Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Iman Sharafaldin,Arash Habibi Lashkari,Ali A. Ghorbani +2 more
- pp 108-116
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TLDR
A reliable dataset is produced that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable and evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.Abstract:
With exponential growth in the size of computer networks and developed applications, the significant increasing of the potential damage that can be caused by launching attacks is becoming obvious. Meanwhile, Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are one of the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of adequate dataset, anomaly-based approaches in intrusion detection systems are suffering from accurate deployment, analysis and evaluation. There exist a number of such datasets such as DARPA98, KDD99, ISC2012, and ADFA13 that have been used by the researchers to evaluate the performance of their proposed intrusion detection and intrusion prevention approaches. Based on our study over eleven available datasets since 1998, many such datasets are out of date and unreliable to use. Some of these datasets suffer from lack of traffic diversity and volumes, some of them do not cover the variety of attacks, while others anonymized packet information and payload which cannot reflect the current trends, or they lack feature set and metadata. This paper produces a reliable dataset that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable. Consequently, the paper evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.read more
Citations
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Fog-Based Attack Detection Framework for Internet of Things Using Deep Learning
TL;DR: The proposed framework is effective in terms of response time and detection accuracy and can detect several types of cyber-attacks with 99.97% detection rate and 99.96% detection accuracy.
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An Intrusion Detection System Against DDoS Attacks in IoT Networks
TL;DR: An Intrusion Detection System (IDS) founded on the fusion of a Jumping Gene adapted NSGA-II multi-objective optimization method for data dimension reduction and the Convolutional Neural Network integrating Long Short-Term Memory (LSTM) deep learning techniques for classifying the attack is proposed.
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APT datasets and attack modeling for automated detection methods: A review
TL;DR: The major achievement is the description and analysis of existing feature extraction methodologies and detailed overview of datasets used in APT detection related literature, showing that the large enterprise network use case, has incorporated a much more frequent use of datasets with quite short periods of time.
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Detecting Port Scan Attempts with Comparative Analysis of Deep Learning and Support Vector Machine Algorithms
Dogukan Aksu,M. Ali Aydin +1 more
TL;DR: Deep learning and support vector machine (SVM) algorithms were used to detect port scan attempts based on the new CICIDS2017 dataset and 97.80%, 69.79% accuracy rates were achieved respectively.
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Evaluation of Machine Learning Algorithms for Anomaly Detection
TL;DR: This paper evaluates twelve Machine Learning algorithms in terms of their ability to detect anomalous behaviours over the networking practice and verifies that the Random Forest algorithm achieves the best performance on all these datasets.
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