3 research outputs found
Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques
Cybercrime is a growing threat to organizations and individuals worldwide,
with criminals using increasingly sophisticated techniques to breach security
systems and steal sensitive data. In recent years, machine learning, deep
learning, and transfer learning techniques have emerged as promising tools for
predicting cybercrime and preventing it before it occurs. This paper aims to
provide a comprehensive survey of the latest advancements in cybercrime
prediction using above mentioned techniques, highlighting the latest research
related to each approach. For this purpose, we reviewed more than 150 research
articles and discussed around 50 most recent and relevant research articles. We
start the review by discussing some common methods used by cyber criminals and
then focus on the latest machine learning techniques and deep learning
techniques, such as recurrent and convolutional neural networks, which were
effective in detecting anomalous behavior and identifying potential threats. We
also discuss transfer learning, which allows models trained on one dataset to
be adapted for use on another dataset, and then focus on active and
reinforcement Learning as part of early-stage algorithmic research in
cybercrime prediction. Finally, we discuss critical innovations, research gaps,
and future research opportunities in Cybercrime prediction. Overall, this paper
presents a holistic view of cutting-edge developments in cybercrime prediction,
shedding light on the strengths and limitations of each method and equipping
researchers and practitioners with essential insights, publicly available
datasets, and resources necessary to develop efficient cybercrime prediction
systems.Comment: 27 Pages, 6 Figures, 4 Table
Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails
Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches