System workflow

System workflow

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Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher's attention with the application of deep learning techniques in IDS. In this paper, we...

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... Deep learning is a subset of machine learning algorithms which has been applied for intrusion detection using WSNs Lee et al., 2021;Singh, Amutha, Nagar, Sharma, & Lee, 2022a, 2022bSood, Prakash, Sharma, Singh, & Choubey, 2022). It is also employed for pattern matching and network security where it identifies the malicious activities occurring in the network and is termed as Network Intrusion Detection System (NIDS). ...
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Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.
... The network's IDS has rapidly developed in business and academia regarding the entire cyber-attacks on commercial and government fields worldwide [1]. Moreover, the annual cost of Cybercrime is steadily increasing [2]. ...
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The network's digital applications and functions are vulnerable to get attacks from malicious events. Hence, an Intrusion Detection System (IDS) is the required process for the network application to protect the information from unauthenticated malicious events. Many IDS have been implemented on the basis of neural modules for predicting the unauthenticated access that is present in the network medium. But, there are several difficulties on specifying the malicious features from network users. To address this problem, the present research article has planned to develop a novel Krill herd-based Deep Belief Intrusion Forecasting with suitable parameters to detect the present malicious features based on user behaviors. Initially, the data was pre-processed and entered into the classification layer. Consequently, feature extraction and attack specification has been performed. Moreover, the planned model is executed in the python environment, and the scalability score has been measured using dual datasets that are NSL-KDD and CICIDS. Here, incorporating the krill function has helped earn the desired outcomes. Also, attacks like DoS, probe, R2L, and probe have been included in the trained NSL-KDD and CICIDS. Finally, the designed model has earned a better outcome than the compared model by achieving high accuracy and a lower error rate.
... Deep learning is a subset of machine learning algorithms which has been applied for intrusion detection using WSNs Amutha et al., 2021b;Singh et al., 2022a;Sood et al., 2022;Singh et al., 2022b). It is also employed for pattern matching and network security where it identifies the malicious activities occurring in the network and is termed as Network Intrusion Detection System (NIDS). ...
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Full-text available
Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.