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Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

Self-attention based recurrent convolutional neural network for disease prediction using healthcare data

The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.

Wireless Sensor Networks (WSNs) have become a hot research topic in recent years. They have many potential applications for both civil and military tasks. However, the unattended nature of WSNs and the limited computational and energy resources of their nodes make them susceptible to many types of attacks. Intrusion detection is one of the major and efficient defence methods against attacks in a network infrastructure. Intrusion Detection Systems can be seen as the second line of defence and they complement the security primitives that are adopted in order to prevent attacks against the computer network being protected. The peculiar features of a wireless sensor network pose stringent requirements to the design of intrusion detection systems. In this paper, we propose a hybrid, lightweight, distributed Intrusion Detection System (IDS) for wireless sensor networks. This IDS uses both misuse-based and anomaly-based detection techniques. It is composed of a Central Agent, which performs highly accurate intrusion detection by using data mining techniques, and a number of Local Agents running lighter anomaly-based detection techniques on the motes. Decision trees have been adopted as classification algorithm in the detection process of the Central Agent and their behaviour has been analysed in selected attacks scenarios. The accuracy of the proposed IDS has been measured and validated through an extensive experimental campaign. This paper presents the results of these experimental tests.

Some security protocols or mechanisms have been designed for wireless sensor networks (WSNs). However, an intrusion detection system (IDS) should always be deployed on security critical applications to defense in depth. Due to the resource constraints, the intrusion detection system for traditional network cannot be used directly in WSNs. Several schemes have been proposed to detect intrusions in wireless sensor networks. But most of them aim on some specific attacks (e.g. selective forwarding) or attacks on particular layers, such as media access layer or routing layer. In this paper, we present a framework of machine learning based intrusion detection system for wireless sensor networks. Our system will not be limited on particular attacks, while machine learning algorithm helps to build detection model from training data automatically, which will save human labor from writing signature of attacks or specifying the normal behavior of a sensor node.

A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

Multimodal emotion recognition has become a hot topic in human-computer interaction and intelligent healthcare fields. However, combining information from different human different modalities for emotion computation is still challenging. In this paper, we propose a three-dimensional convolutional recurrent neural network model (referred to as 3FACRNN network) based on multimodal fusion and attention mechanism. The 3FACRNN network model consists of a visual network and an EEG network. The visual network is composed of a cascaded convolutional neural network-time convolutional network (CNN-TCN). In the EEG network, the 3D feature building module was added to integrate band information, spatial information and temporal information of the EEG signal, and the band attention and self-attention modules were added to the convolutional recurrent neural network (CRNN). The former explores the effect of different frequency bands on network recognition performance, while the latter is to obtain the intrinsic similarity of different EEG samples. To investigate the effect of different frequency bands on the experiment, we obtained the average attention mask for all subjects in different frequency bands. The distribution of the attention masks across the different frequency bands suggests that signals more relevant to human emotions may be active in the high frequency bands γ (31-50 Hz). Finally, we try to use the multi-task loss function Lc to force the approximation of the intermediate feature vectors of the visual and EEG modalities, with the aim of using the knowledge of the visual modalities to improve the performance of the EEG network model. The mean recognition accuracy and standard deviation of the proposed method on the two multimodal sentiment datasets DEAP and MAHNOB-HCI (arousal, valence) were 96.75 ± 1.75, 96.86 ± 1.33; 97.55 ± 1.51, 98.37 ± 1.07, better than those of the state-of-the-art multimodal recognition approaches. The experimental results show that starting from the multimodal information, the facial video frames and electroencephalogram (EEG) signals of the subjects are used as inputs to the emotion recognition network, which can enhance the stability of the emotion network and improve the recognition accuracy of the emotion network. In addition, in future work, we will try to utilize sparse matrix methods and deep convolutional networks to improve the performance of multimodal emotion networks.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor networks cannot be secured against internal attacker with only cryptographic techniques because their nodes are not physically protected and can be easily captured by the attacker. Therefore, in this work we consider intrusion detection systems that monitor behavior of sensor nodes and detect the malicious ones among them. Our work is exploratory in that we propose metrics for evaluation of these systems. There are evaluation metrics for intrusion detection systems for conventional wired and wireless networks. However, to our best knowledge there is no work that proposes evaluation metrics for intrusion detection systems for wireless sensor networks. We divide the proposed metrics into two groups. In the first group there are metrics for detection techniques without a response mechanism. In the second group there are metrics for detection techniques together with a response mechanism. The proposed metrics will help an administrator of a network to choose the best intrusion detection system from a set of systems or to optimize a configuration of a certain intrusion detection system for a given network with a particular topology, sensor nodes capabilities and anticipated types of attack.

In this paper, by combining the known deep learning related literature in the field of intrusion detection, and the existing in the design of intrusion detection system based on the deep learning common low detection efficiency and high rate of false positives, as well as the training data set used by the problems of unbalanced data, etc., through the depth study of feature extraction and data processing ability. To study the intrusion detection method of the Internet of Things based on deep learning. The work and achievements are as follows:First of all, will be used for feature selection embedded model (EM) and used in intrusion detection of the convolution neural network (CNN), combined with lightweight intrusion detection model (XCNN), because it is an embedded model (EM), so can reduce the impact on the equipment, at the same time compared with other algorithms, the computing time is shorter, the same or even better.Second, proposed to the Attention mechanism (Self - Attention) is applied to intrusion detection system, due to the nature of the Attention mechanism, the mechanism and long Attention can greatly reduce the training time, through introducing the residual network, application feature coding technique again the sparse features thickened into low density at high altitudes, and join to its relative position encoding, So it can sense global information and solve the long-term dependence problem of recurrent neural network.Finally, the results of the intrusion detection method were analyzed on the Pthon simulation platform, and the relevant indicators were verified by using THE NSL-KDD dataset, THE CIC-IDS2017 dataset and the CSE-CIC-IDS2018 dataset. The effectiveness of the method was proved by building a laboratory Internet of Things environment for actual testing. The experimental results show that the algorithm can greatly reduce training time, improve training efficiency, and have the same or even better performance results.

MP-MID: Multi-Protocol Oriented Middleware-level Intrusion Detection method for wireless sensor networks

Wireless sensor networks (WSNs) are a new and most recent technology which is used in many critical applications. WSN is a network which is deployable anywhere in the environment. Due to this nature of WSN, an unattended network is more probable for vulnerable and unavoidable attacks. WSN is comprised of huge different types of nodes which are distributed within the network. Some of the nodes have sensing technique for detecting unusual behaviour in the network. Detection and prevention mechanisms are used for securing the network, with the consideration of energy efficiency. Biometric-based system is most efficient way of detecting an intruder which is one of the major techniques of pattern matching. Since WSN is more probable vulnerable to various harmful security attacks as it has the nature of broadcasting the transmission data and has very low capacity of computation, an Intrusion detection system (IDS) is used to detect such mobile attackers or intruders. Intrusion prevention system (IPS) uses a mechanism which enables the reduction of consumption of energy in sensor nodes of WSN.