Security Number Research Articles

Bio-mimicking DNA fingerprint profiling for HLS watermarking to counter hardware IP piracy

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches.

The Number of Stocks Before (n) and After Portfolio Optimization (k): The Heuristic k ≈ sqrt(n)

This study focuses on the relationship between the number of securities (n) pre-selected for mean-variance portfolio optimization and the number of optimal securities (k). We propose a heuristic k ≈ square root (n) based on empirical research optimizing different sized (n) portfolios. That is, a sample selection of 20-30 securities should yield a portfolio of about five optimal securities, and an initial sample of 500 securities, should result in an optimal portfolio of about 22. We focus on the tangent portfolio that maximizes the return-to-risk ratio. The heuristic finds its support, rationale, and logic in the numerical properties and statistical nature of the optimization. More specifically, the heuristic seems to originate in the dynamic convergence patterns observable in many statistical processes, especially in standard deviations. It is also supported by available results in the literature. Our “square root” heuristic functions as part of the wider family of approximation around the power law, where some variables (authors, securities, people) receive a disproportionate share of a given collection of items – see, for example, Pareto’s principle, Zipf’s law, Lotka’s law, Price’s square root law, Simon’s law, etc. The heuristic provides assistance not only in anticipating the number of optimal securities chosen by the mean-variance optimizer but also in suggesting selectivity in the effort of pre-selecting securities prior to the optimization and in sharpening portfolio-based approaches to investing in general. In sum, the heuristic k ≈ sqrt(n) seems helpful at all levels of portfolio management.

Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets

This article focuses on testing for the presence of alpha in time-varying factor pricing models, specifically when the number of securities N is larger than the time dimension of the return series T. We introduce a maximum-type test that performs well in scenarios where the alternative hypothesis is sparse. We establish the limit null distribution of the proposed maximum-type test statistic and demonstrate its asymptotic independence from the sum-type test statistics proposed by Ma et al. Additionally, we propose an adaptive test by combining the maximum-type test and sum-type test, and we show its advantages under various alternative hypotheses through simulation studies and two real data applications.

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.

Automating Markowitz Optimizations Using VBA

In the interest of reducing grading workload and managing cross-student sharing (cheating), we provide an example of automating the construction of Markowitz (1959) efficient frontiers. The program automates the entire procedure from downloading data for a specified number of securities (using either randomly generated securities from a provided list or specified tickers) to generating graphs of the efficient frontier. Many commercially available optimization programs are available, but this program is free to use and works within Microsoft Excel (2007-2010), the software package most professors use when creating optimization assignments. The program is written in Visual Basic for Applications (VBA) butdoes not require any knowledge of VBA to use. The program allows easy creation of random portfolios for student assignments so that cross-student sharing can be minimized without adding an additional grading burden on the professor or TA. The professor can also be assured that the stocks selected produce the desired type of optimization (actually work) before using each set of securities. Where students are allowed to select their own portfolios, the program allows professors to easily check computations and optimizations without having to manually optimize each portfolio. The program can also be used to demonstrate the similarity of efficient frontiers across different stocks, portfolio sizes, and dates.

A survey on the (in)security of trusted execution environments

As the number of security and privacy attacks continue to grow around the world, there is an ever increasing need to protect our personal devices. As a matter of fact, more and more manufactures are relying on Trusted Execution Environments (TEEs) to shield their devices. In particular, ARM TrustZone (TZ) is being widely used in numerous embedded devices, especially smartphones, and this technology is the basis for secure solutions both in industry and academia. However, as shown in this paper, TEE is not bullet-proof and it has been successfully attacked numerous times and in very different ways. To raise awareness among potential stakeholders interested in this technology, this paper provides an extensive analysis and categorization of existing vulnerabilities in TEEs and highlights the design flaws that led to them. The presented vulnerabilities, which are not only extracted from existing literature but also from publicly available exploits and databases, are accompanied by some effective countermeasures to reduce the likelihood of new attacks. The paper ends with some appealing challenges and open issues.

Is There Value in Large Cap Index Selection?

The rise of passive investing is shining a new light on the world of Indexing. Indexing is all around us today, whether being discussed by a media outlet showing the movements of the day or a newly launched passive ETF that tracks a recently created index. But there is little research on whether picking one index over another provides value to the investor when the indexes all share very similar index construction methodologies. Is there value in selecting one over the other or should investors choose the one that is the most accessible or cheapest to access? This article aims to fill that gap by conducting a comparative analysis of US Large Cap indexes that are substantially similar in their construction methodologies. As expected, the indexes selected perform very similar to one another based on the quantitative methods used within the analysis. Other than number of securities, the largest difference among the indexes is their exposure to Mid-Cap stocks. The decision of which index to select is dependent on the application being selected. This article focuses on the four purposes outlined by DiBartolomeo (2019) which are: journalistic, representation of an asset class, a fair benchmark, and basis of passive investment. In most cases, each index is a reasonable substitution for the others. Given that risk and return profiles are similar, factors such as number of securities, licensing costs, and brand value are the most significant to consider. This is especially true when selecting an index as the basis of a passive product, where those factors affect the product cost and capacity that can determine the success or failure of a passive product.

Strategy of Value Averaging for Private Investor on Russian Stock Exchange

The Russian stock exchange revived and began its energetic development in 1991 and it was connected with economy liberalization. Thus we can say that modern history of the Russian stock exchange embraces about 30 years, while in global practice the first stock exchange was born in 1661 in Amsterdam. The most progressive, according to numerous international investors, American stock exchange started functioning in 1791. This short history of the Russian stock exchange development could explain low finance literacy of the population. In spite of fast growth in the number of registered broker’s accounts on the Moscow exchange, a lot of investors in Russia prefer to redistribute a considerable proportion of their resources in foreign currency, gold, real estate. Such fund distribution cannot be explained only by low finance literacy. The Russian stock exchange demonstrates high volatility as, according to global rating agencies, it is emerging. However, stock exchange can be an appealing tool of investment, in case clear and high-quality strategies are used. Within the frames of the article the use of averaged value strategy on the Russian stock exchange is studied, at the same time it is compared with other popular strategies of long-term investment, such as strategy of equal amount purchase, strategy of buying the same number of securities.

High‐dimensional non‐parametric tests for linear asset pricing models

This paper develops a novel non‐parametric test for testing the high‐dimensional alpha in linear asset pricing models, where the number of securities can be much larger than the time‐dimension of the return series. The asymptotic null distribution and the local power property are established for a class of weighted spatial‐sign tests, which results in an optimal test INST by choosing the weight function as the inverse of the norm. The INST test is optimal in the sense that it is locally most powerful within this class. As a non‐parametric test, the INST test is also robust to the departures from normality of the error distribution. Monte Carlo simulation and empirical study with real financial data show the superiority of INST test in terms of both robustness and efficiency.

TACAS-IoT: Trust Aggregation Certificate-Based Authentication Scheme for Edge-Enabled IoT Systems

The Internet of Things (IoT) is a network of interconnected, Internet-connected items (i.e., smart devices) that can collect and transmit data across a wireless network without the need for human intervention. IoT enables the systems to have higher efficiency and dependability in their day-to-day operations due to its strong focus on machine-to-machine (M2M) connectivity, big data, and machine learning. While IoT has many advantages over traditional techniques, it also has a number of security and privacy concerns. Trust is a belief in the competence of a device (computing machine) to act dependably, securely and reliably in some specific context. In an M2M (one IoT device to other IoT device) communication, trust is accomplished by making the use of cryptographic operations (i.e., digital signatures and electronic certificates). Various security threats and attacks have been launched on IoT connectivity in recent years. A trust mechanism is necessary to ensure the quality of collaborative service behaviors and to build confidence between IoT devices. As a result, how to create an effective trust computing mechanism has become an emerging topic in IoT. Therefore, we provide the design of a novel trust-aggregation-based authentication scheme for secure communication of edge-enabled IoT (in short, TACAS-IoT). The security analysis shows that TACAS-IoT is secured against a variety of attacks. Moreover, TACAS-IoT delivers greater security and capabilities with less communication and computation overheads, according to the performance comparison. Finally, a practical implementation of TACAS-IoT is provided in order to assess its impact on the key performance parameters.

Bike-and-Ride:Build It and They Will Come

Converting park-and-ride to bike-and-ride trips could yield important environmental, energy conservation, and public-health benefits. While cycling in general is becoming increasingly popular in the United States, it still makes up a miniscule portion of access trips to most rail transit stations. At several rail stations of the Bay Area Rapid Transit (BART) system, 10 percent or more of access trips are by bicycle, up considerably from a decade earlier. This paper adopts a case-study approach to probe factors that have had a hand in not only cycling grabbing a larger market share of access trips to rail stops, but also in the enlargement of bike access-sheds over time. Both on-site factors, such as increases in the number of secure and protected bicycle parking racks, as well as off-site factors, such as increases in the lineal miles of separated bike-paths and bike boulevards, appear to explain growing use of bicycles for accessing rail stations. The adage “build it and they will come,” we argue, holds for bicycle improvements every bit as much as other forms of urban transportation infrastructure. Pro-active partnerships between transit agencies, local municipalities, and bicycle advocacy organizations are critical to ensuring such improvements are made.

An Evaluation of Security in Blockchain-based Sharing of Student Records in Higher Education

Blockchain has recently taken off as a disruptive technology, from its initial use in cryptocurrencies to wider applications in areas such as property registration and insurance due to its characteristic as a distributed ledger which can remove the need for a trusted third party to facilitate transactions. This spread of the technology to new application areas has been driven by the development of smart contracts – blockchain-based protocols which can automatically enforce a contract by executing code based on the logic expressed in the contract. One exciting area for blockchain is higher education. Students in higher education are ever more mobile, and in an ever more agile world, the friction and delays caused by multiple levels of administration in higher education can cause many anxieties and hardships for students as well as potential employers who need to examine and evaluate student credentials. Distance learning as a primary platform for higher education promises to open up higher education to a wider range of learners than ever before. Blockchain-based storage of academic credentials is being widely studied due to the advantages it can bring. As with any network-based system, blockchain comes with a number of security and privacy concerns. Blockchain needs to meet several security-related requirements to be widely accepted: decentralization; confidentiality; integrity; transparency; and immutability. Researchers have been busy devising schemes to ensure that such requirements can be met in blockchain-based systems. Several types of blockchain-specific attacks have been identified: 51% attacks; malicious contracts; spam attacks; mining pools; targeted DDoS attacks; and others. Real-world attacks on blockchain-based systems have been seen on cryptocurrency sites. In this paper, we will evaluate the specific privacy and security concerns for blockchain-based systems used for academic credentials as well as suggested solutions. We also examine the issues for academic credentials which are stored “off-chain” in such systems (as is often the case). In this case, a Distributed File System (DFS) implemented with a peer-to-peer (P2P) architecture is often the choice for the storage of the academic credentials since it matches the decentralized nature of blockchain. Blockchain then contributes much to the usefulness of such a DFS, making it in turn a good match for a P2P DFS such as IPFS.

An efficient and access policy-hiding keyword search and data sharing scheme in cloud-assisted IoT

By leveraging the cloud computing paradigm, the cloud-assisted Internet of Things (IoT) is able to offer the massive storage and highly available computation services for end-users. When the data collected from IoT devices is endlessly gathering to the cloud center, the data security become new challenges. Encrypting data is an effective measure to guarantee data confidentiality. However, traditional encryption techniques make the data searching and access control inoperative. Current attribute-based keyword search (ABKS) techniques provide a feasibility to achieve data searching and access control over encrypted data simultaneously. However, existing ABKS schemes leak sensitive information via clear access policy and may be unsuitable for certain IoT applications, where the access policies in data might contain private information of data owners such as the security number or the residential area of a resident in smart grids. In this work, we design an efficient and policy-hiding attribute-based keyword search and data sharing scheme (PH-ABKS-DS) in cloud-assisted IoT. To the best of our knowledge, this construction is the first PH-ABKS-DS with practical efficiency that is constructed on prime order group. We provide detailed correctness and security analyses for PH-ABKS-DS. Extensive experiments demonstrate that our proposed PH-ABKS-DS is correct and practical.

Sustainable Development: Strengthening of Food Security in EU Countries

The issue of food security is relevant for all countries, but it does not have a universal solution. In particular, this is confirmed by the countries of the European Union, which, despite a common food policy, demonstrate different levels of food security. Using their example, this study aims to develop recommendations for improving food security in the context of sustainable development. The study is based on quantitative approaches and analyses the main food security indicators of the Czech Republic, Germany, the Netherlands, and Norway, based on data from the Food and Agriculture Organisation of the United Nations. This study proves the need for more detailed development of individual development strategies in the field of food security in the context of sustainable development. The main differences between the food systems of the Czech Republic, Germany, the Netherlands, and Norway in terms of average food energy needs, GDP per capita, the number of obese adults and food security per capita were identified. There have also been changes in the food security systems of the Netherlands and Norway, which have reduced the volatility of food supply per capita in 2010-2020. EU countries need to stabilise volatility to increase food security. EU Member States’ attempts to implement new food security policies must consider the local specificities of food systems. Despite the common problems of waste, nutrition of different social groups, obesity, equal access to food, sustainable production, implementation of research and development of the food security programme, the degree of these challenges varies. The practical value of the study lies in the empirical assessment of the state of food security on the example of the Czech Republic, Germany, the Netherlands, and Norway in 2000-2020, which proves the need for differentiation in the common food security policy

On the Diversification of Fixed Income Assets

This article introduces a new approach for dealing with the diversification/concentration risk of fixed income assets. Because Government bonds, corporate bonds, and mortgage backed securities constitute a large proportion of the assets of institutional investors in most countries, it is important to be able to determine the number of lines/issuers of such assets, not only for portfolio management but also for risk management purposes. The approach that I introduce shows the dependence of the critical number of lines of fixed income assets on the main interest rate risk and credit risk drivers. Specifically, I examine the importance of volatility risk, force of mean reversion, default risk, recovery risk, and default dependence risk on the critical number of assets in a fixed income portfolio. The methodology in this paper relies on the use of the coefficient of variation for the computation of the critical number of credit-sensitive securities in a fixed income portfolio. To the best of my knowledge, this paper is the first to develop such an approach.

Better researchers, better people? The dangers of empathetic research on the extreme right

ABSTRACT The difficulties of empathising with violent perpetrators has preoccupied a small number of social scientists, particularly criminologists and critical security and terrorism scholars. Most of this work examines how researchers can mitigate the impact of engaging with violent perpetrators and minimize any distress this may cause. This article examines whether a deeper empathetic exchange with violent actors - one in which researchers open themselves cognitively and emotionally and that accepts the dialogical nature of empathy - is the next step that needs to be undertaken by critical scholars. It then examines the costs of such a move. The discomfort and danger of empathizing with violent actors is examined through the particularly thorny example of research with violent actors from the extreme-right.

ДЕРЖАВНЕ РЕГУЛЮВАННЯ ФОНДОВОЇ БІРЖІ В КРАЇНАХ ЄВРОПИ ТА УКРАЇНІ

In this article considers that the stock market is the object of state regulation which substantiates the main aspects of the formation of the stock exchange within financial market. Here given characteristic of regulatory infrastructure of financial market, revealed the essence of its state regulation in general and stock exchange in particular. Since the stock market is a more to spreading basis of financial market, than he necessarily primes state regulation as matter element economical system of the each country. The mainly in this article by illuminated foreign experience of formation and improvement stock market, where by indicated state institutions as appointed conducting regulation of exchange trades such as state power bodies, state departments, self-regulation organization, norm-legal providing. Because the stock market in the that or other countries characterized primarily by the volume of the stock exchange trades stocks, and also number of securities, what to located in turnover on the stock exchanges. This is where it is advisable to reveal the features of the state regulation in the European countries and implement this comparison with domestic realities, at what stage they are. For this in article by formation and generalities the structure of National securities and stock market commission, as single body of regulation of sphere stocks exchange in the state. In high-developed countries the main direction of improving the system of state regulation financial market is its adaptation to condition World market, in first duty, legal base and by taking standards what to concerning financial tools, securities issues and other. But in middle-developed countries basis task for body of regulation is a involvement of massed investor, introduction of new tools, improvement of the technological structure of market. Exactly from activities regulatory bodies it depends on the level of transparency the financial market, which is the most important means of protecting investors and allows them to better evaluation the possible risks, and therefore increase the efficiency of the market as a whole. And based on this there is a practical possibility for improvement state regulation of domestic stock market with aim increasing effectiveness in the economics Ukraine.

Дослідження пам’яток кам’яної сакральної архітектури ніез «Переяслав» (1991–2020): історіографія проблеми

An attempt at historiographical analysis dedicated to the monuments of stone sacred architecture of the National Historical and Ethnographic Reserve «Pereiaslav» was made in this research. These monuments are listed on the State Register of Immovable Monuments of Ukraine by the Resolution of the Cabinet of Ministers of Ukraine № 929 of October 10, 2012 under security numbers: 100030/1-N, 100030/2-N, 100031/1- N, 100031/2-N. These include: Ascension Cathedral (1700), St. Michael’s Church (1750) and the bell tower with the refectory of St. Michael’s Church (1747). The bell tower of Ascension Cathedral (1770–1776), which is on the balance of Pereiaslav UTC and is an integral part of the complex of monuments of Ascension Monastery 1700–1776, was taken into account too. The results of the study of the above sacred monuments should be considered as part of the author’s research. The subject of this research is the study of scientific literature on cultural heritage sites of the National Historical and Ethnographic Reserve «Pereiaslav». Publications on this issue should be considered within two periods: the soviet (1954−1991) and modern (1991−2019). An overview of scientific works published during the modern period, characterized by an increase of scientific interest in sacred stone monuments of architecture of the National Historical and Ethnographic Reserve «Pereiaslav» was offered in this study. In order to convenience of perception, we divided these studies into two groups according to their thematic content. The first one includes publications directly devoted to the study of these objects. Among the researchers in this group are: D. A. Teteria, M. T. Tovkailo, O. V. Yurchenko, L. M. Nabok, O. V. Kolybenko, V. V. Vecherskyi, V. P. Melnyk, I. V. Bilchenko and other. Another group consists of generalized and fragmentary researches, related to the study of the history of Pereiaslav and Ukraine in general. These include studies of various thematic areas, where monastic complexes of Pereiaslav are mentioned in a certain aspect. Among the researchers in this group are: O. V. Lesyk, V. V. Nikitina, M. I. Sikorskyi, H. M. Buzian, M. V. Rodobudko, O. V. Tkachenko, N. M. Soloviova, V. M. Lialko, H. P. Tkachenko, N. H. Tkachenko, L. V. Lykhovyd, I. I. Kucherenko, S. O. Korkach, A. Kuzmenko, N. V. Kostenko, Yu. V. Avramenko, O. I. Tonkonoh, V. M. Shakula, O. M. Zham, O. A. Horbovyi, L. V. Naumenko, S. M. Zuber, S. M. Zakharchenko ta Yu. S. Fihurnyi, Yu. A. Mytsyk ta I. Yu. Tarasenko, V. Yu. Vysharovskyi, S. S. Dembitskyi, N. V. Kostiuk, I. H. Dunaina, S. A. Teteria, N. A. Boiko, M. V. Shkira, N. M. Shkira, A. O. Ivanenko, O. A. Tarapon, T. Yu. Nahaiko, S. V. Taranets, V. A. Lokha, V. P. Kotsur, O. I. Hurzhii, O. L. Lastovska and other. Our work on collecting and systematizing scientific literature on stone sacred monuments of architecture will be used in the future during the creation of a bibliographic index of works on objects under the operational management of the NHER «Pereiaslav».

Supervisory framework for threat detection withmultilayer processing in CNN

Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).

High-dimensional test for alpha in linear factor pricing models with sparse alternatives

We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the time dimension of the return series, and the alternative hypothesis is sparse, i.e. the alpha vector is sparse. As a general alternative, we suggest to combine the max-of-squares type test and a sum-of-squares type test, to benefit from the power advantages of both tests. We apply the two proposed tests to the monthly returns on securities in the Chinese and the U.S. stock markets, respectively under the Fama–French three-factor model, and confirm the usefulness of the proposed tests in detecting the presence of alpha.