Intrusion detection with deep learning on internet of things heterogeneous network

Sharipuddin, Sharipuddin; Purnama, Benni; Kurniabudi, Kurniabudi; Winanto, Eko Arip; Stiawan, Deris; Hanapi, Darmawijoyo; Idris, Mohd. Yazid; Budiarto, Rahmat

doi:10.11591/ijai.v10.i3.pp735-742

Cited by 11 publications

(13 citation statements)

References 20 publications

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“…Supervised and unsupervised learning algorithms are used to produce increasingly higher levels of abstraction defined by the output properties of previous levels. Various methods are used in DL, such as deep belief networks (DBNs) [19], autoencoders [20], recurrent neural networks (RNNs) [21], long short-term memory networks (LSTMs) [22], and convolutional neural networks (CNNs) [13] [23][24]. This study proposes the use of a CNN for detection and segmentation methods in AR.…”

Section: Proposed Methods For Ar Using a Convolutional Neural Networkmentioning

confidence: 99%

Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks

Nurhadi

Stiawan

Idris

et al. 2022

IJEEI

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Augmented reality (AR) has been applied in maintenance, simulation, remote assistance, and other fields. One of the issues arising from these applications is how objects can be placed in physical environments using an AR system. Object placement consists of two processes: object detection and segmentation. Due to the importance of placement, in this paper, we propose using deep learning to address issues with the placement of objects through detection and segmentation in AR. Deep learning can help complete tasks by providing correct information about environmental changes in real-time situations. The problem is that it is rarely used in AR, which suggests a combination of deep learning-based object detection and instance segmentation with wearable AR technology to improve performance on complex tasks. In our work, we propose to address this problem by applying a convolutional neural network to facilitate the detection and segmentation of objects in real environments. To measure AR performance, we examined detection accuracy in environments with different intensities. The results of the experiment demonstrate satisfactory performance, reaching 98% for segmentation and accurate detection.

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Section: Proposed Methods For Ar Using a Convolutional Neural Networkmentioning

confidence: 99%

Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks

Nurhadi

Stiawan

Idris

et al. 2022

IJEEI

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“…Deep learning is quite effective at dealing with such massive amounts of data and can generate highly accurate results. As a result, Internet of Things (IoT), big data analytics, and deep learning are all important to the growth of a high-tech society [22], [28], [74], [77], [89], [92], [93], [101], [103], [110], [114], [126], [149], [152], [155], [183], [188], [202], [232], [265], [318], [352].…”

Section: Big Data Analytics In Iotmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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“…They proposed a new migration learning method which linearly transforms the feature mapping of the target region, increases the weights of feature matching, enables knowledge transfer between heterogeneous networks, and adds discriminators based on adversarial principles to speed up feature mapping and learning [10]. Sharipuddin et al addressed the intrusion detection system in heterogeneous networks which is easily affected by objective factors such as devices and network protocols, proposed an identification method combining deep learning, and conducted preliminary experiments on denial-of-service attacks, and the experimental results showed that deep learning can improve the detection accuracy in heterogeneous networks [11]. Fu et al proposed a new model called Metapath Aggregation Graph Neural Network (MAGN) to improve the final performance.…”

Section: Related Workmentioning

confidence: 99%

Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning

Zhan

2022

Journal of Applied Mathematics

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Data classification is one of the main tasks in the current data mining field, and the existing network data triage algorithms have problems such as too small a proportion of labeled samples, a large amount of noise, and redundant data, which lead to low classification accuracy of data stream implementation. Network embedding can effectively improve these problems, but the network embedding itself has problems such as capturing relational honor and ambiguity. This study proposes a SNN-RODE based LapRLS heterogeneous network data classification algorithm to achieve deep embedding of structure and semantics among nodes by constructing a multitask SNN and selecting dead song datasets to perform mining tasks to train the neural network. Then a semisupervised learning classifier based on Laplace regular least squares regression model is designed to use the relative support difference function as the decision method and optimize the function. The simulation experimental results show that the SNN-RODE-LapRLS algorithm improves the performance by 14%-51% over the mainstream classification algorithms, and the consumption time meets the demand of real-time classification.

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Intrusion detection with deep learning on internet of things heterogeneous network

Cited by 11 publications

References 20 publications

Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks

Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks

Insights on the internet of things: past, present, and future directions

Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning

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