Deep learning in intrusion detection perspective: Overview and further challenges

Kim, Kwangjo; Aminanto, Muhamad Erza

doi:10.1109/iwbis.2017.8275095

Cited by 52 publications

(33 citation statements)

References 19 publications

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“…Discriminative deep architectures or supervised learning are supposed to differentiate several parts of data for classification. CNN is the best example of supervised learning; it allows exceptional architectural proficiency for image recognition [103]. Face recognition is commonly studied in computer vision, and CNN has achieved great success, becoming a powerhouse in this topic.…”

Section: Introduction To Deep Learningmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

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Section: Introduction To Deep Learningmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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“…However, as data sets are evolving in terms of size and type, traditional machine learning algorithms become increasingly unable to cope with real-world network application environments. 1 Despite several decades of research and applications in IDS, there are still many challenges to be addressed. In particular, better detection accuracy, reduced falsepositive rates, and the ability to detect unknown attacks are all required.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Al-Turaiki

Altwaijry

2021

Big Data

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Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.

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“…Ainda em 2017, no trabalho de Kim [7] são mostradas algumas limitações em IDSs anteriores que utilizam apredizagem de máquina clássicas e introduzem o aprendizado de características, incluindo a construção, extração e seleção de características para superar os desafios. Também discutem algumas técnicas de deep learning e suas aplicações para utilização em IDS.…”

Section: Trabalhos Relacionadosunclassified

Sistema Distribuído para Detecção de Ameaças em Redes Utilizando Deep Learning

Schuartz¹,

Fonseca²,

Fonseca³

2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-A detecção de ameaças na Interneté um fator essencial para manter a segurança de dados e informações. Um sistema de detecção de ameaças tenta prevenir que esses ataques ocorram através da análise de padrões e do comportamento do fluxo de dados na rede. Este artigo apresenta uma extensão para a plataforma distribuída de detecção e análise de dados em grande fluxo, apresentada no trabalho de Schuartz et al., através do uso de deep learning para redução do espaço de características. A avaliação do sistema se baseia através da acurácia, do número de falsos positivos e de falsos negativos, onde cada classificador apresentou melhor acurácia ao utilizar 5 e 13 atributos. Ainda, o sistema apresentou menor número de falsos positivos e negativos, permitindo a detecção de ameaças em tempo real sobre um grande volume de dados, com maior precisão. Palavras-Chave-Aprendizado de máquinas, aprendizagem profunda, grande volume de dados, mineração de dados, sistema de detecção de ameaças, tempo real.

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Deep learning in intrusion detection perspective: Overview and further challenges

Cited by 52 publications

References 19 publications

Past, Present, and Future of Face Recognition: A Review

Past, Present, and Future of Face Recognition: A Review

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Sistema Distribuído para Detecção de Ameaças em Redes Utilizando Deep Learning

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