2,046 research outputs found
Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU)
The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security solutions are not suitable, as IoT devices are of low storage capacity and less processing power. Moreover, the IoT devices are connected for longer time periods without human intervention. This raises a need to develop smart security solutions which are light-weight, distributed and have a high longevity of service. Rather than per-device security for numerous IoT devices, it is more feasible to implement security solutions for network data. The artificial intelligence theories like Machine Learning and Deep Learning have already proven their significance when dealing with heterogeneous data of various sizes. To substantiate this, in this research, we have applied concepts of Deep Learning and Transmission Control Protocol/Internet Protocol (TCP/IP) to build a light-weight distributed security solution with high durability for IoT network security. First, we have examined the ways of improving IoT architecture and proposed a light-weight and multi-layered design for an IoT network. Second, we have analyzed the existingapplications of Machine Learning and Deep Learning to the IoT and Cyber-Security. Third, we have evaluated deep learning\u27s Gated Recurrent Neural Networks (LSTM and GRU) on the DARPA/KDD Cup \u2799 intrusion detection data set for each layer in the designed architecture. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this unique research outperformed the performance results of existing methods over the KDD Cup \u2799 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security
Network Data Security for the Detection System in the Internet of Things with Deep Learning Approach
We thought to set up a system of interconnection which allows sharing the communication network of data without the intervention of a human being. The Internet of Things system allows many devices to be connected for a long time without human intervention, data storage is low and the level of data processing is reduced, which was not the case with older solutions proposed to secure the data for example: cyber-attack and other systems. But other theories like for example: artificial intelligence, machine learning and deep learning have a lot to show their ability and the real values of heterogeneous data processing of different sizes and many researchers had to work on it.In the case of our work, we have used deep learning theories, to achieve a light data interconnection security solution; we also have TCP/IP protocol for data transmission control, algorithm drillers for classifications. In order to arrive at a good solution; First, we thought of a model for anomalies detection in Internet of Things and we think about the improvement of architectures of the Internet of the existing objects already proposed a system with a light solution and especially multilayer for an IoT network. Second, we analyzed existing applications of machine learning, deep learning to IoT, and cybersecurity. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this research outperformed the performance results of existing methods over the KDD Cup '99 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security
Anomaly Detection Using Deep Neural Network for IoT Architecture
The revolutionary idea of the internet of things (IoT) architecture has gained enormous
popularity over the last decade, resulting in an exponential growth in the IoT networks, connected
devices, and the data processed therein. Since IoT devices generate and exchange sensitive data
over the traditional internet, security has become a prime concern due to the generation of zero-day
cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed
efficient security solution to the IoT network by protecting the network entry points through constant
network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies,
including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection
mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT
network. A comparative analysis of different deep-learning models such as DNN, Convolutional
Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit
and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental
results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time
reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared
to the well-known deep learning models. It was also observed that using only the 16–35 best
numerical features selected using MI instead of 80 features of the dataset result in almost negligible
degradation in the model’s performance but helped in decreasing the overall model’s complexity. In
addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in
terms of the detection accuracy considering only the top five categorical and numerical features
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
A deep learning approach for intrusion detection in Internet of Things using bi-directional long short-term memory recurrent neural network
Internet-of-Things connects every ‘thing’ with the Internet and allows these ‘things’ to communicate with each other. IoT comprises of innumerous interconnected devices of diverse complexities and trends. This fundamental nature of IoT structure intensifies the amount of attack targets which might affect the sustainable growth of IoT. Thus, security issues become a crucial factor to be addressed. A novel deep learning approach have been proposed in this thesis, for performing real-time detections of security threats in IoT systems using the Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). The proposed approach have been implemented through Google TensorFlow implementation framework and Python programming language. To train and test the proposed approach, UNSW-NB15 dataset has been employed, which is the most up-to-date benchmark dataset with sequential samples and contemporary attack patterns. This thesis work employs binary classification of attack and normal patterns. The experimental result demonstrates the proficiency of the introduced model with respect to recall, precision, FAR and f-1 score. The model attains over 97% detection accuracy. The test result demonstrates that BLSTM RNN is profoundly effective for building highly efficient model for intrusion detection and offers a novel research methodology
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset
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