Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

According to BBC News,online hate speech increased by 20%during the COVID-19 pandemic.Hate speech from anonymous users can result in psychological harm,including depression and trauma,and can even lead to suicide.Malicious online comments are increasingly becoming a social and cultural problem.It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level.To achieve a healthy and safe Internet environment,studies should focus on institutional and technical topics.The detection of toxic comments can create a safe online environment.In this study,to detect malicious comments,we used approxi-mately 9,400 examples of hate speech from a Korean corpus of entertainment news comments.We developed toxic comment classification models using supervised learning algorithms,including decision trees,random forest,a support vector machine,and K-nearest neighbors.The proposed model uses random forests to classify toxic words,achieving an F1-score of 0.94.We analyzed the trained model using the permutation feature importance,which is an explanatory machine learning method.Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea.Using this research methodology,the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

Design and Implementation of Novel Precision Internet Marketing Patterns under the Big Data and Cloud Environment

Nowadays most of the cloud applications process large amount of data to provide the desired results. The Internet environment, the enterprise network advertising, network marketing plan, need partner sites selected as carrier and publishers. Website through static pages, dynamic pages, floating window, AD links, take the initiative to push a variety of ways to show the user enterprise marketing solutions, when the user access to web pages, use eye effect and concentration effect, attract users through reading web pages or click the page again, let the user detailed comprehensive understanding of the marketing plan, which affects the user＇ s real purchase decisions. Therefore, we combine the cloud environment with search engine optimization technique, the result shows that our method outperforms compared with other approaches.

Internet based design of distributed virtual environment for multi-robot teleoperation

Studies the design of distributed virtual environments （DVEs） for tele-multi-robotics. The proposed design, incorporating two models （ distributlon-supported model and VE-supported model）, attempts to represent common functionality, communication issues, and requirements found in multi-operator DVEs. The distribution-supported model concentrates on the introduction of computer-supported collaborative work （CSCW） to realize the coordination of multi-operators, while the VE-supported model concentrates on the utilization of an object-oriented approach to strengthen the expandability and robustness of the system. Finally, the configuration anti running environments of the system are given.

Design and Implementation of Environment Monitoring and Alarm System for Broiler Breeding Based on Internet of Things

Environment plays a vital role in the breeding process of broilers. In order to effectively monitor and control the breeding environment of broilers, a broiler breeding environment monitoring and alarm system based on internet of things is studied and established. The system adopts the narrow band internet of things communication technology, and can transmit the temperature, humidity, illumination and ammonia and other environ-mental data in the chicken house remotely through terminal collection unified interface device. Meantime, it realizes the control of fans, wet cur-tains, small windows and illumination in the chicken house by threshold method. The system software is designed and implemented by C#, SQL Server, WeX5 and other development tools, including platform terminal and enterprise terminal. Since its operation, the system is featured by stable state, reliable data and timely alarm, which solves the problem of unified control of different sensors and realizes the effective control of house envi-ronment.

Novel Architecture of Security Orchestration, Automation and Response in Internet of Blended Environment

New technologies that take advantage of the emergence of massive Internet of Things(IoT)and a hyper-connected network environment have rapidly increased in recent years.These technologies are used in diverse environments,such as smart factories,digital healthcare,and smart grids,with increased security concerns.We intend to operate Security Orchestration,Automation and Response(SOAR)in various environments through new concept definitions as the need to detect and respond automatically to rapidly increasing security incidents without the intervention of security personnel has emerged.To facilitate the understanding of the security concern involved in this newly emerging area,we offer the definition of Internet of Blended Environment(IoBE)where various convergence environments are interconnected and the data analyzed in automation.We define Blended Threat(BT)as a security threat that exploits security vulnerabilities through various attack surfaces in the IoBE.We propose a novel SOAR-CUBE architecture to respond to security incidents with minimal human intervention by automating the BT response process.The Security Orchestration,Automation,and Response(SOAR)part of our architecture is used to link heterogeneous security technologies and the threat intelligence function that collects threat data and performs a correlation analysis of the data.SOAR is operated under Collaborative Units of Blended Environment(CUBE)which facilitates dynamic exchanges of data according to the environment applied to the IoBE by distributing and deploying security technologies for each BT type and dynamically combining them according to the cyber kill chain stage to minimize the damage and respond efficiently to BT.

Intrusion detection and prevention system for an IoT environment

Internet of Things(IoT)security is the act of securing IoT devices and networks.IoT devices,including industrial machines,smart energy grids,and building automation,are extremely vulnerable.With the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this study.An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering.The proposed NBIPS inspects network activity streams to identify and counteract misuse instances.The NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe substances.Networkbased IPS sensors can be installed either in an inline or a passive model.An inline sensor is installed to monitor the traffic passing through it.The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern economy.It aims at enhancing road safety and environmental quality.There is a tremendous increase observed in the number of vehicles in recent years,owing to increasing population.Each vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard value.Owing to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution.The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS environment.The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants.In addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the sensors.In HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO algorithm.In order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

基于物联网技术的育雏鸡舍环境监控系统设计与应用

本文设计基于物联网技术的育雏鸡舍环境监控系统,在监控系统设计中从软件与硬件两个部分对整体结构进行构建。其主要功能包括育雏鸡舍环境温湿度监测,有害气体浓度监测以及自动加温与报警。主要功能设计完成后,分别从软件与硬件两个部分进行系统调试,设计系统整体功能实现符合育雏鸡舍环境内部监控系统使用要求,能够帮助提升育雏鸡舍管理能力,降低管理中投入的人力成本。

物联网技术支持下的城市热环境时空变化分析

热环境是城市生态系统最为关键的大气环境要素之一,开展城市热环境研究的前提是在时空维度上获取足量的热环境参数。利用先进的物联网技术构建了在线式热环境监测设备,收集2022年10月16日至24日广州大学校园气温、风速、太阳辐射和地面温度四种热环境参数,分析小尺度下城市热环境的时空变化特征。研究结果表明:1)不同测点之间的风速特征具有一定的相关性,极个别测点的风速特征与其它测点相关性仅在0.5左右,显示其小气候的独特性;2)不同测点风速和热岛强度变化时空差异明显,即使距离靠近的测点,受邻近建筑和植被特征的影响热环境特征会有所差异,各测点的日间风速大,热岛强度较为明显,夜间风速较小,热岛强度较弱;3)地面温度与气温的相关性达0.8左右,这种相关性在夜间表现更为密切,并且这种关系受风速的影响不大。研究结果反映了城市热环境参数在小尺度上的高度异质性,并揭示了物联网技术在城市热环境监测领域的可行性、便捷性和高效性。

智能时代二次创作的著作权保护与限制研究

智能时代,二次创作产业勃兴发展,原始作品激励与二次创作自由急需一个平衡经济效益和法律正义的协调机制。本文建议:首先,应加快著作权集体管理组织和相关制度的完善,有效对接互联网环境下的著作权集体管理,强化网络服务经营者的监管义务,优化授权许可模式;其次,将“三步检验法+转换性使用”的判断标准纳入合理使用判断体系之中,更加科学合理地判断互联网环境下的二次创作;再次,对于未经许可的二次创作作品,应对其进行著作权法消极保护,赋予其一定的禁止性权利,限制其积极性权利的行使,同时通过法律设计引导市场对未经许可的二次创作进行调节,减少司法资源浪费。

物联网环境下复杂光纤通信传输网络节点调度

为了解决物联网环境下,光纤通信传输网络节点能耗高、调度效率低的问题,提出一种物联网环境下复杂光纤通信传输网络节点调度方法。在物联网环境下,分析光纤通信传输网络节点的分布情况,根据动态联盟理论,以最快调度速度和最小投资为目标,构建复杂光纤通信传输网络节点调度模型。通过蚁群和粒子群算法相结合的融合算法展开全部搜索,获取最佳调度方案,实现光纤通信传输网络节点调度。实验结果表明,所提方法的节点平均能耗在6.0 mJ以下,LBF值始终高于0.20,且节点调度耗时始终低于200 ms,具有良好的调度效果。

高管团队与组织环境如何驱动企业实现高水平颠覆性创新——来自25个互联网企业案例的组态分析

颠覆性创新是企业获取核心竞争优势,实现跨越式发展的重要方式。激烈竞争环境下,如何提升颠覆性创新水平是企业亟需解决的现实问题。基于25家国内互联网企业案例,运用模糊集定性比较方法,从高管团队、组织环境层面探讨企业颠覆性创新驱动要素与等效路径。结果发现:企业高水平颠覆性创新是高管团队与组织环境两个维度下的多因素作用结果;企业高水平颠覆性创新存在6条路径,这6条路径能够反映出不同组织环境下,高管团队不同特征组合对企业高水平颠覆性创新的驱动机制;进一步对6条路径比较分析发现,高教育水平、高风险偏好、高市场竞争强度作为核心条件发挥关键作用,组织惯性成为驱动企业实现高水平颠覆性创新的重要条件,由此表明组织惯性可能并非是企业颠覆性创新的制约因素。

基于物联网的城市网格化大气环境监测系统设计

针对城市的大气环境的综合治理问题,提出网格化环境监测部署方案。综合运用物联网、嵌入式系统、Web等相关开发技术,设计并搭建大气环境质量要素的数据远程监测与管理系统。通过在小区域进行部署实验发现,系统能够长时间稳定运行,且监测数据结果稳定可靠,可为城市大气环境相关研究提供充足的数据来源,为城市环境治理提供一定参考。

基于视频AI的互联网数据中心机房智慧管理技术研究

人工智能正在重塑电信市场的竞争环境和盈利模式,电信运营商急需战略转型,通过优化价值链争取竞争优势以赶上数字经济发展的战略机遇。本文使用文献研究法和案例研究法研究了中国电信的互联网数据中心使用视频人工智能进行智慧化管理的情况,发现虚拟价值链逐步超越传统价值链,得出对人工智能技术的恰当运用有助于电信企业长远发展的结论,可供电信运营商在智能数字时代发展相关业务时参考。

基于激光测距仪的智能物联网体感交互感知研究

当前的智能物联网设备在识别用户指令方面存在不足,时常需要用户多次尝试,降低了用户的使用体验和对设备的信任度。为了解决问题,提出了一种基于激光测距仪的智能物联网体感交互感知方法。首先,拟合激光波绘制结果,并利用距离变换算法设定智能物联网的体感交互方式。然后,通过误差传递公式,计算激光测距仪的协方差矩阵,依据此调整参数和测量点。最后,融合激光图像特征,识别操作者的动态指令。实验结果表明,所提方法能够显著提升智能物联网设备对体感指令的采集完整度,正确完成所有体感指令,且平均体感动作类别划分精准度高达97.02%,说明所提方法具有较高的体感交互感知精度。

设施温室物联网智能测控系统研究

针对温室环境调控中手动控制和阈值条件调控存在调控精度低、参数超调严重等问题,以设施番茄温室为例,研究提出一种基于环境预测的精准调控策略。首先,构建专用物联网“六域”架构,采用MSP430F5438A作为主控模块,设计感知、通信等功能模块。其次,构建SSA-LSTM预测模型实现对温室环境的精准预测,并根据模型预测结果确定环境调控策略,通过PSO-PID控制模型实现对温室风口电机的精准控制。实验结果表明,相较于传统LSTM模型,SSA-LSTM预测模型的MAE降低58.52%,MAPE降低61.68%,RMSE降低63.84%。同时,相较于传统PID模型,PSO-PID控制模型的超调量降低89.99%,调节时间降低59.85%。系统经过实地部署验证,在保持种植品种和农事管理操作一致的情形下,智能调控的温室产量相较于传统温室提升约8.5%,证明了系统的有效实用性。

Internet模型与网络遥操作系统仿真环境的建立

通过对Internet进行不同地点、时间段和数据包大小的传输时延统计 ,得到了网络传输时延近似符合正态、指数分布或对数正态分布的统计规律。根据Internet的统计模型建立了以Internet仿真器为基础的网络遥操作系统仿真环境。Internet仿真器提供统计模型的三种分布和与实测数据相同分布的时延 。