The use of online social networks, such as Facebook and Twitter, has grown at a phenomenal rate. These platforms offer services that support interactions via messaging, chatting or audio/video conferencing, and also sharing of content. Most, if not all, of these platforms use centralized computing systems; therefore, the control and management of the systems lies entirely in the hands of one provider, who must be trusted to treat the data and communication traces securely. As a zero-trust alternative, peer-to-peer (P2P) technologies promise to support end-to-end communication, uncompromising access control, anonymity and resilience against censorship and massive data leaks through misused trust. The goals of this survey are threefold. First, the survey elaborates the properties of P2P-based online social networks and defines the requirements for such (zero-trust) platforms. Second, it gives an exposition of the building blocks for P2P frameworks that allow the creation of such sophisticated and demanding applications, such as user/identity management, reliable data storage, secure communication, access control and general-purpose extensibility, which are not properly addressed in other P2P surveys. As a third point, it gives a comprehensive analysis of proposed P2P-based online social network applications, frameworks and architectures by exploring the technical details, inter-dependencies and maturity of these solutions.

Summary: Distributed online social networks (DOSNs) were first proposed to solve the problem of privacy, security, and scalability. A significant amount of research was undertaken to offer viable DOSN solutions that were capable of competing with the existing centralized OSN applications such as Facebook, LinkedIn, and Instagram. This research led to the emergence of the use of peer‐to‐peer (P2P) networks as a possible solution, upon which several OSNs such as LifeSocial.KOM, Safebook, PeerSoN among others were based. In this article, we define the basic requirements for an P2P OSN. We then revisit one of the first P2P‐based OSNs, LifeSocial.KOM, that is now called LibreSocial, which evolved in the past years to address the challenges of running a completely decentralized social network. Over the course of time, several essential new technologies have been incorporated within LibreSocial for better functionalities. In this article we describe the architecture and each individual component of LibreSocial and point out how LibreSocial meets the basic requirements for a fully functional distributed OSN.

Peer-to-peer (P2P) networks have been under investigation for several years now, with many novel mechanisms proposed as is shown by available articles. Much of the research focused on showing how the proposed mechanism improves system performance. In addition, several applications were proposed to harness the benefits of the P2P networks. Of these applications, online social networks (OSNs) raised much interest particularly because of the scalability and privacy concerns with centralized OSNs, hence several proposals are in existence. However, accompanying studies on the overall performance of the P2P network under the weight of the OSN applications outside simulations are very few, if any. In this paper, the aim is to undertake a systematic evaluation of the performance of a P2P framework for online social networks called LibreSocial. Benchmark tests are designed, taking into account the random behavior of users, effects of churn on system stability and effect of replication factor. We manage to run benchmark tests for up to 2000 nodes and show the performance against costs of the system in general. From the results it is evident that LibreSocial’s performance is capable of meeting the needs of users.

Peer-to-peer (P2P) networks were first made popular through file-sharing applications. With the rapid technological developments, current computing devices have become increasingly smaller and mobile, usually connecting to the Web via mobile networks operated by telecommunication companies. Additionally, it is now possible to run more sophisticated resource-intensive applications such as online social networks (OSNs) on P2P platforms. Thus, the P2P networks now must take into account the need for supporting device heterogeneity. This need is met by incorporating appropriate mechanisms with dedicated roles for more reliable devices and short-term functionalities for mobile devices that may not have adequate resource capacities (bandwidth, memory, or storage space) to share with the P2P network. In this paper, we present a capacity management protocol that categorizes nodes by their capacities in a formerly flat structured p2 $p$ overlay and converts it into a hierarchical structure by assigning dedicated roles and tasks to the peers involved. We implement and test the algorithm on our P2P framework for OSNs called LibreSocial. We show that role assignment and heterogeneity support work well even when a significantly high number of limited capacity devices join the network. We also show that, with 1/4 of the network nodes being strong, the network remains stable but with a notable reduction in overall performance.

While the growing accessibility of technology improves usability, it also creates more opportunities for cybercriminals to exploit vulnerabilities, significantly accelerating the proliferation of cybersecurity attacks. Deep learning (DL) approaches present significant advancements over conventional machine learning (ML) techniques by automating feature selection and extraction while minimizing external dependencies. This study proposes a deep learning-based model to enhance cyberattack detection and ensure that data security goals are achieved. This study employs a quantitative research design, utilizing simulation and modeling as the primary analytical tools. The dataset used is the Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS-2017) dataset. Three distinct DL algorithms are used to design the detection models, namely, Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). The performance comparison metrics are F1-score, accuracy, sensitivity, false positive rate (FPR), specificity, positive predictive value (PPV), false negative rate (FNR), and negative predictive value (NPV). Optimization concepts are integrated to enhance the detection efficiency in web-based systems, including loss functions, gradient-based optimization, and efficient model generalization techniques. The results of k-fold cross-validation show LSTM’s higher scores for F1-score (94.6%), recall (94.7%), accuracy (94.8%), and precision (94.6%). LSTM outperformed RNN and DNN, achieving the highest accuracy, precision, specificity, and sensitivity at 94.7%, 94.3%, 98.9%, and 94.7% respectively, validating LSTM's superior generalizability for cyberattack detection tasks.