Himalayas as a global hot spot of springtime stratospheric intrusions:Insight from isotopic signatures in sulfate aerosols

Downward transport of stratospheric air into the troposphere(identified as stratospheric intrusions)could potentially modify the radiation budget and chemical of the Earth's surface atmosphere.As the highest and largest plateau on earth,the Tibetan Plateau including the Himalayas couples to global climate,and has attracted widespread attention due to rapid warming and cryospheric shrinking.Previous studies recognized strong stratospheric intrusions in the Himalayas but are poorly understood due to limited direct evidences and the complexity of the meteorological dynamics of the third pole.Cosmogenic^(35)S is a radioactive isotope predominately produced in the lower stratosphere and has been demonstrated as a sensitive chemical tracer to detect stratospherically sourced air mass in the planetary boundary layer.Here,we report 6-month(April–September 2018)observation of^(35)S in atmospheric sulfate aerosols(^(35)SO_(4)^(2-))collected from a remote site in the Himalayas to reveal the stratospheric intrusion phenomenon as well as its potential impacts in this region.Throughout the sampling campaign,the^(35)SO_(4)^(2-)concentrations show an average of 1,070±980 atoms/m^(3).In springtime,the average is 1,620±730 atoms/m^(3),significantly higher than the global existing data measured so far.The significant enrichments of^(35)SO_(4)^(2-)measured in this study verified the hypothesis that the Himalayas is a global hot spot of stratospheric intrusions,especially during the springtime as a consequence of its unique geology and atmospheric couplings.In combined with the ancillary evidences,e.g.,oxygen-17 anomaly in sulfate and modeling results,we found that the stratospheric intrusions have a profound impact on the surface ozone concentrations over the study region,and potentially have the ability to constrain how the mechanisms of sulfate oxidation are affected by a change in plateau atmospheric properties and conditions.This study provides new observational constraints on stratospheric intrusions in the Himalayas,which would further provide additional information for a deeper understanding on the environment and climatic changes over the Tibetan Plateau.

Lacustrine shale oiliness influenced by diabase intrusions in the Paleogene Funing Formation,Subei Basin,China

The research on the correlation between hydrocarbon accumulations and magmatic activities has always attracted aroused much wide attention.Existing research has primarily the hydrocarbon generations capability of source rocks and the quality of reservoirs by diabase intrusions.whereas,rare systematic research has been conducted on the oiliness and enrichment mechanism.To be specific,the diabase intrusive zone,the contact metamorphic zone and the normal shale zone of the Funing Formation in the Gaoyou Sag,Subei Basin were taken as the object of this study.Moreover,in this study,the hydrocarbon generation quality,reservoir quality,and oil-bearing quality of diabase-metamorphic zone-normal shale were evaluated using X-ray diffractions,argon ion polishing-field emission scanning electron microscope,energy spectrum,rock slice/light-sheet microscopic observations,organic geochemical tests,N_(2) gas adsorption and 2D NMR tests.The results indicated that the intrusive zone,the metamorphic zone,and the normal zone were formed in order by the degree of effect of diabase intrusions.Secondly,the oil content of different parts exhibited significant heterogeneity due to the baking effect of diabase,and the metamorphic zone had the significantly better oil-bearing nature than the intrusive zone.Lastly,a distribution model was proposed for the diabase intrusive zone-metamorphic zone-normal surrounding rock zone.

Quaternary Intrusions from the Zhongjiannan Basin,South China Sea:Their Relationship with the Hainan Mantle Plume and Influence on Hydrocarbon Reservoirs

The transportation of magma in sedimentary basins often occurs through extensive dyke-sill networks.The role of sills on the plumbing system in rifted margins and the impact of sills on hydrocarbon reservoirs of prospective sedimentary basins has long been an area of great industrial interest and scientific debate.Based on 2D seismic reflection,we present data on how the sills emplaced to form a magmatic plumbing system of the volcanic system for the Zhongjiannan Basin(ZJNB).The results show that sixty-nine sills and fourteen forced folds have been identified.The distribution and geometry of the sills suggest that magma flowed from west to east and then ascended to near the surface.The onlap relationship of the forced folds indicates that the timing of magmatic activities can be constrained at ca.0.2 Ma.The spatial and temporal occurrences of intrusions imply that the strong post-rift magmatism in ZJNB was associated with the Hainan mantle plume arising from the core-mantle boundary.Furthermore,these forced folds could produce several types of hydrocarbon traps,due to accommodation through bending and uplift of the overlying rock and free surface,but it is critical to evaluate the effect of such emplacement when setting exploration targets.

Characterization of Near Surface Intrusions in South-West Cameroon Zone Using Gravity Data: Mining and Geothermal Implications

The succession of tectonic phenomena in the South-West Cameroon area suggests that structures from the upper mantle infiltrated and took advantage of the cracks and fractures left by these phenomena to get closer to the earth’s surface. However, the intrusive structures closest to the surface remain poorly known. The objective of this work is to improve the knowledge related to the interpretation of gravity data in order to characterise the near-surface intrusive bodies in the South-West Cameroon area, and then analyse their mining and geothermal implications. To achieve this objective, the indirect, inverse and normalized standard deviation (NSTD) methods were used. The NSTD method was used to detect the contours of the intrusive bodies. The indirect method (spectral analysis) was used to determine the depths of the interfaces of three intrusive bodies, one located on the Bipindi-Ebolowa I axis (G5), the other on the Eseka-Pouma axis (G8) and the last on the Bokito-Monatele axis (G11). The results obtained show roofs located between 0 and 0.61 km, between 0 and 0.37 km and between 0 and 0.73 km for the G5, G8 and G11 bodies, respectively. Finally, the application of the 2D inversion method allowed us to estimate the density contrasts of the intrusive bodies (G5, G8 and G11). The superposition of the intrusive bodies detected by the NSTD with the geological and mineral resources map, as well as an analysis of the results obtained, gave indications of interesting zones for mining prospecting and for the search for geothermal reservoirs.

Subducting passive continental margins with crustal (ultra)mafic intrusions: An underappreciated mechanism for recycling water back into the mantle

It is well known that outer rise bending-assisted oceanic plate hydration is an important mechanism for transporting substantial amounts of water into the mantle.A key question is:Are there other equally or more important water transport mechanisms?Here we propose,for the first time,that subducting passive continental margins,particularly those with crustal(ultra)mafic intrusions,play a critical role in recycling water back into the mantle.Evidence for this mechanism is the exceptionally high outer rise seismicity observed in a subducting passive continental margin(i.e.,the northeastern South China Sea continental margin)near the northern Manila trench,characterized by a high-velocity lower crust that has been attributed to(ultra)mafic intrusions.Our interpretation of this correlation between high outer rise seismicity and lower crust(ultra)mafic intrusions is that(ultra)mafic intrusions alter the crustal rheology and increase brittle deformation in the lower crust in this region,thereby promoting lithospheric fracturing and plate hydration,which is evidenced by increased outer rise seismicity.

A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Igneous intrusion contact metamorphic system and its reservoir characteristics:A case study of Paleogene Shahejie Formation in Nanpu sag of Bohai Bay Basin,China

Taking the Paleogene Shahejie Formation in Nanpu sag of Bohai Bay Basin as an example,this study comprehensively utilizes seismic,mud logging,well logging,physical property analysis and core thin section data to investigate the metamorphic reservoir formed by contact metamorphism after igneous rock intrusion.(1)A geological model of the igneous intrusion contact met amorphic system is proposed,which can be divided into five structural layers vertically:the intrusion,upper metamorphic aureole,lower metamorphic aureole,normal sedimentary layers on the roof and floor.(2)The intrusion is characterized by xenoliths indicating intrusive facies at the top,regular changes in rock texture and mineral crystallization from the center to the edge on a microscopic scale,and low-angle oblique penetrations of the intrusion through sedimentary strata on a macroscopic scale.The metamorphic aureole has characteristics such as sedimentary rocks as the host rock,typical palimpsest textures developed,various low-temperature thermal metamorphic minerals developed,and medium-low grade thermal metamorphic rocks as the lithology.(3)The reservoir in contact metamorphic aureole has two types of reservoir spaces:matrix pores and fractures.The matrix pores are secondary"intergranular pores"distributed around metamorphic minerals after thermal metamorphic transformation in metasandstones.The fractures are mainly structural fractures and intrusive compressive fractures in metamudstones.The reservoirs generally have three spatial distribution characteristics:layered,porphyritic and hydrocarbon impregnation along fracture.(4)The distribution of reservoirs in the metamorphic aureole is mainly controlled by the intensity of thermal baking.Furthermore,the distribution of favorable reservoirs is controlled by the coupling of favorable lithofacies and thermal contact metamorphism,intrusive compression and hydrothermal dissolution.The proposal and application of the geological model of the intrusion contact metamorphic system are expected to promote the discovery of exploration targets of contact metamorphic rock in Nanpu sag,and provide a reference for the study and exploration of deep contact metamorphic rock reservoirs in the Bohai Bay Basin.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected world.However,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion attacks.In addition,IoT devices generate a high volume of unstructured data.Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data sources.Given the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack efficiency.Across numerous research domains,deep learning techniques have demonstrated their capability to precisely detect anomalies.This study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT networks.Firstly,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error.Secondly,the Convolutional Neural Network(CNN)technique is employed to create a binary classification approach.The proposed SAE-CNN approach is validated using the Bot-IoT dataset.The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of 0.9992.In addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Lightweight Intrusion Detection Using Reservoir Computing

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.