By: Navaneeth Jampula1
1Vel Tech University, Chennai, India
2International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan Email: navaneethjampula@gmail.com
Abstract
In this article we are going to see the different deep learning models which frontier in cybersecurity. Cybersecurity is the one of the fast-growing technology which is the practice of defending servers, mobiles devices, computers and electronic systems and data from malicious attacks. Let understand the concept of frontier in cybersecurity, deep learning models and there uses. As we see more people uses smartphone now a days, so there have more chances of cyberattacks even without knowing them. The article show the cybersecurity defense in its further advancements requires new computational tools, including generative AI for threat discovery, vulnerability assessment and a model refinement.
Keywords: Cybersecurity, Malicious attacks, Computational tools, AI, Deep Learning
Introduction
Over the past few years, there has been a surge in the frequency of networks’ attacks which has brought about several issues concerning to security. Smart network technologies that have recently appeared necessitate the creation of new approaches in the sphere of cybersecurity. What has been clearly seen is that it is extremely important to safeguard the key structures from any threats and intrusions. Cybersecurity is the science of protecting technologies, application and networks and restoring them after a disaster, operational security, and educating users on how to protect information [1]. Today, security threats in the cyber space area present some of the most threatening economic and national security threats and therefore requires the identification of motives for perpetrating cyber-crimes. Cybercrimes, or a warfare without firearms, in several ways can compromise one’s data, organization’s functionality, and place substantial burdens to countries’ economies. A cyberattack focuses on computers, networks and personal devices; can be untraceable; and aims at obtaining, changing or destroying a target.
Deep Learning approaches for cybersecurity
Deep Learning (DL) can be defined as a set of prediction models based on ANN and the latter can be described as a number of interconnected neurons transmitting data to each other. Comparing DNNs with simple single-hidden-layer NNs, the difference is in the depth of the network, that is the number of layers engaged in the pattern recognition step. A DNN comprises an input layer, one or many hidden layer/s, and an output layer. Every layer of a DNN is made up of neurons that transform the input into nonlinear outputs. The data passed through the layers is processed through neurons that produced the weighted sum of the input data which goes through various activation function in the hidden layers. The outputs are then forwarded to the last or output layer where the results are given [2], [3].
Cyber Security – Adversarial Learning in Action
Motivated by the fact that advanced adversarial learning methods (e.g., black-box attacks) have become quite effective, an increasing number of LIME-based algorithms are being adopted in practice and resulting in cyber security problems. The following presents some recent work that has been able to attack commercial products and web services in the real world, with high success rate [4].
Challenges
There are several challenges that can be faced in cyber security by deep learning models. For training deep learning models require a large amount of quality data, moreover the labeled datasets of cyber threats can be difficult [1]. Due to there complexity all deep learning models are seen like block boxes. There are several adversarial techniques that are used by the cyber attackers to fool deep learning techniques. And the important challenge for training and deploying deep learning models they require proper computational power [2].
Conclusion
This atricle has established that deep learning models are some of the benefits at the frontier of cybersecurity solutions. Such directions will be important in the case of development of deep learning and its application in the sphere of cybersecurity in the future where the issues connected with data quality, interpretability of models, or adversarial attacks have to be solved. Thus, with further evolution, deep learning has a potential to become one of the key solutions for a secure digital future.
References
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Cite As
Jampula N. (2024) Deep Learning Models: The New Frontier in Cybersecurity, Insights2Techinfo, pp.1