A Survey on Emerging Cyber Crimes and Their Impact Worldwide

Gangwar, Suraj; Narang, Vinayak

doi:10.4018/978-1-6684-5594-4.ch080

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The world has witnessed a significant rise in cybercriminal activities [28]. Cybercriminals employ various techniques, such as phishing, ransomware attacks, data breaches, identity theft, and financial fraud, to exploit vulnerabilities in computer systems and networks.…”

Section: Context Of Cybersecurity Challengesmentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

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Section: Context Of Cybersecurity Challengesmentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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“…For tactical command and control, the objective of the BoT is a dynamic, flexible, and goal-driven networked battlefield environment. 16,17 In the future, smart things and machine intelligence will replace human authorities and war combatants in military 18 operations. The BoT will connect warfighters with advanced technology to improve armor, weapons, and other things to provide warfighters with enhanced sensory awareness, situational knowledge, prediction power in critical situations, and risk assessment.…”

Section: Blockchain For Botmentioning

confidence: 99%

Role of cybersecurity and Blockchain in battlefield of things

Sharma

Kumar

2023

Internet Technology Letters

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The Internet of Things is an essential component in the growth of an ecosystem that enables quick and precise judgments to be made for communication on the battleground. The usage of the battlefield of things (BoT) is, however, subject to several restrictions for a variety of reasons. There is a potential for instances of replay, data manipulation, breaches of privacy, and other similar occurrences. As a direct result of this, the implementation of a security mechanism to protect the communication that occurs within BoT has turned into an absolute requirement. To this aim, we propose a blockchain-based solution that is both safe and private for use in communications inside the BoT ecosystem. In addition, research is conducted on the benefits of integrating blockchain technology and cybersecurity into BoT application implementations. This work elaborates on the importance of integrating cybersecurity and blockchain-based tools, techniques and methodologies for BoT.

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“…This part is owing to this tremendous international digitalization. Consequently, this development leads to a collection of issues and significant impediments in safeguarding databases, whether for people, corporations, or governments, hurting their data security guided by M. Seete [5,6], Gangwar & Narang [7], Cybersecurity refers to the process of securing computer infrastructure from intrusion, theft, and harm caused by digital techniques. This entails adopting efforts to prevent and mitigate online threats including hacking, phishing, and malware.…”

Section: Introductionmentioning

confidence: 99%

Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

2024

IJIES

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Malware attacks have become a pressing concern in the domain of computer and communication systems, posing significant threats to data security and privacy. A practical approach is represented aiming to enhance the malware detection process with the help of Honeypot. A hybrid model of particle swarm optimization (PSO) integrated with Fuzzy_KNN algorithms is used in this research. Numerical simulations and mathematical analysis are conducted after developing numeric codes of this scheme. The performance and practicality are examined via these evaluation metrics including accuracy, precision, recall, and F1-score. Based on the numerical investigations, the findings confirmed satisfying performance measures of the hybrid model. The FuzzyKNN algorithm does attain the most remarkable effectiveness, achieving accuracy between 99.95% and 99.97%. This model employs a premium method of neighbourhood voting with an element of fuzziness and excels in large or complex datasets where patterns may emerge based on instance similarity.

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A Survey on Emerging Cyber Crimes and Their Impact Worldwide

Cited by 5 publications

References 4 publications

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Role of cybersecurity and Blockchain in battlefield of things

Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

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