Geochemical Characteristics and Metallogenesis of the Qingkuangshan Ni-Cu-PGE Mineralized Mafic-Ultramafic Intrusion in Huili County, Sichuan Province, SW China

The Qingkuangshan Ni-Cu-PGE deposit, located in the Xiaoguanhe region of Huili County, Sichuan Province, is one of several Ni-Cu-PGE deposits in the Emeishan Large Igneous Province （ELIP）. The ore-bearing intrusion is a mafic-ultramafic body. This paper reports major elements, trace elements and platinum-group elements in different types of rocks and sulfide-mineralized samples in the intrusion. These data are used to evaluate the source mantle characteristics, the degree of mantle partial melting, the composition of parental magma and the ore-forming processes. The results show that Qingkuangshan intrusion is part of the ELIP. The rocks have trace element ratios similar to the coeval Emeishan basalts. The primitive mantle-normalized patterns of Ni-Cu-PGE have positive slopes, and the ratios of Pd/Ir are lower than 22. The PGE compositions of sulfide ores and associated rocks are characterized by Ru depletion. The PGE contents in bulk sulfides are slightly depleted relative to Ni and Cu, which is similar to the Yangliuping Ni-Cu-PGE deposit. The composition of the parental magma for the intrusion is estimated to contain about 14.65 wt% MgO, 48.66 wt% SiO2 and 15.48 wt% FeOt, and the degree of mantle partial melting is estimated to be about 20%. In comparison with other typical Ni-Cu-PGE deposits in the ELIP, the Qingkuangshan Ni-Cu-PGE deposit has lower PGE contents than the Jinbaoshan PGE deposit, but has higher PGE contents than the Limahe and Baimazhai Ni-Cu deposit, and has similar PGE contents to the Yangliuping Ni-Cu-PGE deposit. The moderate PGE depletions in the bulk sulfide of the Qingkuanghan deposit suggest that the parental magma of the host intrusion may have undergone minor sulfide segregation at depth. The mixing calculations suggests that an average of 10% crustal contamination in the magma, which may have been the main cause of sulfide saturation in the magma. We propose that sulfide segregation from a moderately PGE depleted magma took place prior to magma emplacement at Qingkuangshan, that small amounts of immiscible sulfide droplets and olivine and chromite crystals were suspended in the ascending magma, and that the suspended materials settled down when the magma passed trough the Qingkuangshan conduit. The Qingkuangshan sulfide-bearing intrusion is interpreted to a feeder of Emeishan flood basalts in the region.

Geochemical characteristics and tectonic setting of the Tuerkubantao mafic-ultramafic intrusion in West Junggar,Xinjiang,China

Mineral chemistry, whole-rock major oxide, and trace element compositions have been determined for the Tuerkubantao mafic-ultramafic intrusion, in order to understand the early Paleozoic tectonic evolution of the West Junggar orogenic belt at the southern margin of the Central Asian orogenic belt. The Tuerkubantao mafic-ultramafic intrusion is a well-differentiated complex comprising peridotite, olivine pyroxenite, gabbro, and diorite. The ultramafic rocks are mostly seen in the central part of the intrusion and surrounded by mafic rocks. The Tuerkubantao intrusive rocks are characterized by enrichment of large ion lithophile elements and depleted high field strength elements relative to N-MORB. In addition, the Tuerkubantao intrusion displays relatively low Th/U and Nb/U （1.13-2.98 and 2.53-7.02, respectively） and high La/Nb and Ba/Nb （1.15 4.19 and 37.7-79.82, respectively）. These features indicate that the primary magma of the intrusion was derived from partial melting of a previously metasomatized mantle source in a subduction setting. The trace element patterns of peridotites, gabbros, and diorite in the Tuerkubantao intrusion have sub-parallel trends, suggesting that the different rock types are related to each other by differentiation of the same primary magma. The intrusive contact between peridotite and gabbro clearly suggest that the Tuerkubantao is not a fragment of an ophiolite. However, the Tuerkubantao intrusion displays many similarities with Alaskan-type mafic-ultramafic intrusions along major sutures of Phanerozoic orogenic belts. Common features include their geodynamic setting, internal lithological zoning, and geochemistry. The striking similarities indicate that the middle Devonian Tuerkubantao intrusion likely formed in a subduction-related setting similar to that of the Alaskan-type intrusions. In combination with the Devonian magmatism and porphyry mineralization, we propose that subduction of the oceanic slab has widely existed in the expansive oceans during the Devonian around the Junggar block.

SHRIMP Zircon U-Pb Age of the Sidingheishan Mafic-Ultramafic Intrusion in the Southern Margin of the Central Asian Orogenic Belt,NW China and its Petrogenesis implication

Objective The Sidingheishan mafic-ultramafic intrusion is located in the eastern part of the North Tianshan Mountains. This work used zircon U-Pb age data, bulk rock major and trace elements, Sr-Nd-Pb isotope data to assess mantle source characteristics and crustal assimilation of the parental magma of the Sidingheishan intrusion. We have also discussed the tectonic evolution of the southern margin of the Central Asian Orogenic belt in the Late Paleozoic.

Geochronology,Petrology and Geochemistry of Xingdi No.3 Mafic-Ultramafic Intrusions in the Northeastern Tarim Craton,NW China

The Xingdi mafic-ultramafic intrusions occur in the northeastern margin of the Tarim craton. The Xingdi No. 3 intrusion is the smallest of four intrusions, with an exposed area of 1.7 km2, and the zircon U-Pb age of the intrusion is 752±4 Ma. The intrusion consists of gabbros, pyroxenites and peridotites, and exhibits a crystallization sequence of the main rock-forming minerals as olivine, orthopyroxene, clinopyroxene and plagioclase. Mineralization occurred at or near the boundaries of the intrusion between pyroxenites and peridotites, and appears as a layered or lenticular shape about 500 m long and 4–15 m wide. The primary sulfides have a relatively simple mineralogy dominated by pyrrhotite-pentlandite-chalcopyrite assemblages, which occur as droplet, star-like and graphic texture and locally sideronitic structures. Geochronological and geochemistry investigations suggest that the Xingdi mafic-ultramafic intrusions and coeval volcanic rock in the Kuluktag area of the Tarim craton formed in an intracontinental breakup environment. Based on the composition of the dominant rockforming minerals and covariant relationships of other oxides versus Mg O, the parental magma of the Xingdi No.3 intrusion belongs to high-Mg tholeiitic basaltic magmas with Mg O of 10.78 wt%. The Xingdi No.3 intrusive rocks are characterized by light REE enrichment relative to heavy REE, negative Nb-Ta anomalies, low 143Nd/144Nd ratios（from 0.511183 to 0.511793） and high initial 87Sr/86Sr ratios（from 0.7051 to 0.7113）. The magma was derived from the enriched-lithospheric mantle and was contaminated during emplacement. According to rock assemblages, mineralization, olivine characteristics, geochemical characteristics and mass balance, there are better copper-nickel ore prospects in the Xingdi No.3 intrusion than in the other three intrusions in the area.

Geochemical and Petrological Studies on the Early Carboniferous Sidingheishan Mafic-Ultramafic Iintrusion in the Southern Margin of the Central Asian Orogenic Belt,NW China

The Sidingheishan mafic-ultramafic intrusion is located in the eastern part of the Northern Tianshan Mountain, along the southern margin of the Central Asian Orogenic Belt in northern Xinjiang autonomous region of China. The Sidingheishan intrusion is mainly composed of wehrlite, olivine websterite, olivine gabbro, gabbro and hornblende gabbro. At least two pulses of magma were involved in the formation of the intrusion. The first pulse of magma produced an olivine-free unit and the second pulse produced an olivine-bearing unit. The magmas intruded the Devonian granites and granodiorites.An age of 351.4±5.8 Ma(Early Carboniferous) for the Sidingheishan intrusion has been determined by U-Pb SHRIMP analysis of zircon grains separated from the olivine gabbro unit. A U-Pb age of 359.2±6.4 Ma from the gabbro unit has been obtained by LA-ICP-MS. Olivine of the Sidingheishan intrusion reaches 82.52 mole% Fo and 1414 ppm Ni. On the basis of olivine-liquid equilibria, it has been calculated that the MgO and FeO included in the parental magma of a wehrlite sample were approximately10.43 wt% and 13.14 wt%, respectively. The Sidingheishan intrusive rocks are characterized by moderate enrichments in Th and Sm, slight enrichments in light REE, and depletions in Nb, Ta, Zr and Hf. The εNd(t) values in the rock units vary from +6.70 to +9.64, and initial87Sr/86Sr ratios range between 0.7035 and0.7042. Initial206Pb/204Pb,207Pb/204Pb and208Pb/204Pb values fall in the ranges of 17.23-17.91,15.45-15.54 and 37.54-38.09 respectively. These characteristics are collectively similar to the Heishan intrusion and the Early Carboniferous subduction related volcanic rocks in the Santanghu Basin, North Tianshan and Beishan area. The low(La/Gd)PMvalues between 0.26 and 1.77 indicate that the magma of the Sidingheishan intrusion was most likely derived from a depleted spinel-peridotite mantle.(Th/Nb)PMratios from 0.59 to 20.25 indicate contamination of the parental magma in the upper crust.Crystallization modeling methods suggest that the parental magma of the Sidingheishan intrusion was generated by flush melting of the asthenosphere and subsequently there was about 10 vol%contamination from a granitic melt. This was followed by about 5 vol% assimilation of upper crustal rocks. Thus, the high-Mg basaltic parental magma of Sidingheishan intrusion is interpreted to have formed from partial melting of the asthenosphere during the break-off of a subducted slab.

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.

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.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

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.

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and applied.Among them,Intrusion Detection System(IDS)has been proven to be stable and efficient.However,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious attacks.To address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy.TheMarkov TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS model.Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.