Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic

Ferriyan, Andrey; Thamrin, Achmad Husni; Takeda, Kazuya; Murai, Jun

doi:10.3390/app11177868

Cited by 51 publications

(25 citation statements)

References 52 publications

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“… Brute-Force Web: attacks that aim at cracking vulnerable computer networks that depend on weak user credentials, which consist of weak usernames and passwords [11,20,21].…”

Section: A Network Attacksmentioning

confidence: 99%

“… Distributed Denial of Service (DDoS): attacks designed to target network or web servers with limited bandwidth by overwhelming them with requests from tens or hundreds of distributed URL addresses [11,22,23]. DDOS LOIC-UDP stands for Distributed Denial of Service (DDoS) Low Orbit Ion Cannon (LOIC) Datagram Protocol (UDP).…”

Section: A Network Attacksmentioning

confidence: 99%

“… SQL Injection: stands for Structured Query Language (SQL) attacks. It works by injecting code into databases to gain unauthorized access to data by executing unsolicited queries or damaging the integrity of your database [11,[24][25][26][27].…”

Section: A Network Attacksmentioning

confidence: 99%

“…The dataset in this work was sampled from a publicly simulated big dataset which was created by the Communications Security Establishment (CSE) and the Canadian Institute of Cybersecurity (CIC) for Intrusion Detection Systems (IDS). The dataset was published under the name CSE-CIC-IDS2018 [11,12]. The original dataset comprises 16 million samples described using 80 features related to the traffic flow on the network [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Securing Traffic in Computer Networks

BaniMustafa¹,

Baklizi²,

Khatatneh³

2022

IJACSA

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Computer network attacks are among the most significant and common threats against computer-wired and wireless communications. Intrusion detection technology is used to secure computer networks by monitoring network traffic and identifying attacks. In this paper, we investigate and evaluate the application of four machine learning classification algorithms for identifying attacks that target computer networks: DDoS, Brute Force Web, and SQL Injection attacks, in addition to Benign Traffic. A public dataset of 80 features was used to build four machine learning models using Random Forest, Logistic Regression, CN2, and Neural Networks. The constructed models were evaluated based on 10-fold cross-validation using Classification Accuracy (CA), Area under the Curve (AUC), F1, Recall, Specificity, and Sensitivity metrics in addition to Confusion Matrix, Calibration, Lift, and ROC plots. The Random Forest model achieved 98% in the CA score and 99% in the AUC score, while the Logistic regression achieved 90% in the CA score and 98% in the AUC score.

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“… Brute-Force Web: attacks that aim at cracking vulnerable computer networks that depend on weak user credentials, which consist of weak usernames and passwords [11,20,21].…”

Section: A Network Attacksmentioning

confidence: 99%

Section: A Network Attacksmentioning

confidence: 99%

Section: A Network Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Securing Traffic in Computer Networks

BaniMustafa¹,

Baklizi²,

Khatatneh³

2022

IJACSA

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“…Ferriyan et al [14] developed several machine learning models for detecting cyberattacks, and used a new dataset called ALLFLOWMETER HIKARI2021. Background, Benign, Bruteforce, Bruteforce-XML, Probing, and XMRIGCC CryptoMiner are the six types of attacks represented among the 555,278 instances and 86 features extracted by Zeek [https://zeek.org/] in this dataset.…”

Section: Related Workmentioning

confidence: 99%

Cybersecurity in Deep Learning Techniques: Detecting Network Attacks

Ghazal¹,

Mjlae²

2022

IJACSA

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Deep learning techniques have been found to be useful in a variety of fields. Cybersecurity is one such area. In cybersecurity, both Machine Learning and Deep Learning classification algorithms can be used to monitor and prevent network attacks. Additionally, it can be utilized to identify system irregularities that may signal an ongoing attack. Cybersecurity experts can utilize machine learning and deep learning to help make systems safer. Eleven classification techniques, including eight machine learning algorithms (Decision Tree, Random Forest, and Gradient Boosting) and one statistical technique, were employed to examine the popular HTTP DATASET CSIC 2010. (K-Means). Along with XGBoost, AdaBoost, Multilayer Perceptrons, and Voting, three deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and LSTM plus CNN. To evaluate the performance of such models, precision, accuracy, f1score, and recall are often used metrics. The results showed that when comparing the three deep learning algorithms by the aforementioned metrics, the LSTM with CNN produced the best performance outcomes in this paper. These findings will show that our use of this algorithm allows us to detect multiple attacks and defend against any external or internal threat to the network.

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A Simple Distributed Approach for Running Machine Learning Based Simulations in Intrusion Detection Systems

Fernandes,

Lopes

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic

Cited by 51 publications

References 52 publications

Machine Learning for Securing Traffic in Computer Networks

Machine Learning for Securing Traffic in Computer Networks

Cybersecurity in Deep Learning Techniques: Detecting Network Attacks

A Simple Distributed Approach for Running Machine Learning Based Simulations in Intrusion Detection Systems

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