LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

Damaševičius, Robertas; Venčkauskas, Algimantas; Grigaliūnas, Šarūnas; Toldinas, Jevgenijus; Morkevičius, Nerijus; Aleliunas, Tautvydas; Smuikys, Paulius

doi:10.3390/electronics9050800

Cited by 89 publications

(56 citation statements)

References 76 publications

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“…In this section, we performed our experiments with the most recent, publicly available benchmark dataset, LITNET-2020 [ 29 ], published by Kaunas Technological University in May 2020. To examine the performance of our framework using selected features, we first trained the individual classifiers and stacked ensemble framework using five-fold cross-validation and present the results in terms of accuracy, average accuracy, standard deviation, and SEM in Table 2 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

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A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

Dutta

Choraś

Pawlicki

et al. 2020

Sensors

103

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Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…The LITNET-2020 ( ) NetFlow dataset [ 29 ] consists of senders and collectors. The senders are made up of Cisco routers and Fortige (FG-1500D) firewalls, which were utilized to evaluate NetFlow data passing through the collectors.…”

Section: Selection Of Datasetsmentioning

confidence: 99%

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

Dutta

Choraś

Pawlicki

et al. 2020

Sensors

103

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“…Some of these studies were dedicated for VANET 41,42 and others were not. 43,44 Lyamin et al 42 proposed a heuristic approach derived from data mining methods for real-time detection of radio jamming DoS attacks in a VANET communication environment. To train the proposed detection model, they conducted a simulation experiment using MATLAB to generate a sort of dataset holding cooperative awareness message (CAM) transmissions in the IEEE 802.11p protocol.…”

Section: Framework-based Studies For Generating Datasetsmentioning

confidence: 99%

“…41,42 (6) Finally, Category 6: studies introducing the generation of datasets for detecting attacks but not considering the specifications of VANETs. 43,44…”

Section: Literature Reviewmentioning

confidence: 99%

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

Alhaidari

Alrehan

2021

International Journal of Distributed Sensor Networks

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Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

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“…There are 12 types of attack e.g., Smurf, ICMP flood, TCP flood, UDP flood, Http flood, Land attack, Blaster worm, Code Red worm, SPAM Reaper worm, Reaper worm, Scanning attack, Fragmentation attack. Each type of attack contains 85 network flow features [48]. The normal and under-attack flow datasets can be downloaded from https://dataset.litnet.lt, and the two datasets are reconstructed one dataset including the normal and underattack data.…”

Section: The Impact Of the Cdbn Sturcture And Data Preprocessing Omentioning

confidence: 99%

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

Yang

Yin

et al. 2020

IEEE Access

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With the development of the wireless network techniques, the number of cyber-attack increases significantly, which has seriously threat the security of Wireless Local Area Network (WLAN). The traditional intrusion detection technology is a prevalent area of study for numerous years, but it may not have a good detection performance in a real-time way. Therefore, it is urgent to design a detection mechanism to detect the attacks timely. In this paper, we exploit a CDBN (Conditional Deep Belief Network)-based intrusion detection mechanism to recognize the attack features and perform the wireless network intrusion detection in real time. To avoid the impact of the imbalanced dataset and the data redundancy on the detection accuracy, a windowbased instance selection algorithm-SamSelect‖ is adopted to undersample the majority class data samples, and a Stacked Contractive Auto-Encoder (SCAE) algorithm is proposed to reduce the dimension of the data samples. By doing so, our proposed mechanism can effectively detect the potential attack and achieve high accuracy. The experiment results show that CDBN can be effectively combined with-SamSelect‖ and SCAE, and the proposed mechanism has a high detection speed and accuracy, with the average detection time 1.14 ms and the detection accuracy 0.974.

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LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

Cited by 89 publications

References 76 publications

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

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