相关论文: Spam filter analysis

Email is a channel of communication which is considered to be a confidential medium of communication for exchange of information among individuals and organisations. The confidentiality consideration about e-mail is no longer the case as…

In this paper, we put forward a new tool, called SpaML, for spam detection using a set of supervised and unsupervised classifiers, and two techniques imbued with Natural Language Processing (NLP), namely Bag of Words (BoW) and Term…

The threat of phishing attacks in financial systems is continuously growing. Therefore, protecting sensitive information from unauthorized access is paramount. This paper discusses the critical need for robust email phishing detection.…

We address the problem of large scale real-time classification of content posted on social networks, along with the need to rapidly identify novel spam types. Obtaining manual labels for user-generated content using editorial labeling and…

Several machine learning schemes have attempted to perform the detection of spam messages. However, those schemes mostly require a huge amount of labeled data. The existing techniques addressing the lack of data availability have issues…

One of the key security threats on the Internet are the compromised machines that can be used to launch various security attacks such as spamming and spreading malware, accessing useful information and DDoS. Attackers for spamming activity…

Using multi group asymmetric public and private keys, this paper proposes a encryption email communication system, which makes email communication more secure, lowers the service provider\'s network and storage consumption, and completely…

Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather…

Phishing attacks have become a serious and challenging issue for detection, explanation, and defense. Despite more than a decade of research on phishing, encompassing both technical and non-technical remedies, phishing continues to be a…

Identifying deceptive content like phishing emails demands sophisticated cognitive processes that combine pattern recognition, confidence assessment, and contextual analysis. This research examines how human cognition and machine learning…

In this paper, a workflow for designing a bot using Robotic Process Automation (RPA), associated with Artificial Intelligence (AI) that is used for information extraction, classification, etc., is proposed. The bot is equipped with many…

The use of Artificial Intelligence (AI) to support cybersecurity operations is now a consolidated practice, e.g., to detect malicious code or configure traffic filtering policies. The recent surge of AI, generative techniques and frameworks…

Decentralized unpermissioned peer-to-peer networks are inherently vulnerable to spam when they allow arbitrary participants to submit content to a common public index or registry; preventing this is difficult due to the absence of a central…

As a major component of online crime, email-based fraud is a threat that causes substantial economic losses every year. To counteract these scammers, volunteers called scam-baiters play the roles of victims, reply to scammers, and try to…

Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link…

Despite sophisticated phishing email detection systems, and training and awareness programs, humans continue to be tricked by phishing emails. In an attempt to better understand why phishing email attacks still work and how best to mitigate…

Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as…

We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling…

Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing…

We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering,…