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| 1 | Intelligent Intrusion Detection System for Industrial Internet of Things Environment显示文摘Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques. | R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani Nadhem Nemri Tarek Lamoudan | 2023 | Computer Systems Science & Engineering2023，44，2： | 1 |
| 2 | 利用高分辨同步辐射衍射研究氨苄青霉素三水合物的电荷密度分布:密度泛函理论和多极模型之间的相关性显示文摘实验研究了氨苄青霉素三水合物的电荷密度分布,并与用密度泛函理论的量子计算结果进行比较.计算了电荷导出性质,Mulliken原子电荷,偶极矩和分子静电势.另外用多极分析对实验总体参数的进行细化.用多极处理获得的结构因子构建了傅立叶图.同时讨论电荷分布的拓扑性质,分析了(3,-1)临界点的特性. | C. Anzline S. lsrael R. Niranjana Devi R. A. J, R.Sheeba P. Richard Rajkumar | 2017 | Chinese Journal of Chemical Physics2017，30，1： | 0 |
| 3 | Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model显示文摘Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches. | R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.Malik | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
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