Biblio

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management.

One of the most challenging issues facing Internet of Medical Things (IoMT) cyber defense is the complexity of their ecosystem coupled with the development of cyber-attacks. Medical equipments lack built-in security and are increasingly becoming connected. Moving beyond traditional security solutions becomes a necessity to protect patients and organizations. In order to effectively deal with the security risks of networked medical devices in such a complex and heterogeneous system, we need to measure security risks and prioritize mitigation actions. In this context, we propose a Fuzzy AHP-based method to assess security attributes of connected medical devices and compare different device models against a selected profile with regards to the user requirements. The proposal aims to empower user security awareness to make well-educated decisions.

Analyzing reflectionally symmetric features inside an image is one of the important processes for recognizing the peculiar appearance of natural and man-made objects, biological patterns, etc. In this work, we will point out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized \textbackslashmathcalR\_fm-transform model. To this end, we will firstly prove the \textbackslashmathcalR\_fmˆ-transform in accordance with reflectional symmetry detection. Then different corresponding \textbackslashmathcalR\_fm-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the \textbackslashmathcalR\_fm-transform is the best for the detection. Experimental results of detecting on single/compound contour-based shapes have validated that the exponentiation of 10 is the most discriminatory, with over 2.7% better performance on the multiple-axis shapes in comparison with the conventional one. Additionally, the proposed detector also outperforms most of other existing methods. This finding should be recommended for applications in practice.

False news has become widespread in the last decade in political, economic, and social dimensions. This has been aided by the deep entrenchment of social media networking in these dimensions. Facebook and Twitter have been known to influence the behavior of people significantly. People rely on news/information posted on their favorite social media sites to make purchase decisions. Also, news posted on mainstream and social media platforms has a significant impact on a particular country’s economic stability and social tranquility. Therefore, there is a need to develop a deceptive system that evaluates the news to avoid the repercussions resulting from the rapid dispersion of fake news on social media platforms and other online platforms. To achieve this, the proposed system uses the preprocessing stage results to assign specific vectors to words. Each vector assigned to a word represents an intrinsic characteristic of the word. The resulting word vectors are then applied to RNN models before proceeding to the LSTM model. The output of the LSTM is used to determine whether the news article/piece is fake or otherwise.

A simulation-based optimization framework is developed to con-currently design the system and control parameters to meet de-sired performance and operational resiliency objectives. Leveraging system information from both data and models of varying fideli-ties, a rigorous probabilistic approach is employed for co-design experimentation. Significant economic benefits and resilience im-provements are demonstrated using co-design compared to existing sequential designs for cyber-physical systems.

Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.

Intelligent, smart, Cloud, reconfigurable manufac-turing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustain-able factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Light-weight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big Data streams, (iii) produce and persist Big Datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big Data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraintsƒ The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.

The Internet of Things (IoT) is advancing technology by creating smart surroundings that make it easier for humans to do their work. This technological advancement not only improves human life and expands economic opportunities, but also allows intruders or attackers to discover and exploit numerous methods in order to circumvent the security of IoT networks. Hence, security and privacy are the key concerns to the IoT networks. It is vital to protect computer and IoT networks from many sorts of anomalies and attacks. Traditional intrusion detection systems (IDS) collect and employ large amounts of data with irrelevant and inappropriate attributes to train machine learning models, resulting in long detection times and a high rate of misclassification. This research presents an advance approach for the design of IDS for IoT networks based on the Particle Swarm Optimization Algorithm (PSO) for feature selection and the Extreme Gradient Boosting (XGB) model for PSO fitness function. The classifier utilized in the intrusion detection process is Random Forest (RF). The IoTID20 is being utilized to evaluate the efficacy and robustness of our suggested strategy. The proposed system attains the following level of accuracy on the IoTID20 dataset for different levels of classification: Binary classification 98 %, multiclass classification 83 %. The results indicate that the proposed framework effectively detects cyber threats and improves the security of IoT networks.

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.

Document-level relation extraction (RE) aims to extract relations among entities within a document, which is more complex than its sentence-level counterpart, especially in biomedical text mining. Chemical-disease relation (CDR) extraction aims to extract complex semantic relationships between chemicals and diseases entities in documents. In order to identify the relations within and across multiple sentences at the same time, existing methods try to build different document-level heterogeneous graph. However, the entity relation representations captured by these models do not make full use of the document information and disregard the noise introduced in the process of integrating various information. In this paper, we propose a novel model DAM-GAN to document-level biomedical RE, which can extract entity-level and mention-level representations of relation instances with R-GCN and Dual-Attention Multi-Instance Learning (DAM) respectively, and eliminate the noise with Generative Adversarial Network (GAN). Entity-level representations of relation instances model the semantic information of all entity pairs from the perspective of the whole document, while the mention-level representations from the perspective of mention pairs related to these entity pairs in different sentences. Therefore, entity- and mention-level representations can be better integrated to represent relation instances. Experimental results demonstrate that our model achieves superior performance on public document-level biomedical RE dataset BioCreative V Chemical Disease Relation(CDR).

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, it is crucial to ensure IoT devices follow strict security requirements. At the same time, the prevalence of IoT devices offers developers a chance to design and develop Machine Learning (ML)-based intelligent software systems using their IoT devices. However, given the diversity of IoT devices, IoT developers may find it challenging to introduce appropriate security and ML techniques into their devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers’ security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. First, we collect around 53K IoT posts (questions + accepted answers) from SO. Second, we tokenize each post into sentences. Third, we automatically identify sentences containing security and ML-related discussions. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML-related discussions. There is no overlap between security and ML-related discussions, i.e., IoT developers discussing security requirements did not discuss ML requirements and vice versa. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.

Mobile healthcare (mHealth) application and technologies have promised their cost-effectiveness to enhance healthcare quality, particularly in rural areas. However, the increased security incidents and leakage of patient data raise the concerns to address security risks and privacy issues of mhealth applications urgently. While recent mobile health applications that rely on password-based authentication cannot withstand password guessing and cracking attacks, several countermeasures such as One-Time Password (OTP), grid-based password, and biometric authentication have recently been implemented to protect mobile health applications. These countermeasures, however, can be thwarted by brute force attacks, man-in-the-middle attacks and persistent malware attacks. This paper proposed grid-based honey encryption by hybridising honey encryption with grid-based authentication. Compared to recent honey encryption limited in the hardening password attacks process, the proposed grid-based honey encryption can be further employed against shoulder surfing, smudge and replay attacks. Instead of rejecting access as a recent security defence mechanism in mobile healthcare applications, the proposed Grid-based Honey Encryption creates an indistinct counterfeit patient's record closely resembling the real patients' records in light of each off-base speculation legitimate password.

This paper considers methods of fingerprint protection in biometric authentication systems. Including methods of protecting fingerprint templates using zero digital watermarks and cryptography techniques. The paper considers a secure authentication model using cryptography and digital watermarks.

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.

In this study, a novel decentralized authentication model is proposed for establishing a secure communications structure in VoIP applications. The proposed scheme considers a distributed architecture called the blockchain. With this scheme, we highlight the multimedia data is more resistant to some of the potential attacks according to the centralized architecture. Our scheme presents the overall system authentication architecture, and it is suitable for mutual authentication in terms of privacy and anonymity. We construct an ECC-based model in the encryption infrastructure because our structure is time-constrained during communications. This study differs from prior work in that blockchain platforms with ECC-Based Biometric Signature. We generate a biometric key for creating a unique ID value with ECC to verify the caller and device authentication together in blockchain. We validated the proposed model by comparing with the existing method in VoIP application used centralized architecture.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.

In this paper, we present a theoretical approach concerning the econometric modelling for the estimation of cyber-security risk, with the use of time-series analysis methods and alternatively with Machine Learning (ML) based, deep learning methodology. Also we present work performed in the framework of SAINT H2020 Project [1], concerning innovative data mining techniques, based on automated web scrapping, for the retrieving of the relevant time-series data. We conclude with a review of emerging challenges in cyber-risk assessment brought by the rapid development of adversarial AI.

Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.

Cloud Computing is a technology which provides flexibility through scalability. Like, Cloud computing, nowadays, Fog computing is considered more revolutionary and dynamic technology. But the main problem with the Fog computing is to take care of its security as in this also person identification is done by single Sign-In system. To come out from the security problem raised in Fog computing, an innovative approach has been suggested here. In the present paper, an approach has been proposed that combines different biometric techniques to verify the authenticity of a person and provides a complete model that will be able to provide a necessary level of verification and security in fog computing. In this model, several biometric techniques have been used and each one of them individually helps extract out more authentic and detailed information after every step. Further, in the presented paper, different techniques and methodologies have been examined to assess the usefulness of proposed technology in reducing the security threats. The paper delivers a capacious technique for biometric authentication for bolstering the fog security.

Generalizing automated medical image segmentation methods to new image domains is inherently difficult. We have previously developed a number of automated segmentation methods that perform at the level of human readers on images acquired under similar conditions to the original training data. We are interested in exploring techniques that will improve model generalization to new imaging domains. In this study we explore a method to limit the inherent bias of these models to intensity and textural information. Using a dataset of 100 T2-weighted MR images with fat-saturation, and 100 T2-weighted MR images without fat-saturation, we explore the use of neural style transfer to induce shape preference and improve model performance on the task of segmenting the kidneys in patients affected by polycystic kidney disease. We find that using neural style transfer images improves the average dice value by 0.2. In addition, visualizing individual network kernel responses highlights a drastic difference in the optimized networks. Biasing models to invoke shape preference is a promising approach to create methods that are more closely aligned with human perception.

Both the sophistication and scale of cyberattacks are increasing, revealing the extent of risks at which critical infrastructure and other information and communication systems are exposed. Furthermore, the introduction of IoT devices in a number of different applications, ranging from home automation to the monitoring of critical infrastructure, has created an even more complicated cybersecurity landscape. A large amount of research has been done on detecting these attacks in real time, however mitigation is left to security experts, which is time consuming and may have economic consequences. In addition, there is no public data available for action selection that could enable the use of the latest techniques in machine learning or deep learning for this area. Currently, most systems deploy a rule-based response selection methodology for mitigating detected attacks. In this paper, we introduce a situation calculus-based approach to automated response for IoT cyberattacks. The approach offers explicit semantic-rich cognitive modeling of attacks, effects and actions and supports situation inference for timely and accurate responses. We demonstrate the effectiveness of our approach for modelling and responding to cyberattacks by implementing a use case in a real-world IoT scenario.