scholarly journals Breaking botnets: A quantitative analysis of individual, technical, isolationist, and multilateral approaches to cybersecurity

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Justin K Haner ◽  
Robert K Knake
Keyword(s):  
Time Series Data ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Power Grids ◽  
Series Data ◽  
The Internet ◽  
The World ◽  
Internet Freedom ◽  
Belt And Road ◽  
Technical Solutions

Abstract Malicious networks of botnets continue to grow in strength as millions of new users and devices connect to the internet each day, many becoming unsuspectingly complicit in cyber-attacks or unwitting accomplices to cybercrimes. Both states and nonstate actors use botnets to surreptitiously control the combined computing power of infected devices to engage in espionage, hacking, and to carry out distributed denial of service attacks to disable internet-connected targets from businesses and banks to power grids and electronic voting systems. Although cybersecurity professionals have established a variety of best practices to fight botnets, many important questions remain concerning why levels of botnet infections differ sharply from country to country, as relatively little empirical testing has been done to establish which policies and approaches to cybersecurity are actually the most effective. Using newly available time-series data on botnets, this article outlines and tests the conventionally held beliefs and cybersecurity strategies at every level—individual, technical, isolationist, and multilateral. This study finds that wealthier countries are more vulnerable than less wealthy countries; that technical solutions, including patching software, preventing spoofing, and securing servers, consistently outperform attempts to educate citizens about cybersecurity; and that countries which favor digital isolation and restrictions on internet freedom are not actually better protected than those who embrace digital freedom and multilateral approaches to cybersecurity. This latter finding is of particular importance as China’s attempts to fundamentally reshape the internet via the “Digital Silk Road” component of the Belt and Road Initiative will actually end up making both China and the world less secure. Due to the interconnected nature of threats in cyberspace, states should instead embrace multilateral, technical solutions to better govern this global common and increase cybersecurity around the world.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

scholarly journals Vulnerability and risk assessment for operating system (OS) with framework STRIDE: comparison between VulnOS and Vulnix

2021 ◽  
Vol 23 (3) ◽  
pp. 1643
Author(s):  
Adityas Widjajarto ◽  
Muharman Lubis ◽  
Vreseliana Ayuningtyas
Keyword(s):  
Information Disclosure ◽  
Risk Estimation ◽  
Rapid Development ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Easy Access ◽  
Case Scenario ◽  
Worst Case ◽  
Network Function ◽  
The World

<p><span lang="EN-US">The rapid development of information technology has made security become extremely. Apart from easy access, there are also threats to vulnerabilities, with the number of cyber-attacks in 2019 showed a total of 1,494,281 around the world issued by the </span><span lang="EN-US">national cyber and crypto agency (BSSN) honeynet project. Thus, vulnerability analysis should be conducted to prepare worst case scenario by anticipating with proper strategy for responding the attacks. Actually, vulnerability is a system or design weakness that is used when an intruder executes commands, accesses unauthorized data, and carries out denial of service attacks. The study was performed using the AlienVault software as the vulnerability assessment. The results were analysed by the formula of risk estimation equal to the number of vulnerability found related to the threat. Meanwhile, threat is obtained from analysis of sample walkthroughs, as a reference for frequent exploitation. The risk estimation result indicate the 73 (seventy three) for the highest score of 5 (five) type risks identified while later on, it is used for re-analyzing based on the spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of prvilege (STRIDE) framework that indicated the network function does not accommodate the existing types of risk namely spoofing.</span></p>

Access to Internet and Internet Shutdown: A Rights Perspective

2021 ◽  
Vol 08 (1&2) ◽  
pp. 7-14
Author(s):  
Ajay Kumar ◽  
Keyword(s):  
Human Rights ◽  
The Internet ◽  
Important Concern ◽  
The World ◽  
Current Scenario ◽  
History Of ◽  
Internet Freedom ◽  
Source Of Information ◽  
Union Territory ◽  
Different Parts

Access to the internet is fast becoming a basic right given the plethora of information available on the net these days. In the current scenario, the issue of internet shutdown has become an important concern in India. Internet shutdown affects people socially, psychologically and economically. On one hand, many democratic countries of the world are discussing about digital freedom and human rights, while on the other hand, some countries including India are continuously practicing Internet shutdowns in different parts of their countries. India has become the top country of the world in terms of the numbers of Internet shutdowns. The Internet has become such a prominent source of information for all of us that when Internet connectivity is suspended, many people are affected as they depend on the Internet services for various purposes. Internet shutdown is not only harmful to democracy and governance but also to the economy of the country. Internet shutdowns are direct violations of digital freedom and human rights. The main objective of this paper is to argue that access to internet is a basic right and highlight the problem of Internet shutdown in India and its adverse impact on the lives of Indians. In addition, this paper attempts to highlight a brief history of Internet shutdowns in India. The paper shows how frequent clampdowns on internet affects the economy, as has been the case of Union Territory of Jammu & Kashmir thereby highlighting the case for internet freedom for the survival of the economy especially in Digital India.

A Deep Learning Based Methods for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognising and extracting the features which affect the pandemic is in the highest priorities. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Out-put Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which is outperforming other proposed method in the literature.

Factors Influencing International Institutional Investments

2020 ◽  
pp. 195-212
Author(s):  
Akriti Gupta ◽  
Gurpreet Kaur ◽  
Mahesh Sarva
Keyword(s):  
Financial Market ◽  
Time Series Data ◽  
Trade Openness ◽  
Global Market ◽  
Series Data ◽  
Institutional Investment ◽  
Flow Of Funds ◽  
The World ◽  
International Investors ◽  
Financial Market Risk

At the turn of the 21st century, globalization of developed and developing countries in the world witnessed institutional inflows from international investors which became the main characteristic of global capital markets. The current research has assessed time-series data from 2000 to 2017 to understand how the different elements that have influenced the foreign institutional investments and helped India become a global market for such investors. The results revealed that political risk, financial market development, trade openness of the country, size of the economy, and rate of return on investment are the important determinants in attracting foreign institutional investments in India. The chapter also found economic risk and financial market risk played an insignificant role in determining foreign institutional investment in India. The findings of the research help the present government and market regulators to introduce policies aimed at increasing the flow of funds from international institutional investors.

Income and Price Elasticities in India's Trade

1994 ◽  
Vol 19 (2) ◽  
pp. 13-20
Author(s):  
G S Gupta ◽  
H Keshava
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Individual Country ◽  
Price Elasticities ◽  
The World ◽  
Annual Time Series ◽  
Income And Price Elasticities ◽  
Annual Time

This article by G S Gupta and H Keshava estimates the export and import functions for India both at the aggregate (rest of the world) as well as the important individual country levels using annual time series data for the period 1960-61 through 1990-91.

Качество планирования городских транспортных сетей в зеркале классических моделей теории транспортного потока

2015 ◽  
pp. 56-67
Author(s):  
Михаил Яковлевич Блинкин
Keyword(s):  
Time Series Data ◽  
Fluid Model ◽  
Nobel Laureate ◽  
Series Data ◽  
Load Factor ◽  
Planning Decisions ◽  
Road Design ◽  
The World ◽  
Two Fluid Model ◽  
Gps Tracks

This article investigates the application of the classical models of the traffi c fl ow theory to the analysis of the modern transport and urban planning problems in the world. As a typical case study author refers to the “Two-Fluid Model of Urban Traffi c”, proposed by the American physicist Robert Herman and Nobel laureate Ilya Prigogine in the 1970s. The choice of this case was based not only on the model’s “noble scientifi c origin”, but on its modern appeal due to emergence of large arrays of the full- scale data (eg, GPS-tracks), that was unavailable before. “Herman–Prigogine” model allows for HP-indicator (η) calculation, which characterizes the elasticity of speeds to street-road network load factor increase. It is based on a comparison of synchronous time series data of running time and travel time. The indicator determines the quality of planning decisions on street and road design. The article presents the results of the HP-indicator calculations made for a number of cities around the world in 1980–2000,

scholarly journals A Deep Learning Based Method for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is the highest priority. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Output Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset, and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which are outperforming other proposed method in the literature.

scholarly journals GLOBAL MARKET OF PETROLEUM PRODUCTS: CURRENT STATE AND PERSPECTIVES OF DEVELOPMENT

2020 ◽  
pp. 27-39
Author(s):  
K. Bezugla ◽  
N. Kostyuchenko
Keyword(s):  
New Technologies ◽  
Time Series Data ◽  
Supply And Demand ◽  
Price Volatility ◽  
Petroleum Products ◽  
Global Market ◽  
Series Data ◽  
Total Output ◽  
Petroleum Production ◽  
The World

The paper is devoted to the peculiarities and perspectives of the global petroleum market development. The peculiarities of supply and demand formation at the global market of petroleum products are investigated in the article. The balance of supply and demand at the petroleum market is determined. The paper outlines the peculiarities of pricing for petroleum products. The dynamics of price changes on the global petroleum market in the period of 2010-2020 is studied. The conclusion was made that there is a price volatility on the global petroleum market. An analysis of the dynamics and structure of the world petroleum production by regions revealed that the total output of oil has increased due to the development of new technologies and due to the increased efficiency of petroleum production. The performed forecasting made it possible to conclude that petroleum price is expected to increase in the coming two periods. That will allow to establish a balance between supply and demand at the petroleum products’ market. Accordingly, the equalization of supply and demand for petroleum products is forecasted (even despite the crisis in the world). The econometric method of economic analysis was applied in the paper. The authors constructed an additive model for time series data to predict the dynamics of prices on the global market of petroleum products. The model was designed based on 16 observations in the period of October 2016 – July 2020.

scholarly journals Cyber-Attack Scoring Model Based on the Offensive Cybersecurity Framework

2021 ◽  
Vol 11 (16) ◽  
pp. 7738
Author(s):  
Kyounggon Kim ◽  
Faisal Abdulaziz Alfouzan ◽  
Huykang Kim
Keyword(s):  
Cyber Security ◽  
Personal Information ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Infrastructures ◽  
Cyber Attack ◽  
Security Framework ◽  
Scoring Model ◽  
Advanced Persistent Threats ◽  
The World

Cyber-attacks have become commonplace in the world of the Internet. The nature of cyber-attacks is gradually changing. Early cyber-attacks were usually conducted by curious personal hackers who used simple techniques to hack homepages and steal personal information. Lately, cyber attackers have started using sophisticated cyber-attack techniques that enable them to retrieve national confidential information beyond the theft of personal information or defacing websites. These sophisticated and advanced cyber-attacks can disrupt the critical infrastructures of a nation. Much research regarding cyber-attacks has been conducted; however, there has been a lack of research related to measuring cyber-attacks from the perspective of offensive cybersecurity. This motivated us to propose a methodology for quantifying cyber-attacks such that they are measurable rather than abstract. For this purpose, we identified each element of offensive cybersecurity used in cyber-attacks. We also investigated the extent to which the detailed techniques identified in the offensive cyber-security framework were used, by analyzing cyber-attacks. Based on these investigations, the complexity and intensity of cyber-attacks can be measured and quantified. We evaluated advanced persistent threats (APT) and fileless cyber-attacks that occurred between 2010 and 2020 based on the methodology we developed. Based on our research methodology, we expect that researchers will be able to measure future cyber-attacks.

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