Unsw-nb15 Dataset Download Kaggle __HOT_

UNSW-NB15 is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,332 records from the different types, attack and normal.

The TON_IoT datasets are new generations of Industry 4.0/Internet of Things (IoT) and Industrial IoT (IIoT) datasets for evaluating the fidelity and efficiency of different cybersecurity applications based on Artificial Intelligence (AI), i.e., Machine/Deep Learning algorithms. The datasets can be downloaded from HERE. You can also use our datasets: the BoT-IoT and UNSW-NB15.

Unsw-nb15 Dataset Download Kaggle

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The datasets can be used for validating and testing various Cybersecurity applications-based AI such as intrusion detection systems, threat intelligence, malware detection, fraud detection, privacy-preservation, digital forensics, adversarial machine learning, and threat hunting.

The datasets have been called 'ToN_IoT' as they include heterogeneous data sources collected from Telemetry datasets of IoT and IIoT sensors, Operating systems datasets of Windows 7 and 10 as well as Ubuntu 14 and 18 TLS and Network traffic datasets. The datasets were collected from a realistic and large-scale network designed at the Cyber Range and IoT Labs, the School of Engineering and Information technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). A new testbed network was created for the industry 4.0 network that includes IoT and IIoT networks. The testbed was deployed using multiple virtual machines and hosts of windows, Linux and Kali operating systems to manage the interconnection between the three layers of IoT, Cloud and Edge/Fog systems. Various attacking techniques, such as DoS, DDoS and ransomware, against web applications, IoT gateways and computer systems across the IoT/IIoT network. The datasets were gathered in a parallel processing to collect several normal and cyber-attack events from network traffic, Windows audit traces, Linux audit traces, and telemetry data of IoT services.

Free use of the TON_IoT datasets for academic research purposes is hereby granted in perpetuity. Use for commercial purposes is allowable after asking the author, Dr Nour Moustafa, who has asserted his right under the Copyright. The datasets was sponsored by the grants from the Australian Research Data Commons, -and-services-discovery-activities-successful-applicants/, and UNSW Canberra. To whom intend the use of the TON_IoT datasets have to cite the above eight papers.

Data mining is a relatively new discipline that arose in response to the proliferation of digital information. Security and privacy issues have gained increased attention as the internet's data storage capacity continues to grow. Problems with data theft and intrusion are a common source of frustration for users. In order to anticipate and identify intrusions, this study suggests developing a model with the XGBoost and Random Forest algorithms. Python Anaconda and Kaggle datasets (found at www.kaggle.com) are integral parts of the study methodology. The research uses the XGBoost and Random Forest algorithms on the UNSW-NB15 2017 and KDD datasets, respectively. The XGBoost algorithm performs admirably on the first dataset, with 100% accuracy, precision, and recall, and a perfect F1-score. In addition, on the second dataset, both algorithms attain near-perfect accuracy (99% and 98%, respectively), after the pre-processing stages (normalization, feature selection, scaling of the dataset) and the application of Synthetic Minority Over-sampling Techniques (SMOTE). These findings shed light on the algorithms' capabilities and how well they achieve the study's goals.

Khraisat A, Alazab A. A critical review of intrusion detection systems in the Internet of Things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cyber Security. 2021;4(18). DOI: 10.1186/s42400-021-00077-7

Hussein A, Li T, Chubato W, Bashir K. A-SMOTE: A new preprocessing approach for highly imbalanced datasets by improving SMOTE. International Journal of Computational Intelligence Systems. 2019;12(1). DOI:10.2991/ijcis.d.191114.002.

Numerous researchers suggested machine learning (ML) methods to find and identify network attackers, including SVM, KNN, RF, and NB [11,12]. These methods, which have a greater computational cost, are based on conventional ML. They are shallow learners; therefore they do not gain a deeper understanding of their datasets [13]. Additionally, they issue warnings that are partially misleading (i.e., they raise false alarms).

In the past few years, a large number of IDS techniques have been presented based on a variety of approaches, such as mathematical formulations, data mining techniques like machine learning, etc. Poor performances are caused by the difficulty in managing the high-dimensional network traffic data with these statistical formulations and conventional machine learning models [14]. Furthermore, the majority of the techniques used binary classification, such as whether it is an attack or not. Therefore, better approaches are required for IDS, such as deep-learning-based techniques. Due to its powerful learning and feature extraction capabilities, particularly in scenarios involving large datasets, deep learning has been widely recommended for IDS in recent years [15]. Multiple layers are used in deep learning approaches to gradually extract important features from raw input without the need for domain knowledge.

To tackle the issue of negative and positive instance imbalance in the initial dataset, Cao et al. [25] developed an ensemble sampling method that combines ADASYN and RENN. To solve the issue of feature redundancy, the RF algorithm and Pearson correlation analysis are combined to pick the features. The spatial features are then retrieved using a CNN and further extracted by fusing average pooling and maxpooling, as well as utilizing an attention strategy to apply varying weights to the features, decreasing overhead and boosting method effectiveness. To ensure effective and useful feature learning, the long-distance dependent information features are extracted using a gated recurrent unit (GRU). The experimental results show that the suggested approach yields greater performance.

The ratio of samples of the normal type to samples of the worm attack type across the entire data set is 534:1. Less than 1% of the attack samples for backdoors and shell code are presented. The class distribution of the UNSW-NB15 dataset is shown in Figure 4. Table 5 represents the features of the UNSW-NB15 dataset.

CICIDS2017 dataset: There are 2,830,473 samples of network traffic in the dataset, of which benign traffic makes up 80.30% and attack traffic represents 19.70%. There is one normal class and 14 assault types. The assault types include the most prevalent attack types, like port scan, DDoS, web attacks, botnet, DoS, etc. The last column of the dataset, which contains the multiclass label, contains 84 features that were extracted from the generated network traffic. Table 6 provides the data distribution for each class.

Table 10 compares the performance of the proposed work to that of other cutting-edge approaches tested on the CICIDS2019 and UNSW-NB15 datasets. Based on the table, the proposed approach-based IDS model achieves the highest results in terms of recall, accuracy, F1-Score, and precision.

Figure 8 represents the graphical representation of accuracy and TPR on the CICIDS-2017 dataset. When the differentiation can be made with the existing techniques, our proposed approach yields greater performance.

For the camera surveillance scenario, we use the UNSW-NB15 and CICIDS2017 datasets, which total 5.37 million packets. In Section 4.1, we take a look at the same setup function. The first half of the packets are employed to train the proposed IGAN method, and the remainder of the packets are utilized to train the Ensemble approach. T = 0.04 is chosen to yield the best results.

Abstract:The cyber security field has witnessed several intrusion detection systems (IDSs) that are critical to the detection of malicious activities in network traffic. In the last couple of years, much research has been conducted in this field; however, in the present circumstances, network attacks are increasing in both volume and diverseness. The objective of this research work is to introduce new IDSs based on a combination of Genetic Algorithms (GAs) and Optimized Gradient Boost Decision Trees (OGBDTs). To improve classification, enhanced African Buffalo Optimizations (EABOs) are used. Optimization Gradient Boost Decision Trees (OGBDT-IDS) include data exploration, preprocessing, standardization, and feature ratings/selection modules. In high-dimensional data, GAs are appropriate tools for selecting features. In machine learning techniques (MLTs), gradient-boosted decision trees (GBDTs) are used as a base learner, and the predictions are added to the set of trees. In this study, the experimental results demonstrate that the proposed methods improve cyber intrusion detection for unused and new cases. Based on performance evaluations, the proposed IDS (OGBDT) performs better than traditional MLTs. The performances are evaluated by comparing accuracy, precision, recall, and F-score using the UNBS-NB 15, KDD 99, and CICIDS2018 datasets. The proposed IDS has the highest attack detection rates, and can predict attacks in all datasets in the least amount of time.Keywords: cyber security; IDS; GA; OGBDT; EABO

The research gaps that are identified from the study on distinct attack detection frameworks are observed, for instance, (i) performance accuracy or exactness is evaluated on smaller datasets with fewer attributes which fall behind in better attack detection. Hence, we considered newer datasets DDoS-SDN [16] and IoTID20 [19] datasets with huge number of instances and attributes. (ii) Even with the increase in dataset size, most of the prognostications are made on conventional ML algorithms which do not yield better accuracy for attack detection and became cumbersome to decide best ML algorithm on selected datasets. (iii) From the observations on many datasets, we listed only fewer number of attacks and hence need to be considered dataset with more number of attacks which helps in better prognostication of attacks. ff782bc1db