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Article Citation - WoS: 11Citation - Scopus: 24The Effect of Social Media User Behaviors on Security and Privacy Threats(Ieee-inst Electrical Electronics Engineers inc, 2022) Cengiz, Aslihan Banu; Kalem, Guler; Boluk, Pinar SarisarayThe number of online social network (OSN) users is increasing daily and attacks and threats against over the time spent on online networks has been increasing equally. Attacks against OSN users exploit not only system vulnerabilities but also user-induced vulnerabilities, which naturally affect the hacker's attack strategy as well. This study is designed to investigate the effect of social media user behaviors on their vulnerability level in terms of security and privacy. The study was conducted survey methods, which was applied to social media users in two countries - Turkey and Iraq. This study documents and analyzes the behaviors of 700 OSN users in two countries. This study examines the behaviors of social media users from two nationalities, investigating whether there is a relationship between social media users' behaviors and security and privacy threats. Research findings demonstrate that there is a significant relationship between OSN users' behaviors and their attitudes towards security and privacy. Additionally, Turkish social media users pay more attention to their behaviors in terms of privacy and security awareness than Iraq users.Article Citation - WoS: 38Citation - Scopus: 72Adopting Automated Whitelist Approach for Detecting Phishing Attacks(Elsevier Advanced Technology, 2021) Azeez, Nureni Ayofe; Misra, Sanjay; Margaret, Ihotu Agbo; Fernandez-Sanz, Luis; Abdulhamid, Shafi'i MuhammadPhishing is considered a great scourge in cyberspace. Presently, there are two major challenges known with the existing anti-phishing solutions. Low detection rate and lack of quick access time in a real-time environment. However, it has been established that blacklist solution methods offer quick and immediate access time but with a low detection rate. This research paper presents an automated white-list approach for detecting phishing attacks. The white-list is determined by carrying out a detailed analysis between the visual link and the actual link. The similarities of the known trusted site are calculated by juxtaposing the domain name with the contents of the whitelist and later match it with the IP address before a decision is made and further analyzing the actual link and the visual link by calculating the similarities of the known trusted site. The technique then takes a final decision on the extracted information from the hyperlink, which can also be obtained from the web address provided by the user. The experiments carried out provided a very high level of accuracy, specifically, when the dataset was relatively at the lowest level. Six different datasets were used to perform the experiments. The average accuracy obtained after the six experiments was 96.17% and the approach detects phishing sites with a 95.0% true positive rate. It was observed that the level of accuracy varies from one dataset to another. This result shows that the proposed method performs better than similar approaches benchmarked with. The efficiency of the approach was further established through its computation time, memory, bandwidth as well as other computational resources that were utilized with the minimum requirements when compared with other approaches. This solution has provided immense benefits over the existing solutions by reducing the memory requirements and computational complexity, among other benefits. It has also shown that the proposed method can provide more robust detection performances when compared to other techniques. (c) 2021 Elsevier Ltd. All rights reserved.
