A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications

Garg, Sahil; Kaur, Kuljeet; Batra, Shalini; Kaddoum, Georges; Kumar, Neeraj; Boukerche, Azzedine

doi:10.1016/j.future.2019.09.038

Cited by 87 publications

(41 citation statements)

References 35 publications

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“…Besides, in terms of Acc obtained in this study, the results are very close to the results of the study proposed by Kim and Cho [4 ] even though the C‐NSA in this study is not necessary heavy computation load and time just as a deep learning model the C‐LSTM in [4 ] requires heavy computation process. Moreover, the results in this study are very close to the results of the study proposed by Garg et al [49 ] in terms of FPR.…”

Section: Developed Application and Experimental Resultssupporting

confidence: 90%

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

Dandıl

2020

IET Inf. secur

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Section: Developed Application and Experimental Resultssupporting

confidence: 90%

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

Dandıl

2020

IET Inf. secur

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“…The authors in [39], proposed a novel multi-stage anomaly detection technique based on Boruta Firefly Aided Partitioning Density-Based Spatial Clustering of Applications with Noise (BFA-PDBSCAN). The authors claimed that their proposed technique produced better results in comparison to the related techniques of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN).…”

Section: Current Reviewsmentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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“…It also provides the suggestion to the travelers to enhance their travel decision. 1 Prediction of traffic flow is considered as a time-series-based problem in which the future value of traffic flow has estimated depending on the past data from one or more locations. 2 The volume of data from a variety of sources introduces the idea of big data into the transportation domain.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, a deep learning architecture like deep neural network (DNN), convolution neural network (CNN), recurrent neural network (RNN), and deep belief network (DBN) used in many complex applications involving large data set for the image, video analysis, 5,6 natural language processing, 7 and also in various data mining process. [8][9][10][11] Figure 1 represents the process of the proposed traffic flow prediction work.…”

Section: Introductionmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications

Cited by 87 publications

References 35 publications

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

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