusion detection security techniques. Continuous monitoring of intrusion detection is required for further technological adaptation, and as a result, presents a comparative comparison of adaptive artificial intelligence-based intrusion detection algorithms. This work shows how reinforcement learning (RL) and regression learning-based intrusion detection systems (IDS) can be used to solve extremely difficult issues, such as choosing input features and taking limited resources into account.

A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset

We propose and evaluate a data-driven intrusion detection system (IDS) for the Internet of Things (IoT) and Industrial IoT (IIoT) environments using the Edge-IIoT-2022 dataset. We model the IDS problem as a classification problem and learn the classifier via supervised learning algorithms. Our main contribution is an empirical analysis and evaluation of the Edge-IIoT-2022 dataset, which is a recent dataset compiled for developing IDSs in IoT and IIoT environments. We develop several IDSs from standard data analytics algorithms and evaluate their performance on Edge-IIoT-2022. We compare our IDSs with prior arts and demonstrate that highly accurate binary-class IDSs can be built via Edge-IIoT-2022, whereas multi-class IDSs would require careful treatment.

Maintainable Log Datasets for Evaluation of Intrusion Detection Systems

Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their selection in specific use-cases. Despite a great need, hardly any labeled intrusion detection datasets are publicly available. As a consequence, evaluations are often carried out on datasets from real infrastructures, where analysts cannot control system parameters or generate a reliable ground truth, or private datasets that prevent reproducibility of results. As a solution, we present a collection of maintainable log datasets collected in a testbed representing a small enterprise. Thereby, we employ extensive state machines to simulate normal user behavior and inject a multi-step attack. For scalable testbed deployment, we use concepts from model-driven engineering that enable automatic generation and labeling of an arbitrary number of datasets that comprise repetitions of attack executions with variations of parameters. In total, we provide 8 datasets containing 20 distinct types of log files, of which we label 8 files for 10 unique attack steps. We publish the labeled log datasets and code for testbed setup and simulation online as open-source to enable others to reproduce and extend our results.

Multi-Attribute Decision-Making for Intrusion Detection Systems: A Systematic Review

Intrusion detection systems (IDSs) employ sophisticated security techniques to detect malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security of computer and network systems. However, numerous evaluation and selection issues related to several cybersecurity aspects of IDSs were solved using a decision support approach. The approach most often utilized for decision support in this regard is multi-attribute decision-making (MADM). MADM can aid in selecting the most optimal solution from a huge pool of available alternatives when the appropriate evaluation attributes are provided. The openness of the MADM methods in solving numerous cybersecurity issues makes it largely efficient for IDS applications. We must first understand the available solutions and gaps in this area of research to provide an insightful analysis of the combination of MADM techniques with IDS and support researchers. Therefore, this study conducts a systematic review to organize the research landscape into a consistent taxonomy. A total of 28 articles were considered for this taxonomy and were classified into three main categories: data analysis and detection ([Formula: see text]), response selection ([Formula: see text]) and IDS evaluation ([Formula: see text]). Each category was thoroughly analyzed in terms of a variety of aspects, including the issues and challenges confronted, as well as the contributions of each study. Furthermore, the datasets, evaluation attributes, MADM methods, evaluation and validation and bibliography analysis used by the selected articles are discussed. In this study, we highlighted the existing perspective and opportunities for MADM in the IDS literature through a systematic review, providing researchers with a valuable reference.

Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0. Then, we evaluate intrusion detection systems according to emerging technologies, including, Cloud computing, Fog/Edge computing, Network virtualization, Autonomous tractors, Drones, Internet of Things, Industrial agriculture, and Smart Grids. Based on the machine learning technique used, we provide a comprehensive classification of intrusion detection systems in each emerging technology. Furthermore, we present public datasets, and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0. Finally, we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

Performance evaluation of intrusion detection system using new support vector machine model

Performance evaluation of intrusion detection system using new support vector machine model

Performance evaluation of intrusion detection system using new support vector machine model

Performance evaluation of intrusion detection system using new support vector machine model

Systematic Literature Review over IDPS, Classification and Application in its Different Areas

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.

Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models

Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack scenarios based on evolving threats. The development of a robust network intrusion dataset is vital for network threat analysis and mitigation. Proactive IDSs are required to tackle ever-growing threats in cyberspace. Machine learning (ML) and deep learning (DL) models have been deployed recently to detect the various types of cyber-attacks. However, current IDSs struggle to attain both a high detection rate and a low false alarm rate. To address these issues, we first develop a Center for Cyber Defense (CCD)-IDSv1 labeled flow-based dataset in an OpenStack environment. Five different attacks with normal usage imitating real-life usage are implemented. The number of network features is increased to overcome the shortcomings of the previous network flow-based datasets such as CIDDS and CIC-IDS2017. Secondly, this paper presents a comparative analysis on the effectiveness of different ML and DL models on our CCD-IDSv1 dataset. In this study, we consider both cyber anomaly detection and multi-attack classification. To improve the performance, we developed two DL-based ensemble models: Ensemble-CNN-10 and Ensemble-CNN-LSTM. Ensemble-CNN-10 combines 10 CNN models developed from 10-fold cross-validation, whereas Ensemble-CNN-LSTM combines base CNN and LSTM models. This paper also presents feature importance for both anomaly detection and multi-attack classification. Overall, the proposed ensemble models performed well in both the 10-fold cross-validation and independent testing on our dataset. Together, these results suggest the robustness and effectiveness of the proposed IDSs based on ML and DL models on the CCD-IDSv1 intrusion detection dataset.

Intrusion detection for network based cloud computing by custom RC-NN and optimization

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

A Machine Learning Approach for Improving the Performance of Network Intrusion Detection Systems

Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.

LEAESN: Predicting DDoS attack in healthcare systems based on Lyapunov Exponent Analysis and Echo State Neural Networks

The availability of the system is one of the main requirements of a multimedia-based e-health application that carries critical patient health information in the network environment. On the other hand, the Distributed Denial of Service (DDoS) attack is one of the most common attacks on the availability of computer networks which can be devastating for a healthcare system. Therefore, a countermeasure to this attack has to be performed in the early steps of the attack to protect the systems against its damages. Detection methods cannot support this and are only able to detect the attack after it happened. Thus, it is necessary to predict DDoS attacks according to the evidence which the attack makes in the network in the early steps of the attack. Therefore, Prediction approaches can reduce the cost of the attacks compared to detection approaches. In this paper, we propose a new method for prediction of DDoS attack based on Lyapunov Exponent Analysis and Echo State Network (LEAESN). In this method, the future traffic of the network is predicted using the Exponential Smoothing technique, then the time series of the prediction error is calculated based on the difference of this prediction and the observed traffic of the network. As shown in this paper, this time series is chaotic in the presence of attack traffics. To predict the DDoS attack, this time series is predicted using a Recurrent Neural Echo State Network (SCESN), and the attack is detected using Lyapunov exponent analysis on the predicted time series. For the evaluation of LEAESN, we test the method on the Darpa98 dataset which consists of a standard dataset for evaluation of intrusion detection systems. LEAESN has an appropriate ability to predict the DDoS attack.

Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed

Evaluations of intrusion detection systems (IDS) require log datasets collected in realistic system environments. Existing testbeds therefore offer user simulations and attack scenarios that target specific use-cases. However, not only does the preparation of such testbeds require domain knowledge and time-consuming work, but also maintenance and modifications for other use-cases involve high manual efforts and repeated execution of tasks. In this article, we therefore propose to generate testbeds for IDS evaluation using strategies from model-driven engineering. In particular, our approach models system infrastructure, simulated normal behavior, and attack scenarios as testbed-independent modules. A transformation engine then automatically generates arbitrary numbers of testbeds, each with a particular set of characteristics and capable of running in parallel. Our approach greatly improves configurability and flexibility of testbeds and allows to reuse components across multiple scenarios. We use our proof-of-concept implementation to generate a labeled dataset for IDS evaluation that is published with this article.

CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

Real time dataset generation framework for intrusion detection systems in IoT

The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.

A Sequential Detection Method for Intrusion Detection System Based on Artificial Neural Networks

With rapidly increasing cyber attacks, network security has become an important issue. To protect ourselves against cyber attacks, the Intrusion Detection System (IDS) has been introduced. In such systems, different kinds of machine learning algorithms play a more and more important role, such as support vector machine(SVM), artificial neural network(ANN), etc. False positive rate and false negative rate, in addition to accuracy, are widely used for the evaluation of IDSs. These indices, however, are often related to each other, which makes it is difficult for us to improve all the indices at the same time. For example, when we try to make the false negative rate decrease to prevent from missing attacks, more normal communications tend to be classified into attacks and the false positive rate may increase, and vice versa. In this study, we propose an ANN based sequential classifier method to mitigate this problem. We design each subclassifier with a low false positive rate, which may lead to high false negative rate. To decrease the false negative rate, the reported negative instances from the former subclassifier are sent to the next one to further check (reclassification). In this way, it can be expected that the false negative rate can also reach an acceptable level. The results of our experiment shows that our proposed method can bring lower false negative rate and higher accuracy, in the mean time the false positive rate is kept at an acceptable level. We also investigated the effect of the number of subclassifiers on detection performance and found that the detection system performed best when using four subclassifiers.

A Survey of Intrusion Detection Systems Leveraging Host Data

This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. To accommodate current researchers, a section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research. Overall, this survey was designed to allow easy access to the diverse types of data available on a host for sensing intrusion, the progressions of research using each, and the accessible datasets for prototyping in the area.

Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.

Assessing degree of intrusion scope (DIS): a statistical strategy for anomaly based intrusion detection

Intrusion detection system (IDS) is a type of security management system which analyzes information gathered from various areas within a computer or a network to identify possible security breaches. In the last decades an unprecedented increase in the volume and sophistication of network attacks are witnessed. As the quality of the training data greatly influences the quality of the learned models it is difficult to collect high quality training data. New attacks leveraging newly discovered security vulnerabilities emerge quickly and frequently, and also it is not possible to collect data related to these new attacks to train a detection model before the attacks are discovered and understood. The exponential growth of zero-day attacks emphasizes the need of defence mechanisms that can accurately detect previously unseen attacks in real-time. In this regard, a meta-heuristic assessment model called assessing degree of intrusion scope, which is aimed to estimate the degree of intrusion scope threshold from optimal features of given network transaction for training. In order to evaluate the proposed approach, widely used dataset for evaluation of IDS, NSL-KDD data set is used which reflects the network traffic and provides considerable and consistent accuracy improvements in detecting the new and existing attacks. The experimental results indicating that the feature correlation is having significant impact towards minimizing the computational and time complexity of measuring Intrusion Impact Scale.