5 research outputs found
Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text
Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf Vectorizer the best to all results than count Vectorizer
A framework for securing email entrances and mitigating phishing impersonation attacks
Emails are used every day for communication, and many countries and
organisations mostly use email for official communications. It is highly valued
and recognised for confidential conversations and transactions in day-to-day
business. The Often use of this channel and the quality of information it
carries attracted cyber attackers to it. There are many existing techniques to
mitigate attacks on email, however, the systems are more focused on email
content and behaviour and not securing entrances to email boxes, composition,
and settings. This work intends to protect users' email composition and
settings to prevent attackers from using an account when it gets hacked or
hijacked and stop them from setting forwarding on the victim's email account to
a different account which automatically stops the user from receiving emails. A
secure code is applied to the composition send button to curtail insider
impersonation attack. Also, to secure open applications on public and private
devices
Formalizing Stack Safety as a Security Property
The term stack safety is used to describe a variety of compiler, runtime, and hardware mechanisms for protecting stack memory. Unlike “the heap,” the ISA-level stack does not correspond to a single high-level language concept: different compilers use it in different ways to support procedural and functional abstraction mechanisms from a wide range of languages. This protean nature makes it difficult to nail down what it means to correctly enforce stack safety