This survey article presents a comprehensive and conceptual overview of anomaly detection (AD) using dynamic graphs. We focus on existing graph-based AD techniques and their applications to dynamic networks. The contributions of this survey article include the following: (i) a comparative study of existing surveys on AD; (ii) aDynamic Graph-based anomaly detection (DGAD)review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine learning models, matrix transformations, probabilistic approaches, and deep learning approaches; (iii) a discussion of graphically representing both discrete and dynamic networks; and (iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. ThisDGADsurvey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in AD in dynamic graphs.

Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. However, existing graph-based methods often focus on static graph structures, which struggle to adapt to the evolving nature of these graphs. In this paper, we propose Adaptive-DecayRank, a real-time and adaptive anomaly detection model for dynamic graph streams. Our method extends the dynamic PageRank algorithm by incorporating an adaptive Bayesian updating mechanism, allowing nodes to dynamically adjust their decay factors based on observed graph changes. This enables real-time detection of sudden structural shifts, improving anomaly identification in streaming graphs. We evaluate Adaptive-DecayRank on multiple real-world security datasets, including DARPA and CTU-13, as well as synthetic dense graphs generated using RTM. Our experiments demonstrate that Adaptive-DecayRank outperforms state-of-the-art methods, such as AnomRank, Sedanspot, and DynAnom, achieving up to $13.94\%$ higher precision, $8.43\%$ higher AUC, and more robust detection in highly dynamic environments.

Given a large graph stream with dynamically changing structures over a given timestep, it is important to detect the sudden appearance of anomalous patterns, such as sudden spikes in IP-network attacks or unexpected surges in social media followers. In addition, it is important that we promptly identify these abrupt changes in the network by considering swift and short-term responses within the network structure. To design a model capable of adapting to dynamic changes, we introduce an approach that utilizes a modified dynamic "PageRank-with-Decay" as a node scoring function. This method enables the detection of sudden dynamic graph changes based on node importance scores, leveraging the temporal evolution of graph structures at each timestep. This approach provides a refined anomaly detection mechanism for tracking rapid structural changes in the network. Through experiments conducted on a real-world dataset, our model demonstrates faster and more accurate results (in terms of precision and recall) compared to state-of-the-art methods.

In this study, we develop Neural Machine Translation (NMT) and Transformer-based transfer learning models for English-to-Igbo translation - a low-resource African language spoken by over 40 million people across Nigeria and West Africa. Our models are trained on a curated and benchmarked dataset compiled from Bible corpora, local news, Wikipedia articles, and Common Crawl, all verified by native language experts. We leverage Recurrent Neural Network (RNN) architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), enhanced with attention mechanisms to improve translation accuracy. To further enhance performance, we apply transfer learning using MarianNMT pre-trained models within the SimpleTransformers framework. Our RNN-based system achieves competitive results, closely matching existing English-Igbo benchmarks. With transfer learning, we observe a performance gain of +4.83 BLEU points, reaching an estimated translation accuracy of 70%. These findings highlight the effectiveness of combining RNNs with transfer learning to address the performance gap in low-resource language translation tasks.

According to the National Vulnerability Database (NVD), the number of registered cyber vulnerabilities for software keeps growing. In the public vulnerability database at cve.mitre.org, as of June 13, 2023, there are 8,314 software vulnerabilities related to Android™ operating system or mobile applications for Android™ widely used in cyber-physical systems. In this paper, we propose a solution to evaluate cyber risks of Android™-based software systems. Our solution determines the software packages installed on a device with Android™, assesses their associated cyber risks, and recommends alternative software products to make the system less vulnerable against cyber attacks. Also, it allows to pre-assess cyber risks for software and choose the least vulnerable configuration of the computing system at the design stage. We calculated the cyber risk scores using Common Vulnerability Scoring System (CVSS) scores for 2,423 Common Vulnerabilities and Exposures (CVEs) extracted from the NVD database and associated with different versions of Android™ operating system; and for 303 CVEs associated with web browsers. Our results indicate that the least vulnerable configuration for the Android™-based system includes Android™ 13 with Opera® web browser.

Anomaly detection in dynamic graphs is essential for identifying malicious activities, fraud, and unexpected behaviors in real-world systems such as cybersecurity and power grids. However, existing approaches struggle with scalability, probabilistic interpretability, and adaptability to evolving traffic patterns. In this paper, we propose ADAPTIVE-GRAPHSKETCH, a lightweight and scalable framework for real-time anomaly detection in streaming edge data. Our method integrates temporal multi-tensor sketching with Count-Min Sketch using Conservative Update (CMS-CU) to compactly track edge frequency patterns with bounded memory, while mitigating hash collision issues. We incorporate Bayesian inference for probabilistic anomaly scoring and apply Exponentially Weighted Moving Average (EWMA) for adaptive thresholding tuned to burst intensity. Extensive experiments on four real-world intrusion detection datasets demonstrate that ADAPTIVE-GRAPHSKETCH outperforms state-of-the-art baselines such as ANOEDGE-G/L, MIDAS-R, and F-FADE, achieving up to 6.5% AUC gain on CIC-IDS2018 and up to 15.6% on CIC-DDoS2019, while processing 20 million edges in under 3.4 seconds using only 10 hash functions. Our results show that ADAPTIVE-GRAPHSKETCH is practical and effective for fast, accurate anomaly detection in large-scale streaming graphs. Keywords: Anomaly Detection, Streaming, Real-time, Dynamic Graphs, Edge Streams, Tensor Sketching