A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Yin, Chuanlong; Zhu, Yuefei; Fei, Jinlong; He, Xinzheng

doi:10.1109/access.2017.2762418

Cited by 1,421 publications

(705 citation statements)

References 21 publications

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“…Table II confirms that A(X) achieves better results compared to another work included two different methods [9]. Although these methods are semi-supervised, they used anomalous records of [10] 83.28 DCNN [11] 85.00 AE [9] 88.28 Sparse AE and MLP [12] 88.39 Random Tree [13] 88.46 De-noising AE [9] 88.65 LSTM [11] 89.00 Random Tree and NBTree [13] 89.24 Ours A(X) 91.39…”

Section: B Evaluationsupporting

confidence: 66%

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Mohammadi

Sabokrou

2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity of network attacks in addition to the need for generalization, motivate us to propose a semi-supervised method. Inspired by the successes of Generative Adversarial Networks (GANs) for training deep models in semi-unsupervised setting, we have proposed an endto-end deep architecture for IDS. The proposed architecture is composed of two deep networks, each of which trained by competing with each other to understand the underlying concept of the normal traffic class. The key idea of this paper is to compensate the lack of anomalous traffic by approximately obtain them from normal flows. In this case, our method is not biased towards the available intrusions in the training set leading to more accurate detection. The proposed method has been evaluated on NSL-KDD dataset. The results confirm that our method outperforms the other state-of-the-art approaches.

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Section: B Evaluationsupporting

confidence: 66%

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Mohammadi

Sabokrou

2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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“…The evaluation demonstrates improved performance over the threshold-based approach with higher precision and recall. In [48], a Recurrent Neural Network (RNN)-Intrusion Detection System (IDS) is compared with a series of previously presented ML techniques (e.g. J48, Artificial Neural Network (ANN), Random Forest, and SVM) applied to the NSL-KDD [49] dataset.…”

Section: Deep Learning For Ddos Detectionmentioning

confidence: 99%

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Doriguzzi-Corin

Millar

Scott-Hayward

et al. 2020

IEEE Trans. Netw. Serv. Manage.

249

105

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Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing stateof-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.

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“…In [9] the authors developed a sparse autoencoder based classifier and reported 79.10% accuracy. In [22] the authors developed a RNN based system and reported multiclass accuracy of 81.29%. In [15], the authors proposed a stacked non-symmetric deep autoencoders and reported 5-class accuracy of 85.42%.…”

Section: Resultsmentioning

confidence: 99%

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection

Ieracitano

Adeel

Gogate

et al. 2018

Lecture Notes in Computer Science

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Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks have made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods, followed by a deep autoencoder (AE) for potential threat detection. Specifically, a preprocessing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discards features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed IDS system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.

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A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Cited by 1,421 publications

References 21 publications

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection

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