Related papers: Machine Learning for E-mail Spam Filtering: Review…

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question…

This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our…

Users are the last line of defense as phishing emails pass filter mechanisms. At the same time, phishing emails are designed so that they are challenging to identify by users. To this end, attackers employ techniques, such as eliciting…

Social networking websites face a constant barrage of spam, unwanted messages that distract, annoy, and even defraud honest users. These messages tend to be very short, making them difficult to identify in isolation. Furthermore, spammers…

Spam messages muddle up users inbox, consume network resources, and build up DDoS attacks, spread malware. Our goal is to present a definite figure about the characteristics of spam and spam vulnerable email accounts. These evaluations help…

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network…

Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extensive research has focused on developing efficient machine…

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world…

Spam messes up user's inbox, consumes network resources and spread worms and viruses. Spam is flooding of unsolicited, unwanted e mail. Spam in blogs is called blog spam or comment spam.It is done by posting comments or flooding spams to…

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground…

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements…

Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such documents. After identifying and evaluating several quantifiable…

Removing the influence of a specified subset of training data from a machine learning model may be required to address issues such as privacy, fairness, and data quality. Retraining the model from scratch on the remaining data after removal…

Spam mitigation can be broadly classified into two main approaches: a) centralized security infrastructures that rely on a limited number of trusted monitors to detect and report malicious traffic; and b) highly distributed systems that…

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with…

With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today possibly due to the emergence of A2P bulk messaging. The effort…

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and…

This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks,…

Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on removing the influence of specific data samples from a trained…

Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant…