Introduction:
When an email account is affected by spam, in most cases, that email id got purchased either by a company or person as part of a list. Additionally, it might be hacked by the cybercriminals that gained access to the list of email addresses of the clients (Sheneamer, 2021). The malicious actors the span ends up sending the same email to multiple persons at the same time, considering that if the email is going to work even on only millions of people, the marketing scheme or attack is going to be successful. Since email spam filtering is capable of recognizing those types of malicious emails, it presents an effective solution to safeguard users from suspicious messages (Kumar & Mittal, 2020). In order to add value to the level of cyber protection, certain spam filters employ insights achieved from machine learning for targeting junk mail even more accurately. At times, an internet service provider uses span filters aiming at reducing the volume of spam transferred to the users whereas the business is capable of doing the same for the sake of reducing the burden of spam on their staff. However, a spam filter also is capable of flagging legitimate emails from the users of businesses, looking forward to receiving messages (Sheneamer, 2021). Under this situation, a user often gets the option for determining the types of emails they would like to receive and can adjust their settings accordingly for allowing them to pass through the spam filter.
The concept of spam filtering and the way it works:
A spam filter can be described as a program employed for detecting virus-infected, unwanted and unsolicited emails and preventing these messages from receiving in the inbox of a user. Additionally, like the other types of filtering applications, a span filter searches for some critical criteria on which to base its examination on (Gangavarapu, Jaidhar & Chanduka, 2020). However, internet service providers, essay typer, businesses and free email services continue using email spam filtering tools in order to reduce the threat of spreading spam (Kumar & Mittal, 2020). For instance, one of the earliest as well as simplest versions of spam filtering, such as the one that got employed by Microsoft’s Hotmail, got set to search for specific expressions belonging to the subject lines of an email. Then again, an email got excluded from the inbox of the users whenever the filter analyzed one of those pre-defined expressions (Thirupurasundari, 2021).
However, this approach would not essentially be effective and at times ends up omitting legitimate emails also which are termed as false positives, whilst allowing the real spam emails to be received in the user inbox (Sheneamer, 2021). Therefore, write my dissertation and sophisticated programs like Bayesian filters as well as different types of heuristic filters should be employed for identifying spam content by detecting the suspicious patterns of words and the frequency of words (Manaa, Obaid & Dosh, 2021). For doing so they learn the preferences of the users depending on the emails pointed out as spam followed by the creation and application of rules by spam software to the future messages that target the inbox of the users. For instance, whenever a user marks emails from a particular sender as spam, the Bayesian filter starts recognizing the pattern with move automatically the future emails from that specific sender to the spam folder (Kumar & Mittal, 2020). On the other hand, internet service providers employ span filters both for outbound as well as inbound emails whereas the SMEs generally take account into the inbound filters for protecting their business networks. There are multiple spam filtering solutions available in the contemporary industry which are hosted on servers and also in the cloud with being integrated into email software like Microsoft Outlook (Dada et al.2019).
Different types of spam filters:
Blocklist filters:
These spam filters are responsible for blocking spam messages from the senders who have been incorporated into the list of comprehensive spammers. Additionally, these spam filters get frequently updated for keeping up with the sophisticated spammers that continue changing their email addresses quite quickly (Gangavarapu, Jaidhar & Chanduka, 2020). However, Research proposal writing service and spammers change their domain of email, the email may be capable of tricking the system by penetrating the filter till it gets detected once again as spam. The businesses build their individual blacklist filter in order to safeguard their organizational interests (Kumar & Mittal, 2020). For instance, they end up blocking headhunters that look for poaching their best employees for the betterment of the competitors. Additionally, they block the emails that are responsible for wasting the time and effort of their employees like emails including special deals and offers (Manaa, Obaid & Dosh, 2021).
Content filters:
These spam filters work by examining the content comprised by every email with using that information for deciding whether or not it is spam. Those filters continue working since spam email content often is predictable, including deals, targeting the fundamental feeling of humans or promoting explicit content (Jawale et al.2018). These kinds of spammers look forward to using target expressions such as discounts and special offers, multiple times which would trigger the content filters. Certain businesses likewise employ content filters for examining emails for wrong languages with blocking them accordingly (Thirupurasundari, 2021).
Header filters:
These spam filters are responsible for analyzing the headers of the emails for determining whether they developed from an authorized source (Karim et al.2019). For example, IP addresses are considered as frequently employed by the spammers whereas information indicating that a message got a part of several emails sent at the same time to the preselected recipients (Kumar & Mittal, 2020).
Language filters:
It is noticed that in most cases the spammers target individuals across the world and at times end up sending emails from different geographical locations where a different language is used than the native language of the recipient (Govil et al.2020). Therefore, language filters are responsible for helping in blocking those emails, however, if an enterprise gets a global consumer base, it would run the risk associated with consumer questions from a different country getting straight to the language filter. In such cases, it helps always in checking the spam folder when the company expect to receive those messages from its global market (Manaa, Obaid & Dosh, 2021).
Rule-based filters:
Rule-based filters are responsible for enabling the users to establish particular rules and making applications of the same to all their incoming emails. It is noticed that whenever the email content corresponds to one of those guidelines or rules, it forwards automatically the email to a spam folder (Karim et al.2019). It is worth mentioning in this context that the rules would include specific phases or words in the header or message. In addition to that, this kind of spam filter often is used frequently by users that receive unnecessary emails in relation to membership since rule-based filters are also capable of targeting specific senders (Rusland et al.2017).
Bayesian filter:
A Bayesian spam filter is capable of learning the preferences of the users by examining the emails that they send to spam. In addition to that it is responsible for observing the content of the emails that are marked by the users as spam followed by setting up guidelines accordingly (Jawale et al.2018). Those guidelines or rules that are applied to future emails tend to get into the inbox of the users (Sheneamer, 2021). For instance, if a user mark constantly all emails from a particular sender as spam, this filter is capable of recognizing this pattern and then it is going to search for emails from that specific sender by moving them to the spam folder automatically (Karim et al.2019).
The way malware filters work:
Malware filters are concentrated on detecting as well as blocking the spam emails that comprise malicious attachments or corrupted software resulting in helping in preventing the users from downloading and running those programs inadvertently (Thirupurasundari, 2021). Additionally, the techniques that are employed in malware filtering would include:
Signature-based detection:
This process compares the email content and attachments against a database associated with known malware signatures for the sake of identifying and blocking them (Govil et al.2020).
Heuristic analysis:
This process involves scanning the email content and attachment for suspicious characteristics or behaviour of the sender of the emails which would end up indicating the presence of malware (Gangavarapu, Jaidhar & Chanduka, 2020).
Sandboxing:
This process is responsible for operating suspicious files or attachments received with emails within a controlled environment in order to observe and analyse their behaviour followed by determining whether or not they are malicious (Jawale et al.2018).
Link analysis:
This process is responsible for examining the links received with the emails in order to identify as well as block those, leading to known corrupted websites (Govil et al.2020).
Real-time updates:
This process of malware spam filtering continues to update regularly the filtering system using the latest information in relation to the emerging threats associated with malware (Gangavarapu, Jaidhar & Chanduka, 2020).
Conclusion:
In the end, it is concluded that malware and spam filters are effective but they are responsible for occasionally blocking legitimate emails received from authorised sources or allowing mistakenly malware or spam to get into the inbox of the users. These situations are known as false negatives or false positives. However, in order to reduce false positives, a number of filter software systems include review options for the users so that they can review all the blocked emails and can mark them as legitimate accordingly. It is worth mentioning that the efficacy and success of malware as well as spam filtering would differ on the basis of the client or the email service being employed. It is noticed that some services include increasingly advanced and high-end filtering capacities whilst the rest may depend on 3rd party solutions. However, the users are provided with the opportunity to adjust the sensitivity of the malware or spam filters taking account into their needs and preferences.
References:
Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O., & Ajibuwa, O. E. (2019). Machine learning for email spam filtering: review, approaches and open research problems. Heliyon, 5(6), e01802. https://www.sciencedirect.com/science/article/pii/S2405844018353404
Gangavarapu, T., Jaidhar, C. D., & Chanduka, B. (2020). Applicability of machine learning in spam and phishing email filtering: review and approaches. Artificial Intelligence Review, 53, 5019-5081. https://link.springer.com/article/10.1007/s10462-020-09814-9
Govil, N., Agarwal, K., Bansal, A., & Varshney, A. (2020, March). A machine learning-based spam detection mechanism. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 954-957). IEEE. https://ieeexplore.ieee.org/abstract/document/9076360/
Jawale, D. S., Mahajan, A. G., Shinkar, K. R., & Katdare, V. V. (2018). Hybrid spam detection using machine learning. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2), 2828-2832. https://www.academia.edu/download/57544996/V4I2-1925.pdf
Karim, A., Azam, S., Shanmugam, B., Kannoorpatti, K., & Alazab, M. (2019). A comprehensive survey for intelligent spam email detection. IEEE Access, 7, 168261-168295. https://ieeexplore.ieee.org/abstract/document/8907831/
Kumar, S., & Mittal, S. K. (2020). Email Spam and Malware Filtering Using Machine Learning and Its Applications. In Performance Management (pp. 25-32). CRC Press. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003089308-3/email-spam-malware-filtering-using-machine-learning-applications-sachin-kumar-sandeep-kumar-mittal
Manaa, M., Obaid, A., & Dosh, M. (2021). Unsupervised approach for email spam filtering using data mining. EAI Endorsed Transactions on Energy Web, 8(36). https://eudl.eu/doi/10.4108/eai.9-3-2021.168962
Rusland, N. F., Wahid, N., Kasim, S., & Hafit, H. (2017, August). Analysis of Naïve Bayes algorithm for email spam filtering across multiple datasets. In IOP conference series: materials science and engineering (Vol. 226, No. 1, p. 012091). IOP Publishing. https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012091/meta
Sheneamer, A. (2021). Comparison of Deep and Traditional Learning Methods for Email Spam Filtering. International Journal of Advanced Computer Science and Applications, 12(1). https://pdfs.semanticscholar.org/d319/43a104227f0e56cf97e73914010c96cc50c7.pdf
Thirupurasundari, D. R. (2021). Efficient Modelling and Analysis on the Propagation Dynamics of Email Malware Filtering for Sustainable IT Development. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 4346-4354. https://turcomat.org/index.php/turkbilmat/article/view/6564