Abstract
It is very demanding to classify and label data in machine learning algorithms manually. This works by analysing the probability of different words occurring in legit and spam mails and classifying them accordingly. This research used the notion of applying few labelled data to extrapolate and work on unlabelled data to attain high-accuracy classifiers. Many organizations, students and workplaces have recently used emails to communicate about current affairs, update people on events, etc. Unfortunately, spammers always find their way to exploit recipients by either flooding them with spam emails or sending spam messages periodically. Researchers started applying machine learning techniques to messages to detect if they were spam or not. This was met by the spammers fabricating standard mails. Most people regard spam mails as annoying emails repeatedly used for advertising products and brand promotion. Many such emails are blocked before they get into the user's inbox, but the spam menace is still prevalent. Some other spam mails pose a significant risk to users, like identity theft, containing malware and viruses used to hack people's gadgets, and spam that facilitates fraud. There is a need to create robust spam filters that can efficiently and correctly classify any incoming mail as either spam or not.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abhila B, Delphin Periyanayagi M, Koushika M, Joseph MN, Dhanalakshmi R (2021) Spam detection system using supervised ML. In: 2021 International conference on system, computation, automation and networking, ICSCAN 2021. https://doi.org/10.1109/ICSCAN53069.2021.9526421
Hosseinalipour A, Ghanbarzadeh R (2022) A novel approach for spam detection using horse herd optimization algorithm. Neural Comput Appl 2022:1–15. https://doi.org/10.1007/S00521-022-07148-X
Mashaleh AS, Binti Ibrahim NF, Al-Betar MA, Mustafa HMJ, Yaseen QM (2022) Detecting spam email with machine learning optimized with Harris Hawks optimizer (HHO) algorithm. Procedia Comput Sci 201(C):659–664. https://doi.org/10.1016/J.PROCS.2022.03.087
Baral MM, Mukherjee S, Chittipaka V, Jana B (2023) Impact of blockchain technology adoption in performance of supply chain. In: Blockchain driven supply chains and enterprise information systems. pp 1–20. https://doi.org/10.1007/978-3-030-96154-1_1
Faris H et al (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inform Fusion 48:67–83. https://doi.org/10.1016/J.INFFUS.2018.08.002
Roy R, Chekuri K, Sandhya G, Pal SK, Mukherjee S, Marada N (2022) Exploring the blockchain for sustainable food supply chain. J Inf Optim Sci 43(7):1835–1847. https://doi.org/10.1080/02522667.2022.2128535
Ahmed N, Amin R, Aldabbas H, Koundal D, Alouffi B, Shah T (2022) Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges. Sec Commun Netw https://doi.org/10.1155/2022/1862888
Roy R, Babakerkhell MD, Mukherjee S, Pal D, Funilkul S (2022) Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access 10:125666–125678. https://doi.org/10.1109/ACCESS.2022.3225555
Sharma VD, Yadav SK, Yadav SK, Singh KN, Sharma S (2021) An effective approach to protect social media account from spam mail—a machine learning approach. Mater Today Proc. https://doi.org/10.1016/J.MATPR.2020.12.377
Petersen LN (2018) The ageing body in Monty Python Live (Mostly). Eur J Cult Stud 21(3):382–394. https://doi.org/10.1177/1367549417708435
Bansal C, Sidhu B (2021) Machine learning based hybrid approach for email spam detection. In: 2021 9th international conference on reliability, infocom technologies and optimization (Trends and Future Directions), ICRITO 2021. https://doi.org/10.1109/ICRITO51393.2021.9596149
Mukherjee S, Baral MM, Pal SK, Chittipaka V, Roy R, Alam K (2022) Humanoid robot in healthcare: a systematic review and future research directions. In: 2022 International conference on machine learning, big data, cloud and parallel computing (COM-IT-CON), pp. 822–826. https://doi.org/10.1109/COM-IT-CON54601.2022.9850577
Raza M, Jayasinghe ND, Muslam MMA (2021) A comprehensive review on email spam classification using machine learning algorithms. In: International conference on information networking, vol. 2021, pp 327–332. https://doi.org/10.1109/ICOIN50884.2021.9334020
Roy R, Baral MM, Pal SK, Kumar S, Mukherjee S, Jana B (2022) Discussing the present, past, and future of Machine learning techniques in livestock farming: a systematic literature review. In: 2022 International conference on machine learning, big data, cloud and parallel computing (COM-IT-CON). pp. 179–183. https://doi.org/10.1109/COM-IT-CON54601.2022.9850749
Kontsewaya Y, Antonov E, Artamonov A (2021) Evaluating the effectiveness of machine learning methods for spam detection. Procedia Comput Sci 190:479–486. https://doi.org/10.1016/J.PROCS.2021.06.056
Srinivasan S, Ravi V, Alazab M, Ketha S, Al-Zoubi AM, Kotti Padannayil S (2021) Spam emails detection based on distributed word embedding with deep learning. Stud Comp Intell 919:161–189. https://doi.org/10.1007/978-3-030-57024-8_7/COVER
Kant Pal S, Mukherjee S, Baral MM, Aggarwal S (2021) Problems of big data adoption in the healthcare industries. Asia Pacific J Health Manag 16(4):282–287. https://doi.org/10.24083/apjhm.v16i4.1359
Blanzieri E, Bryl A (2008) A survey of learning-based techniques of email spam filtering. Artif Intell Rev 29(1):63–92. https://doi.org/10.1007/S10462-009-9109-6
Islam MK, al Amin M, Islam MR, Mahbub MNI, Showrov MIH, Kaushal C (2021) Spam-detection with comparative analysis and spamming words extractions. In: 2021 9th International conference on reliability, infocom technologies and optimization (Trends and Future Directions), ICRITO. https://doi.org/10.1109/ICRITO51393.2021.9596218
Trivedi SK (2016) A study of machine learning classifiers for spam detection. In: 2016 4th International symposium on computational and business intelligence ISCBI, pp 176–180. https://doi.org/10.1109/ISCBI.2016.7743279
Mukherjee S, Chittipaka V (2021) Analysing the adoption of intelligent agent technology in food supply chain management: an empirical evidence. FIIB Bus Rev 231971452110592. https://doi.org/10.1177/23197145211059243
Saleh AJ, et al (2019) An intelligent spam detection model based on artificial immune system. Information (Switzerland) 10(6). https://doi.org/10.3390/INFO10060209
Taloba AI, Ismail SSI (2019) An intelligent hybrid technique of decision tree and genetic algorithm for e-mail spam detection. In: Proceedings–2019 IEEE 9th international conference on intelligent computing and information systems, ICICIS 2019, pp 99–104. https://doi.org/10.1109/ICICIS46948.2019.9014756
bin Siddique Z, Khan MA, Din IU, Almogren A, Mohiuddin I, Nazir S (2021) Machine learning-based detection of spam emails. Sci Program 2021. https://doi.org/10.1155/2021/6508784
Hossain F, Uddin MN, Halder RK (2021) Analysis of optimized machine learning and deep learning techniques for spam detection. In: 2021 IEEE international IOT, electronics and mechatronics conference, IEMTRONICS 2021–Proceedings. https://doi.org/10.1109/IEMTRONICS52119.2021.9422508
Nayak R, Amirali Jiwani S, Rajitha B (2021) Spam email detection using machine learning algorithm. Mater Today Proc https://doi.org/10.1016/J.MATPR.2021.03.147
Nandhini S, Marseline DJ (2020) Performance evaluation of machine learning algorithms for email spam detection. In: International conference on emerging trends in information technology and engineering, ic-ETITE 2020. https://doi.org/10.1109/IC-ETITE47903.2020.312
Kumar N, Sonowal S (2020) Email spam detection using machine learning algorithms. In: Proceedings of the 2nd international conference on inventive research in computing applications, ICIRCA, pp. 108–113. https://doi.org/10.1109/ICIRCA48905.2020.9183098
Govil N, Agarwal K, Bansal A, Varshney A (2020) A machine learning based spam detection mechanism. In: Proceedings of the 4th international conference on computing methodologies and communication, ICCMC 2020, pp 954–957. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000177
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pal, S.K., Raffik, O.J., Roy, R., Jha, P.S. (2023). Machine Learning Methodology for the Recognition of Unsolicited Mail Communications. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-5085-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5084-3
Online ISBN: 978-981-99-5085-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)