By: Achit Katiyar1
1 International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, achitktr@gmail.com
Abstract: It has been identified that there is a continuous rise in the level of threats: the two primary areas of concern are phishing attacks and block-chain systems. In particular, traditional methods of defending against these threats frequently fail because they are not suited for changing methods of the attack. It was found that Genetic Algorithms (GAs) and Data Analytics (DA) are two effective methods to improve security in such domains. To that end, this paper aims at giving an insight on what use GAs can be put to in enhancing security models, and DA in the pursuit and prevention of phishing attacks, and buildings up block chain. GAs and DA gives the best solutions with adaptively and efficiently in the area of cyber threats for enhancing the modern systems.
Introduction
The incorporation of genetic algorithms and data analytics within the cyber-safety approach against phishing and in the blockchain systems constitutes great advancement in securing the cyberspace. Genetic algorithms are parallel to natural selection and enhance efficiencies on parameters like feature selection which makes it easy to combat phishing threats [1]. Also, because it is a distributed form of organization, when it is combined with machine learning, blockchain technology can provide security against certain attacks on data transactions like ransomware [2]. Besides that, new algorithms such as DNA encryption algorithms, are also believed to enhance the prospective directions of legislation protective measures on foreign countries [3]. This multi-branched strategy brings together the potential development of the future of both pursues that is, genetic algorithms and data Analytics in the creation of effective anti-cybercrime systems in which emerging trends would form a part of it.
- Genetic Algorithms in Phishing Detection:
- Genetic algorithms select the best features, which are important in enhancing the performance of machine learning models in the context of the enhanced phishing detection systems [1].
- This method eliminates over training hence improving the performance of the model making the strategy useful in real-time threat detection.
- Blockchain and Cybersecurity:
- Smart contract also gives blockchain a secure and tamper-proof record of data exchanges perfect for safeguarding data [2].
- This is because when combined with other technologies, blockchain can successfully combat forms of cyberattacks such as ransomware using machine learning algorithms.
- DNA Encryption in Cyber Security:
- Proposed method for DNA encryption methods provide potential advantages for data protection and, in general, may dramatically transform the approaches used in protecting disparate kinds of information in various computer systems and networks [3].
- This implementations in industries including health, the algorithms may well be employed in developing trust and security of physical and data assets [4].
Thus, the opportunities of developing new algorithms that would solve the problems of cybersecurity are still remain an issue, including the implementation of a genetic algorithm and the incorporation of blockchain technology resources into existing systems. Because the threat landscape changes continuously, the development of these technologies requires constant study and improvement of new applications to provide effective protection.
Integration of Genetic Algorithms and Data Analytics for Enhanced Cybersecurity
The use of GA and data analysis greatly expands cybersecurity by improving the search for and response to threats. This approach builds on the typical applications of GAs in feature selection and optimization and advanced data analytics methodology to increase the reliability of cybersecurity frameworks.
- Enhanced Threat Detection:
- When combined with neural networks, GAs should provide enhanced attribute detection in relation to cyber threats like DDoS and malware to increase the detection rate [5].
- In phishing detection systems, GAs help choose the best features by optimizing the performance of a machine learning model and also minimizing the computations needed [1].
- Robust Data Security:
- Appending cryptographic functions to genetic operators enhances the security of the databases that contain the data by providing confidentiality and addressing the risks posed by the gene operators [6].
- Sophisticated data analysis which includes Machine Learning can identify signs of cyber threats that can be useful in responding to threats [7].
Despite the direction of novel innovations in enhanced cybersecurity through the combination of GAs and data analytics, issues like data privacy and biases of algorithms must be overcome for the best delivery of techniques.
Conclusion
Computer threats are evolving at a high speed especially in the areas of phishing and blockchain systems meaning that intelligent solutions are required. Genetic Algorithms along with Data Analytics if combined holds a better potential to strengthen the security systems of any firm. What they contribute is that GAs can adapt the alarms systems and the security countermeasures and DA provides real-time analysis and prediction. The ongoing roll out of the above technologies is very strategic to the augmentation of more enhanced cybersecurity systems to meet current and future threats.
References
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Cite As
Katiyar K. (2024) Genetic Algorithms and Data Analytics for Cybersecurity in Phishing and Blockchain Systems, Insights2Techinfo, pp.1