Importance of Cyber Security

Tarter, Alex

doi:10.1007/978-3-319-53396-4_15

Cited by 20 publications

(5 citation statements)

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“…With the recent interest and progress in the development of internet and communication technologies over the last decade, network security has emerged as a vital research domain. It employs tools like firewall, antivirus software, and intrusion detection system (IDS) to ensure the security of the network and all its associated assets within a cyberspace 1 . Among these, network‐based intrusion detection system (NIDS) is the attack detection mechanism that provides the desired security by constantly monitoring the network traffic for malicious and suspicious behavior 2,3 …”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

656

215

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The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

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Section: Introductionmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

656

215

View full text Add to dashboard Cite

show abstract

“…To catch up with a development made to new communication platforms and applications, the subject of network security has become one of famous issues that are widely studied. Along this line, many attempts exploit a range of defence mechanism, for instance, firewall and IDS (intrusion detection system) modules to protect assets in a cyberspace [1][2][3]. To be more specific, unpatched services are good examples of targets of an intrusive attack, which is a significant threat to both individuals and institutes [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of Adversarial Attacks Using Ensemble Clustering Approach

Tatongjai¹,

Boongoen²,

Iam-On³

et al. 2023

Computers, Materials &Amp; Continua

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As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that is applicable to different network applications. In contrary to the wrapper method introduced with the benchmark dataset, three new filter models are proposed to transform the feature space without knowledge of class labels. These ECT (ensemble clustering based transformation) techniques, i.e., ECT-Subspace, ECT-Noise and ECT-Combined, are developed using the concept of ensemble clustering and three different ensemble generation strategies, i.e., random feature subspace, feature noise injection and their combinations. Based on the empirical study with published dataset and four classification algorithms, new models usually outperform that original wrapper and other filter alternatives found in the literature. This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks, and the second that focuses on recognizing obfuscated intrusions. In addition, analysis of algorithmic parameters, i.e., ensemble size and level of noise, is provided as a guideline for a practical use.

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“…The past presence in the state of mechanisms for interagency planning to implement legislative policies appeared to be the most relevant factor in different nations. Numerous researchers previously depicted the importance of cybersecurity law (Tarter 2017;Veale and Brown 2020) and implementation of cybersecurity law (Azmi 2020;Sattar et al 2018), and a few have described the important factors that can influence the implantation of cybersecurity laws in different countries (Goel 2020). Our study will explain different elements and factors that influence the implementation of a law positively or negatively in developing economies.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Study on the Factors Influencing Implementation of Cybersecurity Laws and Regulations in Pakistan

Bokhari

2023

Social Sciences

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The phenomenon of law implementation has received limited attention, despite the clear evidence that it is influenced by various factors prevalent in the country, and these factors can have an impact on and obstruct the effective implementation of legislation. The primary objective of this study was to analyze the critical factors that impact the implementation of cybersecurity laws in developing nations, such as Pakistan. The prevalence of corruption, a major hindrance to the implementation of cybersecurity laws and regulations, emerged as the most influential factor in Pakistan. Additionally, factors such as discrimination, illicit conduct, expertise, ambiguity, and public confidence significantly influenced the implementation of cybersecurity laws in Pakistan. A survey was conducted among managers from banking and IT firms to collect data samples on the factors that could potentially impact the implementation of the law. The findings from a sample of 172 respondents revealed that corruption, discrimination, illicit conduct, and ambiguity appeared to have a significant negative influence on cybersecurity law implementation, whereas expertise and public confidence emerged to have a significant positive influence on the implementation of cybersecurity laws in Pakistan. This study suggests that the government of Pakistan should consider various measures such as providing training, improving capacity building, fostering institutional cooperation, strengthening legislative conviction, and promoting global collaborations to enhance the implementation of cybersecurity.

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Importance of Cyber Security

Cited by 20 publications

References 1 publication

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Classification of Adversarial Attacks Using Ensemble Clustering Approach

A Quantitative Study on the Factors Influencing Implementation of Cybersecurity Laws and Regulations in Pakistan

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