Biblio

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.

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.

Nowadays, the MOBA game is the game type with the most audiences and players around the world. Recently, the League of Legends has become an official sport as an e-sport among 37 events in the 2022 Asia Games held in Hangzhou. As the development in the e-sport, analytical skills are also involved in this field. The topic of this research is to use the machine learning approach to analyze the data of the League of Legends and make a prediction about the result of the game. In this research, the method of machine learning is applied to the dataset which records the first 10 minutes in diamond-ranked games. Several popular machine learning (AdaBoost, GradientBoost, RandomForest, ExtraTree, SVM, Naïve Bayes, KNN, LogisticRegression, and DecisionTree) are applied to test the performance by cross-validation. Then several algorithms that outperform others are selected to make a voting classifier to predict the game result. The accuracy of the voting classifier is 72.68%.

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.

The rapidly increasing malware threats must be coped with new effective malware detection methodologies. Current malware threats are not limited to daily personal transactions but dowelled deeply within large enterprises and organizations. This paper introduces a new methodology for detecting and discriminating malicious versus normal applications. In this paper, we employed Ant-colony optimization to generate two behavioural graphs that characterize the difference in the execution behavior between malware and normal applications. Our proposed approach relied on the API call sequence generated when an application is executed. We used the API calls as one of the most widely used malware dynamic analysis features. Our proposed method showed distinctive behavioral differences between malicious and non-malicious applications. Our experimental results showed a comparative performance compared to other machine learning methods. Therefore, we can employ our method as an efficient technique in capturing malicious applications.

With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84% and 97.83%, verifying the effectiveness of our proposed method.

Internet of Vehicles consists of a three-layer architecture of electric vehicles, charging piles, and a grid dispatch management control center. Therefore, V2G presents multi-level, multi-agent and frequent information interaction, which requires a highly secure and lightweight identity authentication method. Based on the characteristics of Internet of Vehicles, this paper designs a multi-subject information interaction and one-way hash chain authentication method, it includes one-way hash chain and key distribution update strategy. The operation experiment of multiple electric vehicles and charging piles shows that the algorithm proposed in this paper can meet the V2G ID authentication requirements of Internet of Vehicles, and has the advantages of lightweight and low consumption. It is of great significance to improve the security protection level of Internet of Vehicles V2G.

A multi-designated receiver authentication code (MDRA-code) with information-theoretic security is proposed as an extension of the traditional multi-receiver authentication code. The purpose of the MDRA-code is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.

With the increasing number of distributed energy sources and the growing demand for free exchange of energy, Energy internet (EI) is confronted with great challenges of persistent connection, stable transmission, real-time interaction, and security. The new definition of metaverse in the EI field is proposed as a potential solution for these challenges by establishing a massive and comprehensive fusion 3D network, which can be considered as the advanced stage of EI. The main characteristics of the metaverse such as reality to virtualization, interaction, persistence, and immersion are introduced. Specifically, we present the key enabling technologies of the metaverse including virtual reality, artificial intelligence, blockchain, and digital twin. Meanwhile, the potential applications are presented from the perspectives of immersive user experience, virtual power station, management, energy trading, new business, device maintenance. Finally, some challenges of metaverse in EI are concluded.

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.

In this paper, a new model-free adaptive sliding mode load frequency control (LFC) scheme is designed for inter-connected power systems, where modeling is difficult and suffers from load change disturbances and denial of service (DoS) attacks. The proposed algorithm only uses real-time I/O data of the power system to achieve a high control performance. Firstly, the dynamic linearization strategy is used to build a data-based model of the power system, and intermittent DoS attacks are modeled by limiting their duration and frequency. Secondly, the model-free adaptive sliding mode control (MFASMC) scheme is designed based on optimization theory and sliding mode reaching law, and its stability is analyzed. Finally, the three-area interconnected power system was selected to test the presented MFASMC scheme. Simulation data shows the effectiveness of the LFC algorithm in this paper.

The development of industrial automation tools and the integration of industrial and corporate networks in order to improve the quality of production management have led to an increase in the risks of successful cyberattacks and, as a result, to the necessity to solve the problems of practical information security of industrial control systems (ICS). Detection of cyberattacks of both known and unknown types is could be implemented as anomaly detection in dynamic information processes recorded during the operation of ICS. Anomaly detection methods do not require preliminary analysis and labeling of the training sample. In the context of detecting attacks on ICS, cluster analysis is used as one of the methods that implement anomaly detection. The application of hierarchical cluster analysis for clustering data of ICS information processes exposed to various cyberattacks is studied, the problem of choosing the level of the cluster hierarchy corresponding to the minimum set of clusters aggregating separately normal and abnormal data is solved. It is shown that the Ward method of hierarchical cluster division produces the best division into clusters. The next stage of the study involves solving the problem of classifying the formed minimum set of clusters, that is, determining which cluster is normal and which cluster is abnormal.

The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.

We show that the most common flavors of noisy leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to a small statistical simulation error and a slight loss in the leakage parameter. The latter holds true in particular for one of the most used noisy-leakage models, where the noisiness is measured using the conditional average min-entropy (Naor and Segev, CRYPTO’09 and SICOMP’12). Our reductions between noisy and bounded leakage are achieved in two steps. First, we put forward a new leakage model (dubbed the dense leakage model) and prove that dense leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to small statistical distance. Second, we show that the most common noisy-leakage models fall within the class of dense leakage, with good parameters. Third, we prove lower bounds on the amount of bounded leakage required for simulation with sub-constant error, showing that our reductions are nearly optimal. In particular, our results imply that useful general simulation of noisy leakage based on statistical distance and mutual information is impossible. We also provide a complete picture of the relationships between different noisy-leakage models. Our result finds applications to leakage-resilient cryptography, where we are often able to lift security in the presence of bounded leakage to security in the presence of noisy leakage, both in the information-theoretic and in the computational setting. Remarkably, this lifting procedure makes only black-box use of the underlying schemes. Additionally, we show how to use lower bounds in communication complexity to prove that bounded-collusion protocols (Kumar, Meka, and Sahai, FOCS’19) for certain functions do not only require long transcripts, but also necessarily need to reveal enough information about the inputs.

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset.

Mortality from lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. Many existing detection methods on lung nodules can achieve high sensitivity but meanwhile introduce an excessive number of false-positive proposals, which is clinically unpractical. In this paper, we propose the multi-head detection and spatial attention network, shortly MHSnet, to address this crucial false-positive issue. Specifically, we first introduce multi-head detectors and skip connections to capture multi-scale features so as to customize for the variety of nodules in sizes, shapes, and types. Then, inspired by how experienced clinicians screen CT images, we implemented a spatial attention module to enable the network to focus on different regions, which can successfully distinguish nodules from noisy tissues. Finally, we designed a lightweight but effective false-positive reduction module to cut down the number of false-positive proposals, without any constraints on the front network. Compared with the state-of-the-art models, our extensive experimental results show the superiority of this MHSnet not only in the average FROC but also in the false discovery rate (2.64% improvement for the average FROC, 6.39% decrease for the false discovery rate). The false-positive reduction module takes a further step to decrease the false discovery rate by 14.29%, indicating its very promising utility of reducing distracted proposals for the downstream tasks relied on detection results.

Data security is the process of protecting data from loss, alteration, or unauthorised access during its entire lifecycle. It includes everything from the policies and practices of a company to the hardware, software, storage, and user devices used by that company. Data security tools and technology increase transparency into an organization's data and its usage. These tools can protect data by employing methods including encryption and data masking personally identifiable information.. Additionally, the method aids businesses in streamlining their auditing operations and adhering to the increasingly strict data protection rules.

The development of cloud apps enables people to exchange resources, goods, and expertise online with other clients. The material is more vulnerable to numerous security dangers from outsiders due to the fact that millions of users exchange data through the same system. How to maintain the security of this data is now the main concern. The current data protection system functions best when it places a greater priority on safeguarding data maintained in online storage than it does on cybersecurity during transportation. The data becomes open to intrusion attacks while being transferred. Additionally, the present craze states that an outside auditor may view data as it is being transmitted. Additionally, by allowing the hacker to assume a third-person identity while obtaining the information, this makes the data more susceptible to exploitation. The proposed system focuses on using encryption to safeguard information flow since cybersecurity is seen as a major issue. The approach also takes into account the fourth auditing issue, which is that under the recommended manner, the inspector is not allowed to see the user information. Tests have shown that the recommended technique improves security overall by making it harder for hackers to decode the supplied data.

The security of embedded systems is particularly crucial given the prevalence of embedded devices in daily life, business, and national defense. Firmware for embedded systems poses a serious threat to the safety of society, business, and the nation because of its robust concealment, difficulty in detection, and extended maintenance cycle. This technology is now an essential part of the contemporary experience, be it in the smart office, smart restaurant, smart home, or even the smart traffic system. Despite the fact that these systems are often fairly effective, the rapid expansion of embedded systems in smart cities have led to inconsistencies and misalignments between secured and unsecured systems, necessitating the development of secure, hacker-proof embedded systems. To solve this issue, we created a sizable, original, and objective dataset that is based on the latest Linux vulnerabilities for identifying the embedded system vulnerabilities and we modified a cutting-edge machine learning model for the Linux Kernel. The paper provides an updated EVDD and analysis of an extensive dataset for embedded system based vulnerability detection and also an updated state of the art deep learning model for embedded system vulnerability detection. We kept our dataset available for all researchers for future experiments and implementation.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values.

With the rapid development of cloud storage technology, an increasing number of enterprises and users choose to store data in the cloud, which can reduce the local overhead and ensure safe storage, sharing, and deletion. In cloud storage, safe data deletion is a critical and challenging problem. This paper proposes an assured data deletion scheme based on multi-authoritative users in the semi-trusted cloud storage scenario (MAU-AD), which aims to realize the secure management of the key without introducing any trusted third party and achieve assured deletion of cloud data. MAU-AD uses access policy graphs to achieve fine-grained access control and data sharing. Besides, the data security is guaranteed by mutual restriction between authoritative users, and the system robustness is improved by multiple authoritative users jointly managing keys. In addition, the traceability of misconduct in the system can be realized by blockchain technology. Through simulation experiments and comparison with related schemes, MAU-AD is proven safe and effective, and it provides a novel application scenario for the assured deletion of cloud storage data.

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.

As cyber threats become highly damaging and complex, a new cybersecurity compliance certification model has been developed by the Department of Defense (DoD) to secure its Defense Industrial Base (DIB), and communication with its private partners. These partners or contractors are obligated by the Defense Federal Acquisition Regulations (DFARS) to be compliant with the latest standards in computer and data security. The Cybersecurity Maturity Model Certification (CMMC), and it is built upon existing DFARS 252.204-7012 and the NIST SP 800–171 controls. As of 2020, the DoD has incorporated DFARS and the National Institute of Standards and Technology (NIST) recommended security practices into what is now the CMMC. This paper presents the most commonly identified Security-Control-Deficiencies (SCD) faced, the attacks mitigated by addressing these SCD, and remediations applied to 127 DoD contractors in order to bring them into compliance with the CMMC guidelines. An analysis is done on what vulnerabilities are most prominent in the companies, and remediations applied to ensure these vulnerabilities are better avoided and the DoD supply-chain is more secure from attacks.