scholarly journals Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

2018 ◽  
Author(s):  
Tuan A Tang ◽  
Lotfi Mhamdi ◽  
Des McLernon ◽  
Syed Ali Raza Zaidi ◽  
Mounir Ghogho
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Deep Recurrent Neural Network

Deep recurrent neural network for IoT intrusion detection system

2020 ◽  
Vol 101 ◽  
pp. 102031 ◽  
Author(s):  
Muder Almiani ◽  
Alia AbuGhazleh ◽  
Amer Al-Rahayfeh ◽  
Saleh Atiewi ◽  
Abdul Razaque
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Deep Recurrent Neural Network

OPTIMIZATION-DRIVEN DEEP RECURRENT NEURAL NETWORK FOR INTRUSION DETECTION AND HEALTH RISK ASSESSMENT IN WIRELESS BODY SENSOR NETWORK

2020 ◽  
pp. 2050044
Author(s):  
Abdalla Alameen ◽  
Ashu Gupta
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Intrusion Detection ◽  
Health Risk ◽  
Sensor Network ◽  
Health Risk Assessment ◽  
Recurrent Neural Network ◽  
Body Sensor Network ◽  
Physical Defense ◽  
Deep Recurrent Neural Network

Wireless body sensor network (WBSN) has gained great attention in the environmental and military applications, but security is the major issue, nowadays. In addition, the data exchanged through the wireless sensor network (WSN) is vulnerable to several malicious attacks because of the physical defense equipment needs. Hence, various intrusion detection methods are required for defending against such attacks. Accordingly, an effective method, named deep recurrent neural network (Deep RNN), is proposed in this research for detecting the intrusion in WBSN. At first, the WBSN nodes are utilized to sense the data from the health records of patient for acquiring certain parameters to make risk assessment. Then, WBSN nodes transmit the data to the target nodes using the obtained parameters. After the determination of parameters, the WBSN nodes are responsible to collect the information of the patient and transfer the obtained information to cluster heads (CHs) based on the hybrid harmony search algorithm–particle swarm optimization (HSA–PSO). HSA–PSO is utilized for identifying the optimal CH node iteratively. From the selected CHs, secure communication is done to exchange the data packets. After that, the KDD features are extracted and intrusion detection is done using the proposed Deep RNN. After the genuine users are detected, the classification is done using fractional cat-based salp swarm algorithm (FCSSA) for the risk assessment. The performance of the intrusion detection and health risk assessment in WBSN based on the proposed model is evaluated based on accuracy, sensitivity, and the specificity. The developed model achieves the maximal accuracy of 95.79%, maximal sensitivity of 95.97%, and the maximal specificity of 95.61% using Cleveland dataset.

Jaya Ant Lion Optimization-Driven Deep Recurrent Neural Network for Cancer Classification Using Gene Expression Data (Preprint)

2020 ◽  
Author(s):  
Ramachandro Majji
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Data Transformation ◽  
Cancer Classification ◽  
Data Normalization ◽  
Ant Lion Optimization ◽  
Deep Recurrent Neural Network ◽  
Novel Strategy ◽  
Feature Dimension

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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