Online Social Networks (OSNs) face various security threats, including account compromisation, where attackers seize control over legitimate user accounts and create fake profiles for nefarious purposes. The dynamic and open nature of OSNs presents unique challenges for cybersecurity, particularly in detecting unauthorized access and malicious activities such as phishing attacks, spamming, and spreading misinformation associated with account compromisation. Traditional intrusion detection systems (IDS) in OSNs often miss attacks or generate false positives due to static thresholds, delayed responses, and poor real-time data handling. These limitations often result in missed detections or false positives during sudden shifts in user activity patterns or emerging attack vectors. We introduce$RACEMAN$, an adaptive IDS designed explicitly for the OSN environment to address this. To enhance adaptability,$RACEMAN$incorporates emergency strategies such as dynamic threshold adjustments based on real-time network traffic analysis and early stopping mechanisms triggered by anomalous behavior spikes, enabling rapid adaptation to changing threat landscapes.$RACEMAN$leverages real-time OSN interactions to continuously update its metamorphic relations, ensuring an up-to-date understanding of normal user behaviour versus potential intrusions. This system utilises advanced semantic analysis to accurately represent user interactions. It generates diverse test cases using genetic algorithms and reinforcement learning to simulate user scenarios and potential intrusion methods. These test cases undergo input transformations to realistically mimic intrusion attempts while maintaining semantic integrity. The system's responses to these test cases are evaluated against expected behaviours defined by the updated metamorphic relations.$RACEMAN$utilizes statistical analysis, Multi-view Convolutional Neural Networks (MVCNN), and rule-based systems for intrusion classification. Our collaborative and distributed IDS approach enhances detection capabilities by promoting knowledge sharing across multiple systems and ensuring scalability without central points of failure. We evaluated$RACEMAN$using six publicly available datasets from Facebook, Google+, Twitter, linkedIn, Youtube and Reddit where it demonstrated a high accuracy rate of 98.85%, outperforming other models such as Convolutional Neural Network (CNN-85.67%), Artificial Neural Network (ANN-86.63%), and Random Forest (RF-78.26%).

Online Social Networks (OSNs) face escalating security threats that imperil user privacy. Conventional Deep Learning methods, relying predominantly on fixed learning rates, encounter limitations when capturing the nuanced intricacies of OSN traffic that arise from shifting user behaviors, diverse content types, and evolving interaction patterns because of social trending topics changes. To tackle these challenges, our paper delves into the diverse variations and transitions from a uniform approach, where a single method is employed for various types of data, to a multi-variation methodology. This methodology dynamically adapts to the special characteristics of each data type, resulting in more effective data representation while alleviating the limitations associated with fixed-rate calibration. Therefore, we devise the Adaptive Swarm Reinforcement Learning (ASRL) method that leverages adaptive learning to intricately analyze a wide range of user interactions, endowing our proposed method with the capacity to flexibly adjust to the constantly shifting OSN patterns. The experiments show that the proposed ASRL method achieves an accuracy of 98.59% in detecting a range of threat patterns, surpassing other prevalent methods by an average of 5% across the datasets from Facebook, Google+, and Twitter. Meanwhile, ASRL logs suspicious activities to identify the intruder for forensic analysis. The implementation of our proposed method is now publicly accessible athttps://github.com/don2c/asrl_Project.

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and e-commerce. However, it raises several questions about privacy, especially for users who prefer to remain anonymous, especially on online social networks (OSNs). Moreover, due to the commercialization of online users' data, some service providers sell users' data to third parties at the blind side of the users, which leads to trust issues between users and service providers. Such matters call for a system that gives online users much-needed control and autonomy of their data. With the advancement of blockchain technology, many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and hacks. To resolve these limitations, we propose RecGuard, a privacy preservation blockchain-based network system. We developed two smart contracts, RG-SH and RG-ST, to ensure the security and privacy of user data. The RG-SH manages user data, whereas the RG-ST stores data. A graph convolutional network (GCN) was integrated with the blockchain-based system to detect malicious nodes. Finally, we implemented our framework prototype on a locally simulated network. The analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.

The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with feature selection. Research available on supervised learning has limitations with feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this article discusses the various behaviors of OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by previous schemes. We detailed our proposed optimized nonsymmetric deep autoencoder (OPT_NSDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using three different social network datasets, Facebook, Google+, and Twitter, in addition to the NSL-KDD and KDDCUP’99 datasets, in a graphical-user-interface-enabled Weka application. The experimental results show that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection.