Water Moth Search Algorithm-based Deep Training for Intrusion Detection in IoT

2021 ◽  
Author(s):  
Rekha P. M. ◽  
Nagamani H. Shahapure ◽  
Punitha M. ◽  
Sudha P. R.
Keyword(s):  
Intrusion Detection ◽  
Water Wave ◽  
Search Algorithm ◽  
Detection Techniques ◽  
Redundant Data ◽  
Search Optimization ◽  
Specificity And Sensitivity ◽  
Wrapper Approach ◽  
Deep Recurrent Neural Network ◽  
Novel Method

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.

scholarly journals An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications

2021 ◽  
Vol 11 (4) ◽  
pp. 7326-7331
Author(s):  
N. K. Al-Shammari ◽  
T. H. Syed ◽  
M. B. Syed
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Medical Devices ◽  
Search Algorithm ◽  
Data Extraction ◽  
Access Time ◽  
Healthcare Applications ◽  
Detection Techniques ◽  
Smart Healthcare ◽  
Layer Extraction

The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.

Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 166-184
Author(s):  
Ratnaprabha Ravindra Pune Borhade ◽  
Manoj S. Nagmode
Keyword(s):  
Epileptic Seizure ◽  
Information Gain ◽  
Classical Method ◽  
The Novel ◽  
Eeg Signal ◽  
Search Optimization ◽  
Spectral Flux ◽  
Deep Recurrent Neural Network ◽  
Processing Module ◽  
Novel Method

Electroencephalogram (EEG) signal is broadly utilized for monitoring epilepsy and plays a key role to revitalize close loop brain. The classical method introduced to find the seizures relies on EEG signals which is complex as well as costly, if channel count increases. This paper introduces the novel method named exponential-squirrel atom search optimization (Exp-SASO) algorithm in order to train the deep RNN for discovering epileptic seizure. Here, the input EEG signal is given to the pre-processing module for enhancing the quality of image by reducing the noise. Then, the pre-processed image is forwarded to the feature extraction module. The features, like statistical features, spectral features, logarithmic band power, wavelet coefficients, common spatial patterns, along with spectral decrease, pitch chroma, tonal power ratio, and spectral flux, are extracted. Once the features are extracted, the feature selection is carried out using fuzzy information gain model for choosing appropriate features for further processing.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Safa Otoum ◽  
Burak Kantarci ◽  
Hussein Mouftah
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Performance Metrics ◽  
Action Learning ◽  
Smart Devices ◽  
Critical Infrastructures ◽  
State Action ◽  
Detection Techniques ◽  
Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

Intrusion detection techniques in network environment: a systematic review

2021 ◽  
Author(s):  
Maruthi Rohit Ayyagari ◽  
Nishtha Kesswani ◽  
Munish Kumar ◽  
Krishan Kumar
Keyword(s):  
Systematic Review ◽  
Intrusion Detection ◽  
Network Environment ◽  
Detection Techniques
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