The ISC International Journal of Information Security

Abstract Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify anomalies (fake nodes) in user accounts in a large-scale social network while handling imbalanced data issues. Despite the problem's complexity, the method achieves an AUC rate of 80\% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks. The findings of this study contribute to ongoing advancements in fake account detection, offering a hopeful solution for securing online spaces against fraudulent activities and anomaly detection in social networks.

Abstract Detecting Advanced Persistent Threats (APTs) is crucial, and a practical approach involves using an intrusion detection system (IDS) integrated with supervised machine learning algorithms. These algorithms require a balanced dataset with ample attack samples to learn and recognize attack patterns effectively. However, widely used APT datasets, such as DAPT2020 and SCVIC-APT-2021, suffer from imbalance issues that limit the performance of machine learning-based intrusion detection systems (IDS). We introduce DSRL-APT-2023, a new balanced synthetic APT dataset generated using CTGAN to address this challenge. The CTGAN model is trained on the DAPT2020 dataset to create this balanced dataset. We evaluate and compare the performance of six standard supervised machine learning algorithms—Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Random Forest, and Multi-Layer Perceptron— alongside an intrusion detection system (IDS) called Intelligent Intrusion Detection System, which is based on tree-structured machine learning models. Our evaluation focuses on detecting attacks in DSRL-APT-2023 and compares its performance to DAPT2020 and SCVIC-APT-2021. Additionally, we assess the data quality of synthetic datasets generated by two prominent GANs, CopulaGAN, and CTGAN, with CTGAN demonstrating slightly superior performance in generating high-quality tabular data. Our results demonstrate that machine learning algorithms and the Intelligent IDS can accurately detect attacks in the synthetic dataset, as evidenced by the F1-Score metrics.