Introduction:

With the increasing challenge of span on the internet and the sophistication of different types of spams, there exist a growing urgency of evaluating the processes of efficiently and quickly identifying a wide number as well as types of spams present on the internet. With the advancement of cloud computing technology, the data storage and processing capacities are enriched as it is capable enough of calculating efficiently and successfully a large volume of e-mail data (Mobile Computing, 2023). On the other hand, because of the lifecycle and uncertainty associated with spams, there is an incorporated of re-judgement of feedback in the anti-spam systems whereas a text filtering system has been designed on the basis of active learning processes which include implementation of four different phases relating to filtering, training, feedback and re-filtering. When it comes to compare with the actual system, a text filtering system including feedback includes the capability of adding value to the filtering of a variety of keywords (Nagwani and Sharaff, 2017). For the sake of reducing efficiently the rate of misjudgment of normal e-mail data and enhancing the accuracy of a spam judgement system, it is essential to facilitate the application of weighted decision-making system associated with email header data for putting efficient auxiliary differentiation in place (Amayri and Bouguila, 2010). As far as an email running short of content is concerned, the text filtering process in relation to title weighting would be effective as well as feasible which would be capable enough of adding value to the process of identifying spams with considerably lesser data content. Since the filtering process on the cloud has been increasing advanced these days as compared to the conventional algorithms, the creation of internet is capable of solving efficiently and successfully the infinitely increasing number of spams (Delany et al.2012).

The concept of spam identification:

Spam identification process can be defined as a method of finding and filtering out all the unsolicited as well as unwanted messages, termed as spam emails, which are transferred to a user with multiple purposes like phishing, advertising or spreading malware and so on. The process of identifying spam emails therefore is recognised as a challenges and crucial operation the reason because these span messages are responsible for causing severe issues for the users, including wasting their resources and time, compromising the security and privacy and impacting their decision-making etc. (Mu, 2022).

Cloud computing system:

The advanced technology of cloud computing can be defined as a technology that is capable of offering access to scalable and shared computation resources like storage, servers, applications, networks etc. over the internet based on the demands of the users (Sajedi et al.2016). This technology includes the benefits in data storage and data processes with including the capability of effectively handling a sheer volume of e-mail data. Be that as it may, irrespective of the multiple advantages of cloud computing technology it comes across certain challenges in relation to compromising the reliability, integrity, confidentiality, security, privacy and availability of data (Delany et al.2012).

Text filtering:

Text filtering is nothing but a technique that is capable of analysing both the structure as well as context of an email data or text message followed by classifying them into diverse categories like legitimate or spam information. Additionally, text filtering technique involves the capability of making application of a range of approaches, such as keyboard-based, rule-based, machine learning-based or content-based approaches whereas this technique is effective enough of using a variety of features like non-textual as well as textual features in order to add value to the classification performance (Amayri and Bouguila, 2010).

The importance of spam detection in cloud computing employing a text filtering system:

The key objective of span detection in cloud computing platform using a text filtering system is to safeguard the users of the system from the compromise of their privacy and security by receiving scams and malicious information. Spam messages majorly contain phishing links, malware and other infected content that are responsible for infecting user systems and thereby stealing their personal detailed stored in these systems (Nagwani and Sharaff, 2017). Therefore, by detecting and blocking these types of malicious emails, organizations become capable of helping in protecting their consumers from these threats with adding value to the productivity of their systems. Spam messages on the other hand are responsible for wasting both the energy and time of the users and by differentiating and eliminating these malicious emails from the authorised ones, spam filters are capable of allowing the users to concentrate on the emails that are essential for them, leading to the reduction of information technology expenses (Abid et al.2022).

Then again, spam emails are responsible for disrupting the information technology resources including bandwidth and storage and by blocking these mails organizations become capable of minimising their IT costs with maintaining compliance with the applicable data protection regulations and legislations (Amayri and Bouguila, 2010). Additionally, spam detection in cloud computing using text filtering technology can be employed for detecting and blocking the advanced and sophisticated targeted phishing attacks these days which are nothing but a category of social engineering attempt where scammers attempt to deceive users into disclosing confidential information or opening a corrupted link. By detecting and blocking emails containing phishing content in a cloud computing environment, text filtering helps in protecting the users from these types of attacks (Mobile Computing, 2023).

Then again, text filtering technique helps identify and block malware containing messages in a cloud computing environment resulting in safeguarding users from malware attacks. Text filters are also installed in a system for identifying and blocking spam messages containing confidential data like consumer information or financial statement and thereby protecting an organization from potential security breaches (Nagwani and Sharaff, 2017). However, the text filtering systems must be updated regularly with the latest versions and advanced threat intelligence as well as algorithmic improvisations and upgrades to keep them ahead of the emerging spam methods. Therefore, text filtering plays a crucial role in spam detecting and blocking a cloud environment and thereby helping the organizations and individual users in maintaining the cybersecurity postures, reducing the cost of IT security and aligning with the data protection laws and regulations (Delany et al.2012).

The challenges associated with spam detection in a cloud computing environment depending on text filtering system:

When it comes to evaluate the challenge associated with spam detection in a cloud computing environment depending on text filtering system is can be said that there are complexities in relation to deciding the right approach to design and implement an efficient and robust system that would be capable of quickly as well as accurately detecting spam messages followed by differentiating them from the authorised data on the cloud computing platform (Mobile Computing, 2023). The system is supposed to be capable enough of coping with the adversarial and dynamic nature of spam data, such as the differentiation in the forms, types, techniques and contents of spam. In addition to that this system must also be capable of dealing with the problem associated with switch of the dataset which takes place under the changes in the process of distributing spam messages with time resulting in affecting both the reliability as well as the accuracy of the system (Sajedi et al.2016). Therefore, the system must be efficient of providing re-judgement and feedback in order to uplift the performance of the system and thereby the satisfaction level of the users of the system (Shafi’I et al.2017).

The benefits and drawbacks of using text filtering for detecting spams in cloud computing environment:

The potential benefits associated with using text filtering for detecting spams in cloud computing environment include it is capable of reducing the receiving of spam emails with remaining free from any type of scam contents or links. Additionally, assignment help Sheffield and the adaptability of a text filtering system to real-time upgrades helps the system response promptly as soon as it detect a new pattern of spam, adding value to the overall efficacy of the filtering procedure (Hu et al.2016). In addition to that, the text filtering technique is relatively faster and simpler as compared to the traditional approaches to spam filtering with including the capacity of innovating its features such as designing a schedule to receive or send a message and minimizing usage of bandwidth leading to potential cost reduction (Mu, 2022). On the other hand, the weaknesses associated with using text filtering for detecting spams in cloud computing environment include there are potential of false positives because of which authorized emails unintentionally get to be marked as spam messages. Then again, the rating of the system keeps on fluctuating since there are users that are still unaware of the application of text filtering technique resulting in mishandling feedback dropped by some clients relating to text filtering system. There exists a potential of crashing an entire text filtering system or slowing its use (Sajedi et al.2016).

Conclusion:

To enhance the accuracy of spam identification in cloud computing platform suing text filtering, an evolutionary algorithm can be used to optimize the system with using the button search algorithm from mobile computing and wireless communication. The approaches to add value to the performance of text filtering include application of accuracy optimization frameworks, use of autonomous learning and application of adaptive algorithm including feedback to feed the text filtering systems.

References:

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Amayri, O. and Bouguila, N., 2010. A study of spam filtering using support vector machines. Artificial Intelligence Review, 34, pp.73-108.

Delany, S.J., Buckley, M. and Greene, D., 2012. SMS spam filtering: Methods and data. Expert Systems with Applications, 39(10), pp.9899-9908.

Hu, W., Du, J. and Xing, Y., 2016, February. Spam filtering by semantics-based text classification. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) (pp. 89-94). IEEE.

Mobile Computing, W.C.A., 2023. Retracted:: Spam Identification in Cloud Computing Based on Text Filtering System.

Mu, R., 2022. [Retracted] Spam Identification in Cloud Computing Based on Text Filtering System. Wireless Communications and Mobile Computing, 2022(1), p.2309934.

Nagwani, N.K. and Sharaff, A., 2017. SMS spam filtering and thread identification using bi-level text classification and clustering techniques. Journal of Information Science, 43(1), pp.75-87.

Shafi’I, M.A., Abd Latiff, M.S., Chiroma, H., Osho, O., Abdul-Salaam, G., Abubakar, A.I. and Herawan, T., 2017. A review on mobile SMS spam filtering techniques. IEEE Access, 5, pp.15650-15666.

Sajedi, H., Parast, G.Z. and Akbari, F., 2016. Sms spam filtering using machine learning techniques: A survey. Machine Learning Research, 1(1), pp.1-14.