Intelligent Intrusion Detection System for Industrial Internet of Things Environment

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 Optimization 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 complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates 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 promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.     

Sintering and mechanical properties of tricalcium phosphate–fluorapatite composites

Tricalcium phosphate and synthesized fluorapatite powder were mixed in order to elaborate biphasic ceramics composites. The effect of fluorapatite addition on the densification and the mechanical properties of tricalcium phosphate were measured with the change in composition and microstructure of the bioceramic. The Brazilian test was used to measure the mechanical resistance of the tricalcium phosphate–26.52 wt% fluorapatite composites. The densification and rupture strength increase versus sintering temperature. The composites have a good sinterability and rupture strength in temperature ranging between 1300 and 1400 °C. Thus, the densification ultimate was obtained at 1350 °C and the mechanical resistance optimum reached 9.6 MPa at 1400 °C. Above 1400 °C, the densification and the mechanical properties were hindered by the allotropic transformation of tricalcium phosphate, grain growth and the formation of both intragranular porosity and many cracks. The ³¹P magic angle spinning nuclear magnetic resonance analysis of composites reveals the presence of tetrahedral P sites.     

Physico-Chemical Characterizations of Poly(vinylidene fluoride)/Cu3(BTC)2 Composite Membranes Prepared by In Situ Crystal Growth

In this work, we present a simple and fast method for elaborating hybrid membranes by growing metal–organic framework crystals inside a polymer solution. The solution thus obtained was casted then annealed at 90°C for 5 h. This method was tested with poly(vinylidene fluoride) (PVDF) as a piezoelectric polymer and the Cu₃(BTC)₂, BTC = 1,3,5-benzene tricarboxylate, as a filler. The characterization of the obtained membranes by attenuated total reflectance Fourier transform infrared spectroscopy and X-ray diffraction showed the presence of the characteristic signatures of Cu₃(BTC)₂ and the β-phase of PVDF. Moreover, scanning electron microscopy images reveal that the Cu₃(BTC)₂ crystallites have grown along the PVDF membranes. The effect of the filler on both thermal and mechanical properties of the membranes was also studied. POLYM. ENG. SCI., 60:464–473, 2020. © 2019 Society of Plastics Engineers     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.     

Detection of Virulence Genes and Growth Potential in Listeria monocytogenes Strains Isolated from Ricotta Salata Cheese

Ricotta Salata is a traditional ripened and salted whey cheese made in Sardinia (Italy) from sheep's milk. This product is catalogued as ready‐to‐eat food (RTE) since it is not submitted to any further treatment before consumption. Thus, foodborne pathogens, such as Listeria monocytogenes, can represent a health risk for consumers. In September 2012, the FDA ordered the recall of several batches of Ricotta Salata imported from Italy linked to 22 cases of Listeriosis in the United States. This study was aimed at evaluating the presence and virulence properties of L. monocytogenes in 87 samples of Ricotta Salata produced in Sardinia. The ability of this product to support its growth under foreseen packing and storing conditions was also evaluated in 252 samples. Of the 87 samples 17.2% were positive for the presence of L. monocytogenes with an average concentration of 2.2 log₁₀ cfu/g. All virulence‐associated genes (prfA, rrn, hlyA, actA, inlA, inlB, iap, plcA, and plcB) were detected in only one isolated strain. The Ricotta Salata samples were artificially inoculated and growth potential (δ) was assessed over a period of 3 mo. The value of the growth potential was always >0.5 log₁₀ cfu/g under foreseen packing and storing conditions. This study indicates that Ricotta Salata supports the L. monocytogenes growth to levels that may present a serious risk to public health, even while stored at refrigeration temperatures.     

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication, integrity verification, and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology. In this paper, a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking (SFASCDW) is proposed for content authentication and tampering detection of English text. A first-level order of alphanumeric mechanism, based on hidden Markov model, is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach. The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text. Moreover, he extracts the features of the interrelationship among the contexts of the text, utilizes the extracted features as watermark information, and validates it later with the studied English text to detect any tampering. SFASCDW has been implemented using PHP with VS code IDE. The robustness, effectiveness, and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks, namely insertion, reorder, and deletion. The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.     

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.     

先进的数码涡旋制冷压缩机技术

介绍了空调系统中的压缩机几种能量调节技术:多级控制系统、变频技术和数码涡旋技术。详细地论述了数码涡旋制冷压缩机优越的特性。     

汽车空调一些关键技术发展的展望

本文主要就汽车空调制冷剂、汽车空调压缩机的发展进行了研究分析,并对其发展前景进行了展望.