By: Dadapeer Agraharam Shaik Department of Computer Science and Technology, Student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu,517325, Andhra Pradesh.
Abstract:
Cloud security is rapidly evolving with integrated AI and Machine Learning, and their usage is initiating drastic changes in security solutions. The AI and the ML technologies provide the heightened features to detect the threats, respond to threats and predict the chance of security threats in real time. Using big data and complex mathematical algorithms organizations are capable of implementing more preventive and dynamic measures than with the old model. This article focuses on the use of Artificial Intelligence and Machine Learning in cloud environments, covering how they work, the advantages and disadvantages that are there, and how these innovative technologies can be used to protect cloud environment against continuously emerging threats.
1.Introduction
Regarding enterprises, cloud computing has changed the way enterprises work by providing variable resources. However, this has brought also new security threats and therefore, organizations must implement new technologies in order to secure the data and applications. AI and the related concept of ML have risen to become vital technologies for strengthening cloud security. Leveraging data analytics and artificial intelligence these technologies are more proactive as they identify: – Anomalies that indicate threats, as well as forecast threats and automatically counteract them.
The use of AI and ML helps to quickly process large volumes of data and determine that there are suspicious signs of threats. This capability is highly important in cloud environment which sees immense and constantly rising amount of data. In addition, it also indicates that such systems can develop from newly received data, which will make the operation of such systems more accurate.
AI and ML’s incorporation into cloud security initiatives envisions a stronger and more agile way to mitigate threats arising from the increasing advancement in cyber criminals and the demand on real time protection. In this article, the author discusses the use of AI and ML techniques and possibilities of applying them in strengthening the protection of cloud structures, showing how they have influenced the process of creating more effective cybersecurity tools.
2.The Evolving Threat Landscape
Accordingly, security threats in cyberspace are constantly changing, which poses a considerable problem for organizations that use the cloud for data storage and processing. To realize the importance of better security, one has to know this environment and the fact that modern security solutions have Artificial Intelligence (AI) or Machine Learning (ML) at their core.
Increasingly Sophisticated Attack Techniques: Todays hackers are smarter and operate using elaborate and diverse methods that might not easily be blocked by conventional security systems. The viruses and malwares that are being spread today are completely different from the viruses and malwares of the past, today’s attackers are wise and have adopted strategies that are almost impossible to counter.
Ransomware Attacks: Ransomware for instance this is where the attackers gains unauthorized access, encrypt the data belonging to an organization, and then demand for a ransom in order to release the decryption key. These attacks can result in huge losses, long hours of business interruption, and loss of important data affecting business operations as well as image.
Data Breaches: This central type of violate usually leads to the disclosure or theft of data, a situation known as data breach where an unauthorized person gains access to restricted data. Such can have severe ramification such as fines, loss of customer trust and facing the law. The sale of people’s and especially financial information is now one of the main sources of income for cybercriminals[1].
Distributed Denial of Service (DDoS) Attacks: A DDoS attacks seeks to overwhelm a host or network with traffic that prevents genuine users a chance to access it. These attacks have increased in both the size and the number of compromised hosts, which can range from dozens to thousands or even millions, depending on an attacker’s goal, with financial motivations or political/religious agendas as the possible reasons for the attack.
Reputation Damage: Other than financial effects, cyber threats pose a considerable threat to an organization’s image. Another consequence is that a leak of such events or the successful attack of ransomware may negatively affect the trust in customers and partners, repairing which can be a time-consuming and expensive process.
IoT Devices and Interconnectivity:The new avenue that has been created is the Internet of Technologies (Iot) devices presumably with low security. The current systems are complex and interrelated; therefore, if a weakness is found in one of the sectors, the whole operation could be put at risk which adds challenges for security practitioners.[2]
3.Cloud computing Security
In this section we describe the security and privacy challenges which are present in cloud computing at the moment. Cloud computing is a vast sector as it transmits and hosts service facilities on the Internet and takes the fee for its beneficial services according to the clients’ requirement. Thus, as more people and organizations use cloud services, the matter of cloud protection remains highly relevant.
Lacks in cloud computing generally stem from the trust deficit between the customers and the providers of the cloud. Customers may fear that the providers they’re using may have embedded certain policies they are unaware of and on the other hand providers may envisage their services being used for attacks. About the key factors for choosing a cloud provider the following can be mentioned expectations of the organization and facilities provided by the provider. Cloud computing threats, mainly known as security weaknesses, can be utilized to penetrate networks and obtain infrastructure assets. A cloud threat then refers to any undesirable event that could be either unintended or one that is orchestrated by an actor. An attack refers to crippling of the cloud resources while exploit means the negative impact on the availability and Return on Investment of cloud services[3].
This paper aims at describing possible attacks toward clouds and the measures that need to be taken to ensure that such threats do not cause harm to the cloud. Common attacks in cloud computing include Common attacks in cloud computing include:
- Denial of Service (DoS) Attack: The author’s effort aimed at influencing the availability of the service to the users. Distributed DoS attack also known as DDoS attack is a DoS attack in which several computers are used.
- Zombie Attack: An attacker sends a large number of requests from other legitimate nodes in the network and deviate from the regular usage of the cloud services.
- Phishing Attack: An effort to deceive people and get their data by leading them to a fake URL. It on the cloud an attacker might set up a phishing site to gain unauthorized access into other users’ accounts and services.
- Man-in-the-Middle Attack: An attacker gets between two users and can breach the information transit between two or more data canters in the cloud. [4]
Conclusion:
Of all the cloud computing technologies, Artificial Intelligence and Machine Learning have become critical in improving cloud security. With the ever-changing nature of threats that exist in the sphere of cybersecurity and the further development of complex cyber threats, using AI and ML as a part of the cloud security architecture is a good solution for detecting, analysing, and preventing threats. These technologies allow for observational analysis of an environment in real-time, coupled with real-time detection of an anomaly and or a likely threat which enhances the rate at which an organization can contain an emerging threat. Integrated with AI and ML, cloud security should shift from being of the reactive type and focus on preventing threats even before the events happen. This shift not only improves the safeguard mechanisms of cloud environments, but also increases the confidence level of users and organizations in cloud services. The most significant advantage of learning algorithms involved in AI and ML is the ability to learn continuously, and given this aspect, security measures are equally capable of learning from emerging threats hence giving an adaptive countermeasure.
Therefore, the incorporation of the AI and Machine Learning in the cloud security operations is more than a comfort but a requirement in the modern era. Therefore, as customers embrace the use of cloud services and as cyber threats evolve these new technologies will be crucial in protecting the data, privacy and the integrity of cloud services. The use of AI and ML in cloud security is therefore an excellent long-term approach aimed at offering protective security in the contemporary uncertain and evolving environment.
Reference:
- “Tabassum F, Rahaman M (2024) An Enhanced Multi-Factor Authentication and Key Agreement Protocol in Industrial Internet of Things, Insights2Techinfo. [Online]. Available: https://insights2techinfo.com/an-enhanced-multi-factor-authentication-and-key-agreement-protocol-in-industrial-internet-of-things/
- T. K. Vashishth, V. Sharma, K. K. Sharma, B. Kumar, S. Chaudhary, and R. Panwar, “Enhancing Cloud Security: The Role of Artificial Intelligence and Machine Learning,” in Improving Security, Privacy, and Trust in Cloud Computing, IGI Global, 2024, pp. 85–112. doi: 10.4018/979-8-3693-1431-9.ch004.
- “Rahaman M (2024) Foundations of Phishing Detection Using Deep Learning: A Review of Current Techniques, Insights2Techinfo. [Online]. Available: https://insights2techinfo.com/foundations-of-phishing-detection-using-deep-learning-a-review-of-current-techniques/
- A. B. Nassif, M. A. Talib, Q. Nasir, H. Albadani, and F. M. Dakalbab, “Machine Learning for Cloud Security: A Systematic Review,” IEEE Access, vol. 9, pp. 20717–20735, 2021, doi: 10.1109/ACCESS.2021.3054129.
- Gupta, B. B., & Narayan, S. (2021). A key-based mutual authentication framework for mobile contactless payment system using authentication server. Journal of Organizational and End User Computing (JOEUC), 33(2), 1-16.
- Vajrobol, V., Gupta, B. B., & Gaurav, A. (2024). Mutual information based logistic regression for phishing URL detection. Cyber Security and Applications, 2, 100044.
- Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of cutting-edge technologies for enterprise information system and management. Enterprise Information Systems, 17(11), 2197406
Cite As
Shaik D.A. (2024) AI and Machine Learning in Cloud Security, Insights2Techinfo, pp.1