Detection of abnormal network traffic is an important issue when builds intrusion detection systems. An effective way to address this issue is time series mining, in which the network traffic is naturally represented as a set of time series. In this paper, we propose a novel efficient algorithm, called RSFID (Random Shapelet Forest for Intrusion Detection), to detect abnormal traffic flow patterns in periodic network packets. Firstly, the Fast Correlation‐based Filter (FCBF) algorithm is employed to remove irrelevant features to decrease the overfitting as well as the time complexity. Then, a random forest which is built upon a set of shapelet candidates is used to classify the normal and abnormal traffic flow patterns. Specifically, the Symbolic Aggregate approXimation (SAX) and random sampling technique are adopted to mitigate the high time complexity caused by enumerating shapelet candidates. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems. Video adversarial attacks add subtle noise to the original example, resulting in a false classification result. Thorough studies on how to generate video adversarial examples are essential to prevent potential attacks. Despite much research on this, existing research works on the robustness of video adversarial examples are still limited. To generate highly robust video adversarial examples, we propose a video-augmentation-based adversarial attack (v3a), focusing on the video transformations to reinforce the attack. Further, we investigate different transformations as parts of the loss function to make the video adversarial examples more robust. The experiment results show that our proposed method outperforms other adversarial attacks in terms of robustness. We hope that our study encourages a deeper understanding of adversarial robustness in video classification systems with video augmentation.

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.

In the evolving landscape of complex business ecosystems and their digital platforms, an increasing number of business Application Programming Interfaces (APIs) are encountering challenges in ensuring optimal authorization control. This challenge arises due to factors such as programming errors, improper configurations, and sub-optimal business processes. While security departments have exhibited proficiency in identifying vulnerabilities and mitigating certain viral or adversarial incursions, the safeguarding of comprehensive business processes remains an intricate task. This paper introduces a novel paradigm, denoted as the Low-Intervention Security Embedding Architecture (LiSEA), which empowers business applications to enhance the security of their processes through judicious intervention within business APIs. By strategically incorporating pre- and post-intervention checkpoints, we devise a finely grained access control model that meticulously assesses both the intent of incoming business requests and the outcomes of corresponding responses. Importantly, these advancements are seamlessly integrated into the existing business codebase. Our implementation demonstrates the effectiveness of LiSEA, as it adeptly addresses eight out of the ten critical vulnerabilities enumerated in the OWASP API Security Top 10. Notably, when the number of threads is less than 200, LiSEA brings less than 20 msec of latency to the business process, which is significantly less than the microservice security agent based on the API gateway.