scholarly journals Machine Learning Cybersecurity Adoption in Small and Medium Enterprises in Developed Countries

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 150
Author(s):  
Nisha Rawindaran ◽  
Ambikesh Jayal ◽  
Edmond Prakash
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Small And Medium Enterprises ◽  
Developed Countries ◽  
Cyber Attacks ◽  
Primary Data ◽  
Network Intrusion ◽  
The Uk ◽  
Medium Enterprises ◽  
Future Path

In many developed countries, the usage of artificial intelligence (AI) and machine learning (ML) has become important in paving the future path in how data is managed and secured in the small and medium enterprises (SMEs) sector. SMEs in these developed countries have created their own cyber regimes around AI and ML. This knowledge is tested daily in how these countries’ SMEs run their businesses and identify threats and attacks, based on the support structure of the individual country. Based on recent changes to the UK General Data Protection Regulation (GDPR), Brexit, and ISO standards requirements, machine learning cybersecurity (MLCS) adoption in the UK SME market has become prevalent and a good example to lean on, amongst other developed nations. Whilst MLCS has been successfully applied in many applications, including network intrusion detection systems (NIDs) worldwide, there is still a gap in the rate of adoption of MLCS techniques for UK SMEs. Other developed countries such as Spain and Australia also fall into this category, and similarities and differences to MLCS adoptions are discussed. Applications of how MLCS is applied within these SME industries are also explored. The paper investigates, using quantitative and qualitative methods, the challenges to adopting MLCS in the SME ecosystem, and how operations are managed to promote business growth. Much like security guards and policing in the real world, the virtual world is now calling on MLCS techniques to be embedded like secret service covert operations to protect data being distributed by the millions into cyberspace. This paper will use existing global research from multiple disciplines to identify gaps and opportunities for UK SME small business cyber security. This paper will also highlight barriers and reasons for low adoption rates of MLCS in SMEs and compare success stories of larger companies implementing MLCS. The methodology uses structured quantitative and qualitative survey questionnaires, distributed across an extensive participation pool directed to the SMEs’ management and technical and non-technical professionals using stratify methods. Based on the analysis and findings, this study reveals that from the primary data obtained, SMEs have the appropriate cybersecurity packages in place but are not fully aware of their potential. Secondary data collection was run in parallel to better understand how these barriers and challenges emerged, and why the rate of adoption of MLCS was very low. The paper draws the conclusion that help through government policies and processes coupled together with collaboration could minimize cyber threats in combatting hackers and malicious actors in trying to stay ahead of the game. These aspirations can be reached by ensuring that those involved have been well trained and understand the importance of communication when applying appropriate safety processes and procedures. This paper also highlights important funding gaps that could help raise cyber security awareness in the form of grants, subsidies, and financial assistance through various public sector policies and training. Lastly, SMEs’ lack of understanding of risks and impacts of cybercrime could lead to conflicting messages between cross-company IT and cybersecurity rules. Trying to find the right balance between this risk and impact, versus productivity impact and costs, could lead to UK SMES getting over these hurdles in this cyberspace in the quest for promoting the usage of MLCS. UK and Wales governments can use the research conducted in this paper to inform and adapt their policies to help UK SMEs become more secure from cyber-attacks and compare them to other developed countries also on the same future path.

scholarly journals Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME)

2021 ◽  
Vol 13 (8) ◽  
pp. 186
Author(s):  
Nisha Rawindaran ◽  
Ambikesh Jayal ◽  
Edmond Prakash ◽  
Chaminda Hewage
Keyword(s):  
Machine Learning ◽  
Home Environment ◽  
Cyber Security ◽  
Working Environment ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Set Up ◽  
The Uk ◽  
Medium Enterprises ◽  
At Home

Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.

scholarly journals How Capability Reconfiguration in Coping With External Dynamism Can Shape the Performance of the Vietnamese Enterprises

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Huy-Cuong Vo-Thai ◽  
Shihmin Lo ◽  
My-Linh Tran
Keyword(s):  
Positive Impact ◽  
Small And Medium Enterprises ◽  
Developed Countries ◽  
Primary Data ◽  
Internal Factors ◽  
Catching Up ◽  
Final Sample ◽  
Research Findings ◽  
Market Turbulence ◽  
Medium Enterprises

The purpose of this study is to examine the direct and moderating effects of internal endowment and external dynamism on capability reconfiguration, which in turn has a positive impact on a firm’s post-reconfiguration performance. A researcher-designed survey questionnaire was developed based on multiple works and subsequently administered with a final sample of 266 Vietnamese small and medium enterprises engaged in manufacturing industries. As a result, we find that internal endowment and external dynamism positively impact a firm’s capability reconfiguration and post-reconfiguration performance consequently. This empirical research provides four major contributions that supplement the extant literature. First, the internal endowment sponsored by resource abundance and absorptive capacity enables both a firm’s capability evolution and capability substitution. Second, the external dynamism in terms of market turbulence, technology turbulence, and competitive intensity directly affects the enterprise’s capability reconfiguration and positively moderates the relationship between internal endowment and capability reconfiguration. Third, this study demonstrates that the firm’s engagement on capability reconfiguration once in line with external and internal factors can help maintain its post-reconfiguration performance. Finally, the primary data collected in Vietnam offers a firsthand investigation of the catching-up economy to be compared with the research findings available in developed countries.

scholarly journals Machine Learning Approach for Intrusion Detection Systems as a Cyber Security Strategy for Small and Medium Enterprises

2022 ◽  
Vol 19 ◽  
pp. 474-480
Author(s):  
Nevila Baci ◽  
Kreshnik Vukatana ◽  
Marius Baci
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Small And Medium Enterprises ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Original Dataset ◽  
Medium Enterprises

Small and medium enterprises (SMEs) are businesses that account for a large percentage of the economy in many countries, but they lack cyber security. The present study examines different supervised machine learning methods with a focus on intrusion detection systems (IDSs) that will help in improving SMEs’ security. The algorithms that are tested through a real dataset, are Naïve Bayes, Sequential minimal optimization (SMO), C4.5 decision tree, and Random Forest. The experiments are run using the Waikato Environment for Knowledge Analyses (WEKA) 3.8.4 tools and the metrics used to evaluate the results were: accuracy, false-positive rate (FPR), and total time to train and build a classification model. The results obtained from the original dataset with 130 features show a high value of accuracy, but the computation time to build the classification model was notably high for the cases of C4.5 (1 hr. and 20 mins) and SMO algorithm (4 hrs. and 20 mins). the Information Gain (IG) method was used and the result was impressive. The time needed to train the model was reduced in the order of a few minutes and the accuracy was high (above 95%). In the end, challenges that SMEs can have for choosing an IDS such as lack of scalability and autonomic self-adaptation, can be solved by using a correct methodology with machine learning techniques.

scholarly journals Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks

2021 ◽  
Vol 297 ◽  
pp. 01057
Author(s):  
Amine Khatib ◽  
Mohamed Hamlich ◽  
Denis Hamad
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Large Scale ◽  
Performance Comparison ◽  
Cyber Attacks ◽  
Network Intrusion ◽  
Critical Issues ◽  
Imbalanced Classes ◽  
Use Of Internet

IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated using the SMOTE technique. The Nystrom based kernel SVM is the first time used to detect attacks in the IoT network and the results are promising. The evaluation metrics used in the performance comparison are accuracy, precision, recall, f1 score, and auc-roc curve.

The Impact of Financial Literacy on Financial Preparedness for Retirement in the Small and Medium Enterprises Sector in Uganda

2020 ◽  
Vol 9 (3) ◽  
pp. 26-41
Author(s):  
Colin Agabalinda ◽  
Alain Vilard Ndi Isoh
Keyword(s):  
Financial Literacy ◽  
Small And Medium Enterprises ◽  
Group Analysis ◽  
Moderating Effect ◽  
Primary Data ◽  
Retirement Preparedness ◽  
Medium Enterprises ◽  
The Impact ◽  
The Relationship ◽  
Effect Of Age

The study investigated the direct effects of financial literacy (knowledge, skills, and attitudes) on financial preparedness for retirement and the moderating effect of age among the small and medium enterprises in Uganda. Primary data was collected from a sample of n = 380 selected from the SME workforce. Descriptive analysis was run on SPSS, while validity and reliability of the measurement items yielded satisfactory composite reliability scores and average variance explained (AVE) scores for all items. Structural equation modelling (SEM) was used to test the hypotheses and multi-group analysis conducted to test for the moderating effect of age on the relationship between financial literacy and retirement preparedness. The results revealed that knowledge and skills were significant predictors of retirement preparedness. However, ‘attitude' was not a significant predictor, and age had no moderating effect on the relationship between the study variables. These findings present practical implications for policymakers and financial educators in a developing country context.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Analysis of Major Problems Faced by Small Firms in North India

2015 ◽  
Vol 15 ◽  
pp. 150-160
Author(s):  
Davinder Singh ◽  
Jaimal Singh Khamba ◽  
Tarun Nanda
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Economic Development ◽  
Small Firms ◽  
Small And Medium Enterprises ◽  
Developed Countries ◽  
North India ◽  
Small Enterprises ◽  
Employment Generation ◽  
Medium Enterprises

Micro, Small and Medium Enterprises (MSMEs) have been noted to play a significant role in promoting economic growth in less developed countries, developing and also in developed countries. Worldwide, the micro and small enterprises have been accepted as the engine of economic growth of any nation. Small and Medium Enterprises are the backbone of the economies, because it trigger employment, output, export, poverty alleviation, economic empowerment, economic development etc. in developed as well as in developing countries. It is more important to developing countries as the poverty and unemployment are burning problems. MSMEs have been playing a momentous role in overall economic development of a country like India where millions of people are unemployed or underemployed. Therefore, the growth of small sectors is essential for the growth in the GDP, employment generation, total manufacturing production and export. India, being one of the fastest growing economies of the world, needs to pay an honest attention for the utmost growth of MSMEs for its increased contribution in above areas.

scholarly journals Promoting Clusters and Networks for Small and Medium Enterprises to Economic Development in the Globalization Era

SAGE Open ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. 215824401769715 ◽  
Author(s):  
Sara Foghani ◽  
Batiah Mahadi ◽  
Rosmini Omar
Keyword(s):  
Developing Countries ◽  
Small And Medium Enterprises ◽  
Empirical Studies ◽  
Developed Countries ◽  
Policy Makers ◽  
Ongoing Research ◽  
Global Challenges ◽  
Medium Enterprises ◽  
Global Arena ◽  
Asian Developing Countries

This research attempts to explore the importance of cluster-based systems in preparation for small and medium enterprises (SMEs) to go global, and it is an ongoing research. The findings of this research are aimed at providing insights to policy makers, academicians, and practitioners with the objective of creating initiatives, strategies, and policies, which reflect the primary aim of supporting SMEs in managing global challenges. SMEs that are cluster-based have the potential to facilitate the successful inclusion of SMEs in the growth of productivity and networks of global distribution. Most Asian developing countries are in the dark when it comes to this matter. The main purpose of this study is to investigate the relations between the capabilities of the networks and clusters in developing SMEs’ preparedness in facing business players in the global arena. This study’s scope includes specific Asian developing countries. Even though the issue of clusters in SMEs has been well researched in developed countries, such empirical studies are still lacking in the Asian region despite its prevalent collectivism practice. In the concluding analysis, the study intends to develop a model emphasizing the cluster-based industrial SMEs toward globalization.

scholarly journals Analisis Dampak Tax Morale Terhadap Kepatuhan Pajak UMKM: Studi Kasus Kota Semarang

2018 ◽  
Vol 1 (1) ◽  
pp. 14
Author(s):  
Muslimah Mahmudah ◽  
Deden Dinar Iskandar
Keyword(s):  
Small And Medium Enterprises ◽  
Tax Compliance ◽  
Binary Logistic Regression ◽  
Primary Data ◽  
Binary Logistic Regression Analysis ◽  
Ethical Business ◽  
Tax Morale ◽  
Business Size ◽  
Medium Enterprises ◽  
The Impact

This study aims to analyze the impact of tax morale on Micro, Small, and Medium Enterprises (MSMEs) tax complianceSemarang City as the case study. This study uses primary data generated from 117 samples of MSMEs in Semarang. Data analysis is performed  using binary logistic regression analysis. The results showed that environmental, institutional, ethical, business, and business size variables significantly influence MSMEs tax compliance. On the other hand, variables whose effect on tax compliance is not statistically significant include happiness, religiosity, gender, age, education, and marital status.

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