Intrusion detection in smart cities using Restricted Boltzmann Machines

Elsaeidy, Asmaa; Munasinghe, Kumudu S.; Sharma, Dharmendra; Jamalipour, Abbas

doi:10.1016/j.jnca.2019.02.026

Cited by 66 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with previous works, which combined different models, deep belief networks were combined with decision tree models for attack detection [20] and deep restricted Boltzmann machines were combined with multi-layer perceptron of different types, in addition to support vector machines and random forest models, for the detection of distributed Denial of service attack [21]. In [20], a deep belief network was used for dimensionality reduction, selecting required features and detecting if there was an attack, while a decision tree model was subsequently applied to classify the attacks and signal alerts.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], a deep belief network was used for dimensionality reduction, selecting required features and detecting if there was an attack, while a decision tree model was subsequently applied to classify the attacks and signal alerts. In [21], deep restricted Boltzmann machines were used to learn higher features that are used by the different classification models to discriminate between normal and different types of attacks.…”

Section: Related Workmentioning

confidence: 99%

“…Modelling time domain and performing a classification is a complex and challenging task for typical machine learning approaches. Even with small number of features, deep models outperform typical machine learning models such as multi-layer perceptron [21], [22]. Due to the small number of features, the proposed network structure starts with a smaller hidden layer compared to the remaining hidden layers, as shown in Fig.…”

Section: The Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

Replay Attack Detection in Smart Cities Using Deep Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research. INDEX TERMS Smart cities, intrusion detection, replay attack, deep learning, convolutional neural networks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Replay Attack Detection in Smart Cities Using Deep Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…RBMs are used as a building block to create Deep Belief Networks. They can be used in a variety of urban intelligence applications, such as urban object recognition [31], intrusion detection [32], object classification [33] and city event detection from Twitter [34].…”

Section: ) Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

Urban Intelligence With Deep Edges

White

Clarke

2020

IEEE Access

View full text Add to dashboard Cite

With the increased accuracy available from state of the art deep learning models and new embedded devices at the edge of the network capable of running and updating these models there is potential for urban intelligence at the edge of the network. The physical proximity of these edge devices will allow for intelligent reasoning one hop away from data generation. This will allow a range of modern urban reasoning applications that require reduced latency and jitter such as remote surgery, vehicle collision detection and augmented reality. The traffic flow from IoT devices to the cloud will also be reduced as with the increased accuracy from deep learning models only a subset of the data will need to be reported after a first pass analysis. However, the training time of deep learning models can be long, taking weeks on multiple desktop GPUs for large datasets. In this paper we show how transfer learning can be used to update the last layers of pre-trained models at the edge of the network, dramatically reducing the training time and allowing the model to perform new tasks without data ever having to be sent to the cloud. This will also improve the users' privacy, which is a key requirement for urban intelligence applications with the introduction of GDPR. We compare our approach to alternative IoT urban intelligence architectures such as cloud-based architectures and deep learning algorithms trained only on local data. INDEX TERMS Edge computing, transfer learning, deep learning, urban intelligence, IoT, QoS. I. INTRODUCTION Urban intelligence can provide insight into a number of the major problems being faced by our cities, such as air pollution, increased mobile communication network demand, traffic congestion and water floods, etc., through the use of data from IoT sensors and intelligent analytics on the data. This can allow for the efficient running of a smart city, making the most of the resources available. Problems in smart cities such as increased network demand and crowd congestion are set to get increasingly difficult with the UN predicting 60% of people globally will live in cities with at least half a million inhabitants by 2030 [1]. This will exacerbate the current problems in our cities, so it will be critical to mange these problems with intelligent urban reasoning algorithms and a suitable deployment architecture. IoT devices will enable access to a range of devices and information that was not available to urban authorities before. This additional information from air pollution sensors, CCTV cameras, vehicles and so on will allow the development of more advanced urban reasoning applications [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Hao Ji.

show abstract

“…Moreover, Yang et al [4], they use restricted Boltzmann machine (RBM) to extract high-level features of traffic data and train SVM with stochastic gradient descent (SGD) for classification of these features. Asmaa et al [5] presented a comprehensive discussion of using RBM for feature learning and a classifier for anomaly detection. Salama et al [6] presented an intrusion detection hybrid scheme using deep belief network (DBN) and SVM, classifying the intrusion into two clusters: normal or attack.…”

Section: Related Workmentioning

confidence: 99%

Visual Analytics for Network Events Classification in LAN With Deep Convolutional Neural Network

Sun

Ochiai

Esaki

2020

Preprint

View full text Add to dashboard Cite

This article illustrates a method of visualizing network traffic in LAN based on the Hilbert Curve structure and the array exchange and projection, with nine types of protocols’ communication frequency information as the discriminators, the results of which we call them feature maps of network events. Several known scan cases are simulated in LANs and network traffic is collected for generating feature maps under each case. In order to solve this multi-label classification task, we adopt and train a deep convolutional neural network (DCNN), in two different network environments with feature maps as the input data, and different scan cases as the labels. We separate datasets with a ratio of 4:1 into the training dataset and the validation dataset. Then, based on the micro scores and the macro scores of the validation, we evaluate performance of the scheme, achieving macro-F-measure scores of 0.982 and 0.975, and micro-F-measure scores of 0.976 and 0.965 separately in these two LANs.

show abstract

Intrusion detection in smart cities using Restricted Boltzmann Machines

Cited by 66 publications

References 19 publications

Replay Attack Detection in Smart Cities Using Deep Learning

Replay Attack Detection in Smart Cities Using Deep Learning

Urban Intelligence With Deep Edges

Visual Analytics for Network Events Classification in LAN With Deep Convolutional Neural Network

Contact Info

Product

Resources

About