The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. This work's significance lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

Phishing is a set of devastating techniques which lure target users to provide critical resources. They are successful because they rely on human weaknesses. Gamification which is a recent and non-traditional learning method with purpose to motivate and engage user to carry out activities, is more and more applied to prevent such cyber threats. This paper provides the first survey of gamified solutions dedicated to educate against phishing from 2007 to 2019. The investigation is conducted on eight proposals in terms of core concepts, game mechanics and learning process. We provide three taxonomies of dimensions to systematically characterize researches on gamified solutions, discuss lacks of surveyed works and opens further orientations to enhance this research area. Some key results are: solutions do not consider elementary level of knowledge and do no offer basic notions; solutions are not adapted to general audience and therefore not reliably applicable in different contexts; platforms partially educate about phishing; learners are evaluated predictably and within a short period. This study constitutes a cornerstone to understand and enhance research on phishing education.

Phishing emails pose a significant threat to cybersecurity worldwide. There are already tools that mitigate the impact of these emails by filtering them, but these tools are only as reliable as their ability to detect new formats and techniques for creating phishing emails. In this paper we investigated how traditional models and transformer models work on the classification task of identifying if an email is phishing or not. We realized that transformer models, in particular DistilBERT, BERT, and RoBERTa had a significantly higher performance compared to traditional models like Logistic Regression, Random Forest, Support Vector Machine, and Naive Bayes. The process consisted in using a large and robust dataset of emails and applying preprocessing and optimization techniques to maximize the best result possible. roBERTa showed its outstanding capacity to identify phishing emails by achieving the maximum accuracy of 0.9943. Even though they were free successful, traditional models performed marginally worse; SVM performed the best, with an accuracy of 0.9854. The results emphasize the value of sophisticated text processing methods and the possibility of transformer models to improve email security by thwarting phishing attempts.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual's susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose purpose is to compromise digital assets of other members by using messages as an attack vector to execute different variants of cyberattacks against them. Therefore, this work presents an approach based on Natural Language Processing tools and a Convolutional Neural Network architecture to detect and classify, on social network messages, four types of cyberattacks, such as malware, phishing, spam, and even one whose purpose is deceiving the user into spreading malicious messages to other users, which in this work is identified as bot attacks. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent from particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first detects the existence of any of the four cyberattacks within the message, obtaining an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage is to classify it into one of the four types of cyberattack, achieving an accuracy value of 0.82.

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-theart machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack

Machine learning (ML) is changing cybersecurity by enabling progressed discovery, anticipation and reaction instruments. This paper gives a comprehensive survey of ML's part in cybersecurity, looking at both hypothetical systems and down to earth usage. It diagrams the rising dangers focusing on ML models, such as ill-disposed assaults, information harming and show reversal assaults and examines state-of-the-art defense procedures, counting ill-disposed preparing, vigorous models and differential protection. Furthermore, the paper investigates different ML applications in cybersecurity from interruption location to malware classification, highlighting their affect on improving security measures. An peculiarity induction calculation is proposed for the early discovery of cyber-intrusions at the substations. Cybersecurity has ended up a imperative investigate range. The paper concludes with a discourse on the key inquire about bearings and best hones for making secure and versatile ML frameworks in a data-driven world. This paper dives into how Machine Learning (ML) revolutionizes cybersecurity, enabling progressed discovery, avoidance, and reaction components. It offers a exhaustive investigation of ML's urgent part in cybersecurity, enveloping hypothetical systems and viable applications. It addresses rising dangers like ill-disposed assaults and information harming, nearby cutting-edge defense techniques such as antagonistic preparing and strong models.

As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.