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.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

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.

Cyber Security within Smart Cities:A Comprehensive Study and a Novel Intrusion Detection-Based Approach

The expansion of smart cities,facilitated by digital communications,has resulted in an enhancement of the quality of life and satisfaction among residents.The Internet of Things(IoT)continually generates vast amounts of data,which is subsequently analyzed to offer services to residents.The growth and development of IoT have given rise to a new paradigm.A smart city possesses the ability to consistently monitor and utilize the physical environment,providing intelligent services such as energy,transportation,healthcare,and entertainment for both residents and visitors.Research on the security and privacy of smart cities is increasingly prevalent.These studies highlight the cybersecurity risks and the challenges faced by smart city infrastructure in handling and managing personal data.To effectively uphold individuals’security and privacy,developers of smart cities must earn the trust of the public.In this article,we delve into the realms of privacy and security within smart city applications.Our comprehensive study commences by introducing architecture and various applications tailored to smart cities.Then,concerns surrounding security and privacy within these applications are thoroughly explored subsequently.Following that,we delve into several research endeavors dedicated to addressing security and privacy issues within smart city applications.Finally,we emphasize our methodology and present a case study illustrating privacy and security in smart city contexts.Our proposal consists of defining an Artificial Intelligence(AI)based framework that allows:Thoroughly documenting penetration attempts and cyberattacks;promptly detecting any deviations from security standards;monitoring malicious behaviors and accurately tracing their sources;and establishing strong controls to effectively repel and prevent such threats.Experimental results using the Edge-IIoTset(Edge Industrial Internet of Things Security Evaluation Test)dataset demonstrated good accuracy.They were compared to related state-of-theart works,which highlight the relevance of our proposal.

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.

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

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.

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.

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.

Stochastic Simulation of Saline Intrusion in the Coastal Aquifer of Saloum, Senegal

In the Saloum region of central-western Senegal, water needs are essentially met by tapping an underground aquifer associated with the sandy-clay formations of the Continental Terminal, in contact with both the ocean to the west and the highly saline waters of the Saloum River to the north. In this estuarine and deltaic zone with its very low relief, the hydraulic loads in the water tables are generally close to zero or even negative, creating a reversal of the natural flow and encouraging saline intrusion into this system, which makes it very vulnerable. This study concerns the implementation of a numerical model of saline intrusion to provide a better understanding of the vulnerability of the water table by analyzing the variability of the freshwater/saltwater interface. The Modflow-2005 code is used to simulate saline intrusion using the SWI2 module, coupled with the GRASS (Geographic Resources Analysis Support System) software under the Linux operating system with the steep interface approach. The probable expansion of the wedge is studied in three scenarios, taking into account its position relative to the bedrock at 1 m, 5 m and 10 m. Simulations carried out under imposed potential and river conditions, based on variations in groundwater reserves using two effective porosity values, 10−1 and 10−2, show that the water table is highly vulnerable in the northwest sector. The probable expansion of the wedge increases as the storage coefficient decreases and is more marked with river conditions in the areas surrounding the Saloum River, reaching 6 km with a probability of 1. The probability of the wedge reaching a certain degree of expansion decreases from 1 to 0.5, and then cancels out as it moves inland. The probable position of the wedge is limited to 500 m or even 1 km depending on the corner around the coast to the southwest and in the southern zone. This modelling, carried out under natural conditions, will be developed further, taking into account climatic parameters and pumping from wells and boreholes.

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.