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.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Class Imbalanced Problem:Taxonomy,Open Challenges,Applications and State-of-the-Art Solutions

The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the power of data-driven machine learning(ML)and deep learning(DL)techniques.The data imbalance presents major hurdles for classification and prediction problems in machine learning,restricting data analytics and acquiring relevant insights in practically all real-world research domains.In visual learning,network information security,failure prediction,digital marketing,healthcare,and a variety of other domains,raw data suffers from a biased data distribution of one class over the other.This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas.We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations.This article has also identified open challenges for further research in the direction of class data imbalance.

Multifaceted functions of Drp1 in hypoxia/ischemia- induced mitochondrial quality imbalance: from regulatory mechanism to targeted therapeutic strategy

Hypoxic-ischemic injury is a common pathological dysfunction in clinical settings.Mitochondria are sensitive organelles that are readily damaged following ischemia and hypoxia.Dynamin-related protein 1(Drp1)regulates mitochondrial quality and cellular functions via its oligomeric changes and multiple modifications,which plays a role in mediating the induction of multiple organ damage during hypoxic-ischemic injury.However,there is active controversy and gaps in knowledge regarding the modification,protein interaction,and functions of Drp1,which both hinder and promote development of Drp1 as a novel therapeutic target.Here,we summarize recent findings on the oligomeric changes,modification types,and protein interactions of Drp1 in various hypoxic-ischemic diseases,as well as the Drp1-mediated regulation of mitochondrial quality and cell functions following ischemia and hypoxia.Additionally,potential clinical translation prospects for targeting Drp1 are discussed.This review provides new ideas and targets for proactive interventions on multiple organ damage induced by various hypoxic-ischemic diseases.

Photobiomodulation provides neuroprotection through regulating mitochondrial fission imbalance in the subacute phase of spinal cord injury

Increasing evidence indicates that mitochonarial lission imbalance plays an important role in derayed neuronal cell death. Our previous study round that photo biomodulation improved the motor function of rats with spinal cord injury.However,the precise mechanism remains unclear.To investigate the effect of photo biomodulation on mitochondrial fission imbalance after spinal cord injury,in this study,we treated rat models of spinal co rd injury with 60-minute photo biomodulation(810 nm,150 mW) every day for 14 consecutive days.Transmission electron microscopy results confirmed the swollen and fragmented alte rations of mitochondrial morphology in neurons in acute(1 day) and subacute(7 and 14 days) phases.Photo biomodulation alleviated mitochondrial fission imbalance in spinal cord tissue in the subacute phase,reduced neuronal cell death,and improved rat posterior limb motor function in a time-dependent manner.These findings suggest that photobiomodulation targets neuronal mitochondria,alleviates mitochondrial fission imbalance-induced neuronal apoptosis,and thereby promotes the motor function recovery of rats with spinal cord injury.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

Pharmacological effects of denervated muscle atrophy due to metabolic imbalance in different periods

Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality.Complex pathophysiological mechanisms with dynamic alterations have been documented in skeletal muscle atrophy resulting from innervation loss.Hence,an in-depth comprehension of the key mechanisms and molecules governing skeletal muscle atrophy at varying stages,along with targeted treatment and protection,becomes essential for effective atrophy management.Our preliminary research categorizes the skeletal muscle atrophy process into four stages using microarray analysis.This review extensively discusses the pathways and molecules potentially implicated in regulating the four stages of denervation and muscle atrophy.Notably,drugs targeting the reactivare oxygen species stage and the inflammation stage assume critical roles.Timely intervention during the initial atrophy stages can expedite protection against skeletal muscle atrophy.Additionally,pharmaceutical intervention in the ubiquitin-proteasome pathway associated with atrophy and autophagy lysosomes can effectively slow down skeletal muscle atrophy.Key molecules within this stage encompass MuRF1,MAFbx,LC3II,p62/SQSTM1,etc.This review also compiles a profile of drugs with protective effects against skeletal muscle atrophy at distinct postdenervation stages,thereby augmenting the evidence base for denervation-induced skeletal muscle atrophy treatment.

Correlation between cognitive impairment and metabolic imbalance of gut microbiota in patients with schizophrenia

BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influence and respond to a variety of behavioral and mental conditions.AIM To explore the correlation between cognitive impairment and gut microbiota imbalance in patients with schizophrenia.METHODS A total of 498 untreated patients with schizophrenia admitted to our hospital from July 2020 to July 2022 were selected as the case group,while 498 healthy volunteers who underwent physical examinations at our hospital during the same period were selected as a control group.Fluorescence in situ hybridization was employed to determine the total number of bacteria in the feces of the two groups.The cognitive function test package was used to assess the score of cognitive function in each dimension.Then,the relationship between gut microbiota and cognitive function was analyzed.RESULTS There were statistically significant differences in the relative abundance of gut microbiota at both phylum and class levels between the case group and the control group.In addition,the scores of cognitive function,such as attention/alertness and learning ability,were significantly lower in the case group than in the control group(all P<0.05).The cognitive function was positively correlated with Actinomycetota,Bacteroidota,Euryarchaeota,Fusobacteria,Pseudomonadota,and Saccharibacteria,while negatively correlated with Bacillota,Tenericutes,and Verrucomicrobia at the phylum level.While at the class level,the cognitive function was positively correlated with Class Actinobacteria,Bacteroidia,Betaproteobacteria,Proteobacteria,Blastomycetes,and Gammaproteobacteria,while negatively correlated with Bacilli,Clostridia,Coriobacteriia,and Verrucomicrobiae.CONCLUSION There is a relationship between the metabolic results of gut microbiota and cognitive function in patients with schizophrenia.When imbalances occur in the gut microbiota of patients,it leads to more severe cognitive impairment.

肠道菌群与心力衰竭关系的研究进展

慢性心力衰竭患者肠道菌群的构成、功能及代谢产物发生了明显的变化。心力衰竭患者肠道菌群中大肠杆菌/志贺菌属、韦荣球菌属、肠杆菌属丰度升高。色胺酸代谢、脂质代谢和脂多糖合成等微生物功能增加,且与生成有益代谢产物丁酸盐相关的细菌基因减少,有害代谢产物氧化三甲胺相关的细菌基因明显增加。肠道菌群的代谢产物,如氧化三甲胺、短链脂肪酸、氨基酸衍生物、胆汁酸,是肠道菌群与宿主相互作用的主要途径之一。这些代谢产物可以直接吸收进入宿主循环系统,然后迁移至不同的器官;或者被宿主酶代谢,产生信号分子,从而发挥作用。

区域水平衡及失衡程度度量方法

区域水平衡量化及诊断是水网工程建设优化和水资源均衡调控的基础,对提升水安全保障能力、支撑经济社会稳定发展、实现人水和谐具有重要意义。本文从空间、时间、结构、过程、总量五个维度解构了区域人水系统的四大平衡,即反映水收入项和支出项定量关系的水收支平衡、体现经济社会供水量与需水量匹配关系的经济社会供需水平衡、结合经济社会-生态系统发展与用水定量关系的经济社会与生态用水平衡、表征人水系统发展状态的人水关系和谐平衡,并提出相应的水平衡度量方法。多角度解析了区域水平衡的失衡状态,提出四大平衡的失衡程度度量方法及区域水平衡的失衡程度综合度量方法。以沁河流域为实例应用和检验了上述方法,结果表明:提出的度量方法具有科学性和适用性,能够应用于区域水平衡研究实践;2019年沁河流域水平衡的失衡度为0.327,处于蓄水量减少、水资源短缺、生态端亏水、水系统受损的中度失衡状态,后续应注重人类涉水行为的管控。研究成果可为区域水平衡评估与调控提供理论支撑,服务国家或区域水平衡策略制定和水网工程规划。

财政纵向失衡与地方财政支出周期失衡

本文采用1980—2019年数据,在制度分析和事实梳理基础上,运用面板联立方程组探讨了财政纵向失衡对地方财政支出周期失衡的影响及财政收入周期在二者之间的传导机制。研究结果表明:财政纵向失衡加剧地方财政支出周期失衡,财政收入周期是财政纵向失衡影响地方财政支出周期失衡的重要传导机制,但财政纵向失衡对财政收入周期和地方财政支出周期的影响方向相反。分税制改革后,财政纵向失衡对地方财政支出周期失衡的直接影响和间接影响的方向没有变化,但总效应从正向扩张效应转向负向抑制效应。本文的研究结论为缓解财政纵向失衡、提高政府逆周期调节能力提供了决策依据。

在城乡融合发展中全面推进乡村振兴:核心任务、突出问题与关键举措

全面推进乡村振兴是解决城乡发展不平衡不充分问题的重要举措,是贯彻落实新发展理念的生动体现。首先,梳理中国城乡关系的走向以及“三农”工作总抓手的转变,总结归纳在城乡融合发展中全面推进乡村振兴的实践逻辑。其次,针对在城乡融合发展中全面推进乡村振兴需要完成“农业强、农村美、农民富”的三大核心任务。再次,研究发现:中国正面临农村经济发展相对落后、生态环境短板挑战较大、城乡要素双向流动不畅、公共资源配置均等化程度不高、城乡收入失衡局面尚待扭转以及县域经济综合承载力不足等突出问题。最后,从加速农业现代化转型、加强乡村治理、打破城乡制度性约束、加大农村公共资源投入力度、多渠道增加农民收入以及推动县域经济高质量发展等角度提出相应的政策建议。

家族相似性、符号圈与艺术跨媒介叙事

总体性的“艺术”仅仅是一个相对意义上的统称,一个代表所有具体门类艺术的“家族相似性”概念。洛特曼把总体性的艺术看成一个大的“符号圈”,而这个符号圈中又存在由各门类艺术构成的多个小符号圈。符号圈具有两个基本特征:一是有界性,即符号圈是有边界的,主要关涉内外关系;二是不均衡性,即符号圈区分为“中心”与“外围”,这是符号圈的内部组织规则。这两个特征都给“艺术文本”的跨媒介叙事带来了深刻影响。综合运用“家族相似性”和“符号圈”理论,不仅可以很好地解释艺术与非艺术,以及艺术符号圈中不同艺术门类之间的跨媒介叙事问题,而且可以合理地解释同一种文学、艺术体裁中的“互参”现象。

基于特征变量扩展的含气饱和度随机森林预测方法

采用数据驱动的方式,提出了一种基于随机森林机器学习算法训练出含气饱和度地震预测方法,并将该方法应用于中国西部复杂天然气藏中,分别对单井资料和二维地震资料进行了含气饱和度预测与分析。研究结果表明:(1)抽取井旁道纵波速度、横波速度和密度3个弹性参数叠前地震反演结果作为基本特征变量样本,引入边界合成少数类过采样技术对基本特征变量样本和对应的含气饱和度样本进行平衡化处理;利用扩展弹性阻抗结合数学变换自动生成一系列的扩展变量;再利用随机森林对特征变量进行含气饱和度预测重要性排名,并优选重要性较高的特征变量进行含气饱和度随机森林训练。(2)该方法大幅减少了特征变量提取和优选的人工工作量,且有效减少了信息冗余以及因含气饱和度样本不平衡导致的训练偏倚问题,有效增强了随机森林算法在含气饱和度地震预测方面的能力。(3)实际单井应用中预测的含气饱和度与测井解释的含气饱和度的相关系数可达0.9855;在二维地震资料应用中,该方法比基于常规未平衡化的11个弹性参数作为随机森林输入预测出的含气饱和度精度更高。

网络著作权算法私人执法的异化及其矫正

经由算法赋能、私法制度提供合法性的网络著作权算法私人执法,借助公权力体系肆意扩张,正在架构新的社会关系和催生新的社会规范。算法的设计缺陷和法律规制的缺失使得网络著作权算法私人执法存在异化为私主体的利益实现工具的风险,可能造成对网络用户合法权益的侵犯,具体体现为侵蚀用户表达权利、挤压公众合理使用空间、剥夺用户经济利益等。权力与权利的平衡应成为矫正网络著作权算法私人执法异化的规制目标,循着公权力合理限制私权力、增益私权利对抗私权力的规制思路,通过立法建立限权与赋权的制度体系,具体应建立算法审查制度、基本权利保护责任制度以及算法问责制度,配置用户算法解释权和申诉权等,可激发最大制度性合力,实现多元主体的利益平衡。

基于端口注意力与通道空间注意力的网络异常流量检测

网络异常流量检测是网络安全保护重要组成部分之一。目前,基于深度学习的异常流量检测方法都是将端口号属性与其他流量属性同等对待,忽略了端口号的重要性。为了提高异常流量检测性能,借鉴注意力思想,提出一个卷积神经网络(CNN)结合端口注意力模块(PAM)和通道空间注意力模块(CBAM)的网络异常流量检测模型。首先,将原始网络流量作为PAM的输入,分离得到端口号属性送入全连接层,得到学习后的端口注意力权重值,并与其他流量属性点乘,输出端口注意力后的流量数据;其次,将流量数据转换成灰度图,利用CNN和CBAM更充分地提取特征图在通道和空间上的信息;最后,使用焦点损失函数解决数据不平衡的问题。所提PAM具有参数量少、即插即用和普遍适用的优点。在CICIDS2017数据集上,所提模型的异常流量检测二分类任务准确率为99.18%,多分类任务准确率为99.07%,对只有少数训练样本的类别也有较高的识别率。

我国养老服务产业优化发展策略研究

我国正处在人口老龄化进程加速发展的关键期,对养老服务产业提出了庞大需求。文章探索养老服务产业发展的优化策略,为政府决策提供现实依据。通过实地调研、访谈等方式调查国内养老服务产业的需求和供给现状,查找养老服务产业发展过程中的难点,结合对美国、澳大利亚、日本和德国等国家养老服务产业的文献分析,总结其成功的经验。基于上述分析研究,提出增加制度供给引导行业有序发展、鼓励参与主体公平竞争、建立多渠道筹资机制、探索长期护理保险制度、完善养老服务服务人才培养体系、建立资格准入制度、搭建人才发展平台等方面提出具体对策。

数字经济对基本公共服务的影响研究

数字经济是中国经济发展的重要引擎之一,数字技术赋能公共服务,有助于改善基本公共服务供给,推动基本公共服务均等化。本文基于2010—2020年中国31个省份面板数据,构建基本公共服务供给水平指标体系,实证检验了数字经济对基本公共服务供给水平及基本公共服务均等化的影响。研究结果表明,数字经济能够显著提升基本公共服务供给水平,这一结论在进行内生性处理和一系列稳健性检验后依然成立。机制分析结果表明,数字经济可以通过提高地方政府财政收入和减少财政纵向失衡提升基本公共服务供给水平。异质性分析表明,与东部地区和西部地区相比,数字经济对中部地区基本公共服务供给水平的促进效果更为明显。进一步研究表明,数字经济能够提升基本公共服务均等化水平。上述结论能够为更好地发挥数字经济特性、提高基本公共服务供给水平、促进基本公共服务均等化提供理论依据。

中国性别失衡转变与家庭发展:基于人口普查与公开数据的发现

中国出生性别比失衡已经持续40余年,性别失衡后果和风险正在由个体扩散至家庭层面,对家庭发展产生深远影响,但现有研究缺乏基于宏观数据对二者变化态势的基本认识。使用2000—2020年全国人口普查和统计年鉴等公开数据,基于人口和家庭转型背景,从家庭结构-功能构建家庭发展指数,实证分析最近20年性别失衡转变和家庭发展的时空变化情况,探讨性别失衡转变和家庭发展的关系。研究发现,中国性别失衡正在进入出生性别比持续下降、后果开始显现的转变阶段,省际因人口、经济、社会等发展差异呈现不同阶段特征;中国家庭发展整体水平较低,当面临性别失衡转变时,存在结构功能发展不协调、地区间发展不平衡和总体发展不充分的问题。建议继续加强引导出生性别比回归正常以减少各年龄段性别结构失衡累积,积极探索面向家庭的性别失衡后果治理,充分考虑家庭发展的地区差异和所处阶段,兼顾家庭结构和功能协调发展,促进人口长期均衡与可持续发展。