Biblio

In this study, the parameters of the Jiles-Atherton (JA) model are investigated to determine suitable solution candidates for hysteresis models of a piezoelectric actuator (PEA). The methodology of this study is to perform Monte Carlo experiments on the JA model by randomly selecting parameters that generate hysteresis curves. The solution space is then restrained such that their normalized area and remnant displacements are comparable to those of the PEA. The data resulting from these Monte Carlo simulations show trends in the parameter space that can be used to further restrain parameter selection windows to find suitable JA parameters to model PEAs. In particular, the results show that selection of the reversibility coefficient and the pinning factor strongly affect both of the hysteresis characteristics studied. A large density of solutions is found in certain parameter distributions for both the area and the remnant displacement, but the remnant displacement generates the densest distributions. These results can be used to more effectively find suitable hysteresis models for modeling purposes.

In this paper, we proposed a data security model of a big data analytical environment in the financial sector. Big Data can be seen as a trend in the advancement of technology that has opened the door to a new approach to understanding and decision making that is used to describe the vast amount of data (structured, unstructured and semi-structured) that is too time consuming and costly to load a relational database for analysis. The increase in cybercriminal attacks on an organization’s assets results in organizations beginning to invest in and care more about their cybersecurity points and controls. The management of business-critical data is an important point for which robust cybersecurity controls should be considered. The proposed model is applied in a datalake and allows the identification of security gaps on an analytical repository, a cybersecurity risk analysis, design of security components and an assessment of inherent risks on high criticality data in a repository of a regulated financial institution. The proposal was validated in financial entities in Lima, Peru. Proofs of concept of the model were carried out to measure the level of maturity focused on: leadership and commitment, risk management, protection control, event detection and risk management. Preliminary results allowed placing the entities in level 3 of the model, knowing their greatest weaknesses, strengths and how these can affect the fulfillment of business objectives.

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

Network security is a problem that is of great concern to all countries at this stage. How to ensure that the network provides effective services to people without being exposed to potential security threats has become a major concern for network security researchers. In order to better understand the network security situation, researchers have studied a variety of quantitative assessment methods, and the most scientific and effective one is the hierarchical quantitative assessment method of the network security threat situation. This method allows the staff to have a very clear understanding of the security of the network system and make correct judgments. This article mainly analyzes the quantitative assessment of the hierarchical network security threat situation from the current situation and methods, which is only for reference.

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.

The aim of this paper is to examine noteworthy cyberattacks that have taken place against ICS and SCADA systems and to analyse them. This paper also proposes a new classification scheme based on the severity of the attack. Since the information revolution, computers and associated technologies have impacted almost all aspects of daily life, and this is especially true of the industrial sector where one of the leading trends is that of automation. This widespread proliferation of computers and computer networks has also made it easier for malicious actors to gain access to these systems and networks and carry out harmful activities.

Using multi-UAV systems to accomplish both civil and military missions is becoming a popular trend. With the development of software and hardware technologies, Unmanned aerial vehicles (UAVs) are now able to operate autonomously at edge. However, the remote control of manned systems, e.g., ground control station (GCS), remains essential to mission success, and the system's control and non-payload communication (CNPC) are facing severe cyber threats caused by smart attacks. To avoid hijacking, in this paper, we propose a secure mechanism that reduces such security risks for multi-UAV systems. We introduce friendly jamming from UAVs to block eavesdropping on the remote control channel. The trade-off between security and energy consumption is optimized by three approaches designed for UAV and GCS under algorithms of different complexities. Numerical results show the approach efficiency under different mission conditions and security demands, and demonstrate the features of the proposed mechanism for various scenarios.

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.

In today’s fast pacing world, cybercrimes have time and again proved to be one of the biggest hindrances in national development. According to recent trends, most of the times the victim’s data is breached by trapping it in a phishing attack. Security and privacy of user’s data has become a matter of tremendous concern. In order to address this problem and to protect the naive user’s data, a tool which may help to identify whether a window executable is malicious or not by doing static analysis on it has been proposed. As well as a comparative study has been performed by implementing different classification models like Logistic Regression, Neural Network, SVM. The static analysis approach used takes into parameters of the executables, analysis of properties obtained from PE Section Headers i.e. API calls. Comparing different model will provide the best model to be used for static malware analysis

Smart city management is going through a remarkable transition, in terms of quality and diversity of services provided to the end-users. The stakeholders that deliver pervasive applications are now able to address fundamental challenges in the big data value chain, from data acquisition, data analysis and processing, data storage and curation, and data visualisation in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and data, also for the smart cities sector where humans, sensors and devices are operating in strict collaboration. The data produced by the ubiquitous devices must be processed quickly to allow the implementation of reactive services such as situational awareness, video surveillance and geo-localization, while always ensuring the safety and privacy of involved citizens. This paper proposes a modular architecture to (i) leverage innovative technologies for data acquisition, management and distribution (such as Apache Kafka and Apache NiFi), (ii) develop a multi-layer engineering solution for revealing valuable and hidden societal knowledge in smart cities environment, and (iii) tackle the main issues in tasks involving complex data flows and provide general guidelines to solve them. We derived some guidelines from an experimental setting performed together with leading industrial technical departments to accomplish an efficient system for monitoring and servitization of smart city assets, with a scalable platform that confirms its usefulness in numerous smart city use cases with different needs.

he growing trend towards network “softwarization” allows the creation and deployment of even complex network environments in a few minutes or seconds, rather than days or weeks as required by traditional methods. This revolutionary approach made it necessary to seek automatic processes to solve network security problems. One of the main issues in the automation of network security concerns the proper and efficient modeling of network traffic. In this paper, we describe two optimized Traffic Flows representation models, called Atomic Flows and Maximal Flows. In addition to the description, we have validated and evaluated the proposed models to solve two key network security problems - security verification and automatic configuration - showing the advantages and limitations of each solution.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

Vehicular crowdsensing (VCS) is a subset of crowd-sensing where data collection is outsourced to group vehicles. Here, an entity interested in collecting data from a set of Places of Sensing Interest (PsI), advertises a set of sensing tasks, and the associated rewards. Vehicles attracted by the offered rewards deviate from their ongoing trajectories to visit and collect from one or more PsI. In this win-to-win scenario, vehicles reach their final destination with the extra reward, and the entity obtains the desired samples. Unfortunately, the efficiency of VCS can be undermined by the Sybil attack, in which an attacker can benefit from the injection of false vehicle identities. In this paper, we present a case study and analyze the effects of such an attack. We also propose a defense mechanism based on generative adversarial neural networks (GANs). We discuss GANs' advantages, and drawbacks in the context of VCS, and new trends in GANs' training that make them suitable for VCS.

Cyberspace is the fifth largest activity space after land, sea, air and space. Safeguarding Cyberspace Security is a major issue related to national security, national sovereignty and the legitimate rights and interests of the people. With the rapid development of artificial intelligence technology and its application in various fields, cyberspace security is facing new challenges. How to help the network security personnel grasp the security trend at any time, help the network security monitoring personnel respond to the alarm information quickly, and facilitate the tracking and processing of the monitoring personnel. This paper introduces a method of using situational awareness micro application actual combat attack and defense robot to quickly feed back the network attack information to the monitoring personnel, timely report the attack information to the information reporting platform and automatically block the malicious IP.

Edge computing is a prospective notion for expanding the potential of cloud computing. It is vital to maintaining a decent atmosphere free of all forms of security and breaches in order to continue utilizing computer services. The security concerns surrounding the edge computing environment has been impeded as a result of the security issues that surround the area. Many researchers have looked into edge computing security issues, however, not all have thoroughly studied the needs. Security requirements are the goals that specify the capabilities and operations that a process that is carried out by a system in order to eliminate various security flaws. The purpose of this study is to give a complete overview of the many different artificial intelligence technologies that are now being utilized for edge computing security with the intention of aiding research in the future in locating research potential. This article analyzed the most recent research and shed light on the following topics: state-of-the-art techniques used to combat security threats, technological trends used by the method, metrics utilize to assess the techniques' ability, and opportunities of research for future researchers in the area of artificial intelligence for edge computing security.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

A wireless communication system with rotating linearly polarized antennas is built and tested as a method for increasing physical layer security. Controlling the linear polarization angle from 0° to 180° yields bit error rates greater than 20% for 40° of rotation.

Zero-day attack is a critical network attack. The zero-day attack period (ZDAP) is the period from the release of malware/exploit until a patch becomes available. IDS/IPS cannot effectively block zero-day attacks because they use pattern-based signatures in general. This paper proposes a Prophetic Defender (PD) by which ZDAP can be minimized. Prior to actual attack, hackers scan networks to identify hosts with vulnerable ports. If this port scanning can be detected early, zero-day attacks will become detectable. PD architecture makes use of a honeypot-based pseudo server deployed to detect malicious port scans. A port-scanning honeypot was operated by us in 6 years from 2009 to 2015. By analyzing the 6-year port-scanning log data, we understand that PD is effective for detecting and blocking zero-day attacks. The block rate of the proposed architecture is 98.5%.

In the era of Big Data, opportunities and challenges are mixed. The data transfer is increasingly frequent and speedy, and the data lifecycle is also extended, bringing more challenges to security and privacy risk governance. Currently, the common measures of risk governance covering the entire data life cycle are the data-related staff management, equipment security management, data encryption codes, data content identification and de-identification processing, etc. With the trend of data globalization, regulations fragmentation and governance technologization, “International standards”, a measure of governance combining technology and regulation, has the potential to become the best practice. However, “voluntary compliance” of international standards derogates the effectiveness of risk governance through this measure. In order to strengthen the enforcement of the international standards, the paper proposes a governance approach which is “the framework regulated by international standards, and regulations and technologies specifically implemented by national legislation.” It aims to implement the security and privacy risk governance of Big Data effectively.

With the advancement in Generative Adversarial Networks (GAN), it has become easier than ever to generate fake images. These images are more realistic and non-discernible by untrained eyes and can be used to propagate fake information on the Internet. In this paper, we propose a novel method to detect GAN generated fake images by using a combination of frequency spectrum of image and deep learning. We apply Discrete Fourier Transform to each of 3 color channels of the image to obtain its frequency spectrum which shows if the image has been upsampled, a common trend in most GANs, and then train a Capsule Network model with it. Conducting experiments on a dataset of almost 1000 images based on Unconditional data modeling (StyleGan2 - ADA) gave results indicating that the model is promising with accuracy over 99% when trained on the state-of-the-art GAN model. In theory, our model should give decent results when trained with one dataset and tested on another.

“Phishing emails” are phishing emails with illegal links that direct users to pages of some real websites that are spoofed, or pages where real HTML has been inserted with dangerous HTML code, so as to deceive users' private information such as bank or credit card account numbers, email account numbers, and passwords. People are the most vulnerable part of security. Phishing emails use human weaknesses to attack. This article describes the application of the principle of persuasion in phishing emails, and based on the existing methods, this paper proposes a phishing email detection method based on the persuasion principle. The principle of persuasion principle is to count whether the corresponding word of the feature appears in the mail. The feature is selected using an information gain algorithm, and finally 25 features are selected for detection. Finally experimentally verified, accuracy rate reached 99.6%.

Phishing is a frequent assault in which unsuspecting people’s unique, private, and sensitive information is stolen through fake websites. The primary objective of phishing websites’consistent resource allocators isto steal unique, private, and sensitive information such as user login passwords and online financial transactions. Phishers construct phony websites that look and sound just like genuine things. With the advent of technology, there are protecting users significantly increased in phishing methods. It necessitates the development of an anti-phishing technology to identify phishing and protect users. Machine learning is a useful technique for combating phishing attempts. These articles were utilized to examine Machine learning for detection strategies and characteristics.

With the high development of Internet technology and the global impacts of Covid-19, a trend of multiple growth is being shown in the business of cross-border e-commerce. The issue of intellectual property rights becomes more obvious in this new mode of trade than in others. China's "14th Five-Year Plan" marked the beginning to implement the strategy of the intellectual property rights for a powerful country. Through the law-and-economics analysis, this paper analyzes the research reports of China's Intellectual Property Court and American Chamber of Commerce, and finds it essential for the cross-border e-commerce to attach great importance to the risk control and protection of property rights. After the analysis and research, on the possible risk of intellectual property rights faced by cross-border e-commerce, it is proposed that enterprises must not only pay attention to but also actively identify and conduct risk warning of the legal risks of their own intellectual property rights as well as the causes of them, so as to put forward corresponding risk control measures and construct prevention and protection mechanisms.

In recent years, the “whole chain” development level of China's intellectual property creation, protection and application has been greatly improved. At the same time, cloud computing technology is booming, and intellectual property data distributed platforms based on cloud storage are emerging one after another. Firstly, this paper introduces the domestic intellectual property cloud platform services from the perspectives of government, state-owned enterprises and private enterprises; Secondly, four typical distributed platforms provided by commercial resources are selected to summarize the problems faced by the operation mode of domestic intellectual property services; Then, it compares and discusses the functions and service modes of domestic intellectual property distributed platform, and takes TSITE IP as an example, puts forward the design and construction strategies of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age. Finally, according to the development of contemporary information technology, this paper puts forward challenges and development direction for the future development of intellectual property platform.

Recently, information leakage accidents have been continuously occurring due to cyberattacks, and internal information leakage has also been occurring additionally. In this situation, many hacking accidents and DDoS attacks related to IoT are reported, and cyber threat detection field is expanding. Therefore, in this study, the trend related to the commercialization and generalization of IoT technology and the degree of standardization of IoT have been analyzed. Based on the reality of IoT analyzed through this process, research and analysis on what points are required in IoT security control was conducted, and then IoT security control strategy was presented. In this strategy, the IoT environment was divided into IoT device, IoT network/communication, and IoT service/platform in line with the basic strategic framework of 'Pre-response-accident response-post-response', and the strategic direction of security control was established suitable for each of them.