Network Intrusion Datasets Used in Network Security Education

ABSTRACT There is a gap between the network security graduate and the professional life. In this paper we discussed the different types of network intrusion dataset and then we highlighted the fact that any student can easily create a network intrusion dataset that is representative of the network they are in. Intrusions can be in form of anomaly or network signature; the students cannot grasp all types but they have to have the ability to detect malicious packets within his network. Original Source URL : http://aircconline.com/ijite/V7N3/7318ijite04.pdf For more details : http://airccse.org/journal/ijite/current.htm

До проблеми формування набору даних для дослідження DDoS-атак

В роботі розглянуто підходи щодо перевірки запропонованих методів виявлення атак. Проаналізовано наявні набори даних, які використовуються для створення систем виявлення DDoS-атак. Також, проаналізовано декілька інструментів, що використовуються для реалізації чи моделювання DDoS-атак для збору даних.The paper considers approaches to checking the proposed method of detecting attacks. The existing datasets that scientists use to create DDoS-attack detection systems are analyzed. Also, there are several tools used to implement or simulate DDoS-attacks for data collectio

Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy

Feature Selection with IG-R for Improving Performance of Intrusion Detection System

As the popularity of the internet computer continued to grow and become an indispensable in human life, the security of computer network has become an important issue in computer security field. The Intrusion Detection System (IDS) is a system used in computer security for network security. The feature selection stage of IDS is considered to be the most critical stage in IDS. This stage is very costly both in efforts and time. However, many machine learning approaches have been presented to improve this stage in order to improve the performance of an IDS. However, these approaches did not give desirable results with respect to the detection accuracy in the IDS. A novel technique is proposed in this paper combining the Information Gain and Ranker (IG+R) method as the feature selection strategy with Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classifiers. The performance of these IG+R-NB, IG+R-SVM, and IG+R-KNN was evaluated on NSLKDD dataset. The experimental results of our proposed method gave high accuracy and low false alarm rate. The results obtained was compared and benchmarked with existing works. The results of this paper outperformed the existing approaches in terms of the detection accuracy

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

Publicly Available Datasets For Electric Load Forecasting – An Overview

Accurate electrical load forecasting is increasingly important, particularly due to digitalization and recently emerging use cases for automated planning and control as part of the I4.0 transition in the manufacturing industry. To enable transparent research and reproducible load forecasting experiments, researchers need access to public and reusable datasets. Although there are such de-facto-standard datasets for the more general research field of time series forecasting, these are not transferable to electric load forecasting, as important external factors and industry-specific characteristics, especially in the industrial sector, are missing. This paper presents a structured literature review of existing load forecasting publications identifying suitable open-access datasets (Open Data) to help other researchers in using them. It also examines the extent to which transparent research is possible and already implemented in the field of electric load forecasting research. For this purpose, 25 unique and publicly accessible datasets were extracted from a representative set of 160 publications. The result datasets are grouped thematically, features are presented, and popularity trends are evaluated. Subsequent analysis shows a non-transparent, poorly reproducible, and methodologically weak research landscape: 54% of all publications use exclusively inaccessible private datasets for validation. Most publications (80%) use only a single dataset, 94% at most two datasets for validation – independent of journal or conference contribution. Although datasets that cover the residential and system consumption sectors are available, there are no popular public datasets showing industrial and manufacturing consumptions available for research

Network Attack Detection using an Unsupervised Machine Learning Algorithm

With the increase in network connectivity in today\u27s web-enabled environments, there is an escalation in cyber-related crimes. This increase in illicit activity prompts organizations to address network security risk issues by attempting to detect malicious activity. This research investigates the application of a MeanShift algorithm to detect an attack on a network. The algorithm is validated against the KDD 99 dataset and presents an accuracy of 81.2% and detection rate of 79.1%. The contribution of this research is two-fold. First, it provides an initial application of a MeanShift algorithm on a network traffic dataset to detect an attack. Second, it provides the foundation for future research involving the application of MeanShift algorithm in the area of network attack detection