CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

Kim, Jiyeon; Kim, Jiwon; Kim, Hyun-Jung; Shim, Minsun; Choi, Eun‐Jung

doi:10.3390/electronics9060916

Cited by 266 publications

(144 citation statements)

References 53 publications

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“…In [21], solution the deep learning is used with a convolutional neural network to develop a deep learning model. It investigates some of the advanced DDoS attacks.…”

Section: Anomaly Detection and Classification Algorithmsmentioning

confidence: 99%

Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems

Babic¹,

Miljković²,

Čabarkapa

et al. 2021

Symmetry

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This paper presents a novel approach for an Intrusion Detection System (IDS) based on one kind of asymmetric optimization which use any three already well-known IDS algorithms and Triple Modular Redundancy (TMR) algorithm together. Namely, a variable threshold which indicates an attack on an observed and protected network is determined by using all three values obtained with three known IDS algorithms i.e., on previously recorded data by making a decision by majority. For these algorithms authors used algorithm of k-nearest neighbors, cumulative sum algorithm, and algorithm of exponentially weighted moving average. Using a proposed method we can get a threshold that is more precisely determined than in the case of any method individual. Practically, using TMR we obtain a dynamically threshold adjustment of IDS software, which reduces the existence of false alarms and undetected attacks, so the efficiency of such IDS software is notably higher and can get better results. Today, Denial of Service attacks (DoS) are one of the most present type of attacks and the reason for the special attention paid to them in this paper. In addition, the authors of the proposed method for IDS software used a known CIC-DDoS2019 dataset, which contains various data recordings of such attacks. Obtained results with the proposed solution showed better characteristics than each individual used algorithm in this solution. IDS software with the proposed method worked precisely and timely, which means alarms were triggered properly and efficiently.

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“…In [21], solution the deep learning is used with a convolutional neural network to develop a deep learning model. It investigates some of the advanced DDoS attacks.…”

Section: Anomaly Detection and Classification Algorithmsmentioning

confidence: 99%

Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems

Babic¹,

Miljković²,

Čabarkapa

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…In [36], the wavelet neural network (WNN) model applied to the IDS gave results with a moderate accuracy and high computational complexity because it is necessary to reduce the size of the wavelet decomposed data. As for convolutional neural network (CNN) algorithm [37], this model gives a moderate accuracy and a high cost of computational in front of a complex architecture and a diversity of the data in real time [38].…”

Section: A Related Workmentioning

confidence: 99%

“…On the other hand, the computational complexity is a another element of paramount importance that highlights the reliability of any proposed algorithm. Towards this end, Fig.9 depicts the running time of EGA-based algorithm compared with three main concurrent ones, namely GA, Fuzzy [49], SVM [50], Wavelet Neural Network (WNN) [36] and Convolutional Neural Network (CNN) with one layer [37]. Mainly, again EGA outperforms its counterpart ones in terms of time complexity, which makes from it a promising candidate for the unknown attack detection.…”

Section: ) Comparison Between Ega and Other Algorithmsmentioning

confidence: 99%

Epigenetic Algorithm-Based Detection Technique for Network Attacks

2020

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Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this paper, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98% and a faster processing time than that of GA and other algorithms that we have compared in this paper.

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“…With the rapid development of computing capability of electronic devices, deep network, especially deep Convolutional Neural Networks (CNN) [17,18] now being one of the most prevalent choices for image classification [19,20] and remarkable achievements have been made. However, the inner recognition mechanism of CNN is still in lack of systematic interpretation.…”

Section: Image-specific Class Saliency Visualisationmentioning

confidence: 99%

A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

2020

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Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD), and etc., usually show limited recognition effects when only a slight difference exists in radar signals. Numerous two-dimensional features on transform domain, like various time-frequency representation and ambiguity function are used to augment information abundance, whereas the unacceptable computational burden usually emerges. To solve this problem, some artfully handcrafted features in transformed domain are proposed, like representative slice of ambiguity function (AF-RS) and compressed sensing mask (CS-MASK), to extract representative information that contributes to machine recognition task. However, most handcrafted features only utilizing neural network as a classifier, few of them focus on mining deep informative features from the perspective of machine cognition. Such feature extraction that is based on human cognition instead of machine cognition may probably miss some seemingly nominal texture information which actually contributes greatly to recognition, or collect too much redundant information. In this paper, a novel data-driven feature extraction is proposed based on machine cognition (MC-Feature) resort to saliency detection. Saliency detection exhibits positive contributions and suppresses irrelevant contributions in a transform domain with the help of a saliency map calculated from the accumulated gradients of each neuron to input data. Finally, positive and irrelevant contributions in the saliency map are merged into a new feature. Numerous experimental results demonstrate that the MC-feature can greatly strengthen the slight intra-class difference in SEI and provides a possibility of interpretation of CNN.

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CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

Cited by 266 publications

References 53 publications

Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems

Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems

Epigenetic Algorithm-Based Detection Technique for Network Attacks

A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

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