While antiphishing techniques have evolved over the years, phishing remains one of the most threatening attacks on current network security. This is because phishing exploits one of the weakest links in a network system—people. The purpose of this research is to predict the possible phishing victims. In this study, we propose the multidimensional phishing susceptibility prediction model (MPSPM) to implement the prediction of user phishing susceptibility. We constructed two types of emails: legitimate emails and phishing emails. We gathered 1105 volunteers to join our experiment by recruiting volunteers. We sent these emails to volunteers and collected their demographic, personality, knowledge experience, security behavior, and cognitive processes by means of a questionnaire. We then applied 7 supervised learning methods to classify these volunteers into two categories using multidimensional features: susceptible and nonsusceptible. The experimental results indicated that some machine learning methods have high accuracy in predicting user phishing susceptibility, with a maximum accuracy rate of 89.04%. We conclude our study with a discussion of our findings and their future implications.

Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites features and use third-party services to detect phishing sites. These techniques have some limitations, one of which is that extracting phishing features requires expertise and is time-consuming. Second, the use of third-party services delays the detection of phishing sites. Hence, this paper proposes an integrated phishing website detection method based on convolutional neural networks (CNN) and random forest (RF). The method can predict the legitimacy of URLs without accessing the web content or using third-party services. The proposed technique uses character embedding techniques to convert URLs into fixed-size matrices, extract features at different levels using CNN models, classify multi-level features using multiple RF classifiers, and, finally, output prediction results using a winner-take-all approach. On our dataset, a 99.35% accuracy rate was achieved using the proposed model. An accuracy rate of 99.26% was achieved on the benchmark data, much higher than that of the existing extreme model.

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Phishing is a very serious security problem that poses a huge threat to the average user. Research on phishing prevention is attracting increasing attention. The root cause of the threat of phishing is that phishing can still succeed even when anti-phishing tools are utilized, which is due to the inability of users to correctly identify phishing attacks. Current research on phishing focuses on examining the static characteristics of the phishing behavior phenomenon, which cannot truly predict a user’s susceptibility to phishing. In this paper, a user phishing susceptibility prediction model (DSM) that is based on a combination of dynamic and static features is proposed. The model investigates how the user’s static feature factors (experience, demographics, and knowledge) and dynamic feature factors (design changes and eye tracking) affect susceptibility. A hybrid Long Short-Term Memory (LSTM) and LightGBM prediction model is designed to predict user susceptibility. Finally, we evaluate the prediction performance of the DSM by conducting a questionnaire survey of 1150 volunteers and an eye-tracking experiment on 50 volunteers. According to the experimental results, the correct prediction rate of the DSM is higher than that for individual feature prediction, which reached 92.34%. These research experiments demonstrate the effectiveness of the DSM in predicting users’ susceptibility to phishing using a combination of static and dynamic features.

The aim of this study is to investigate the differences in the accumulation capacity of chrysolaminarin among six Tribonema species and to isolate this polysaccharide for immunomodulatory activity evaluation. The results showed that T. aequale was the most productive strain with the highest content and productivity of chrysolaminarin, which were 17.20% (% of dry weight) and 50.91 mg/L/d, respectively. Chrysolaminarin was then extracted and isolated from this alga, and its monosaccharide composition was mainly composed of a glucose (61.39%), linked by β-D-(1→3) (main chain) and β-D -(1→6) (branch chain) glycosidic bonds, with a molecular weight of less than 6 kDa. In vitro immunomodulatory assays showed that it could activate RAW264.7 cells at a certain concentration (1000 μg/mL), as evidenced by the increased phagocytic activity and upregulated mRNA expression levels of IL-1β, IL6, TNF-α and Nos2. Moreover, Western blot revealed that this polysaccharide stimulated the phosphorylation of p-65, p-38 and JNK in NF-κB and MAPK signaling pathways. Overall, these findings provide a reference for the further development and utilization of algae-based chrysolaminarin, while also offering an in-depth understanding of the immunoregulatory mechanism.