Developing a multi-level intrusion detection system using hybrid-DBN

With the rapid development of artificial intelligence technology of deep learning method, it has been applied to many fields, especially to life science. In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals. Based on a 24-dimension Mel Frequency Cepstrum Coefficient (MFCC), the authors extracted 256-dimension deep voiceprint features via DBN model that is developed by stacking three layers of Restricted Boltzmann Machine (RBM), and conducted voiceprint recognition by approach of SVM. According to the recognition results, the new approach can significantly improve both of accuracy and efficiency when it was compared with traditional voiceprint features and recognition models. The low dimensional features of voiceprint was extracted into higher dimensions by DBN model, while SVM can avoid the elevation of computation complexity caused by the increases of feature dimension. The combined strengths have been fully expressed by the experimental results.

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

Technology is rapidly evolving in a world powered by social networks, online transactions, cloud computing, and automated processes. However, as technology develops, equal cybercrime. Cyber attacks are increasing rapidly, making cybersecurity a challenge in the digital era. Intrusion detection systems (IDS) are an advancement that enhances network security and protects an organization's data. IDS helps network administrators detect malicious activity within the network and alerts administrators to protect data by taking appropriate measures against these attacks. Deep Belief Networks (DBN) are generative graphics models formed by stacking multiple Restricted Boltzmann Machines (RBMs). High-dimensional representations can be identified and learned.Improving and evaluating Deep Belief Networks (DBN) for detecting cyber-attacks in a network of connected devices using the CICIDS2017 dataset. Several class balancing techniques were aplied and evaluated. The recomendation to improve IDS based on DBN is collect more data, increase the number of layers, tune the hyperparameters, regularize the network, and use more efficient training algorithms.

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer systems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion Detection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective.

Surface roughness is a crucial factor affecting the surface quality of workpieces in manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network (DBN) models, were performed to accurately predict change in surface roughness in the rotational electromagnetic finishing (REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7, 14, 14, 1] showed effective features of the nonlinear relationship between process parameters and response with the highest determination coefficient (R2) of 0.9340 and the lowest mean squared error (MSE) of 1.3037 \(\times\) 10−3 in the testing datasets. In addition, a genetic algorithm (GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. It showed that the maximum change in surface roughness was 0.530 at particle length of 3 mm, particle diameter of 0.7 mm, particle weight of 1.3 kg, liquid water quantity of 1.0 l, a rotational speed of 1323 rpm, working time of 35 min, and initial surface roughness of 2.5478 m\(\upmu\).

As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.

PurposeThe purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.Design/methodology/approachThe proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.FindingsThe experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.Originality/valueTo improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

Deep belief network based electricity load forecasting: An analysis of Macedonian case

Industrial Control Systems (ICS) monitor and control physical processes. The security of ICS has drawn the attention of many researchers since successful cyber-attacks against ICS can cause extensive damage in the physical world. Most of the existing literature describes solutions to protect an ICS against attacks directly targeting its underlying IT infrastructure. However, there are comparatively less works that focus on detecting cyber attacks against the physical process itself. Detection mechanisms that do so are said to be process aware. In this paper, we propose a time-based process aware intrusion detection system (IDS) that detects attacks against a physical process by leveraging its regular nature and temporal properties. The IDS learns the temporal behavior of the process variables and uses it to detect attacks. We evaluate the performance of our IDS on a public SCADA dataset and on a simulated SCADA system developed as part of this study, and we compare it with two other process-aware IDS proposed in the literature. The results show that our solution is able to detect attacks that are not detected by IDS that ignore temporal properties.

A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively.

Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model

Supply chain flexibility is the processing ability of the enterprize to deal with the uncertain environment of supply and demand. In this paper, we consider the supply side (node interrupt) and demand side (demand fluctuations) under uncertain environment. By using the deep belief network (DBN), which is composed of multilayer Restricted Boltzmann Machine (RBM), it establishes the supply chain flexibility network with optimization of the transfer node and flow. The deep belief network is trained by the data of large manufacturing enterprize, compared with the traditional neural network (MLR, BP and GA). The results show that the deep belief network model overcomes the shortcomings of the traditional neural networks, such as easy to fall into local optimum, long training time and low function fitting degree, and it has higher prediction accuracy. This network model based on the deep belief network can promote the supply chain flexibility more, when supply and demand fluctuations occur.

Detecting cyberattacks in industrial control systems using online learning algorithms