A mathematical modeling of GOOSE network traffic is adopted for the anomaly detection based on statistical hypothesis testing. The EDA4GNeT method considers the dynamic behavior of network traffic in electrical substations. In order to accurately detect similar attacks, a novel method for the Early Detection of Attacks for GOOSE Network Traffic (EDA4GNeT) is developed in the present work. The availability of communication in IEC 61850 substations can be hindered by Generic Object Oriented Substation Event (GOOSE) poisoning attacks that might result in threats such as Denial of Service (DoS) or flooding attacks. The requirements for the security of the network communication in critical infrastructures have been more focused on the availability of the data rather than the integrity and the confidentiality. Moreover, they are supported by simulation studies and extended to problems with non-iid data and composite hypotheses. These results are based on an asymptotic analysis for the fundamental binary testing problem as the two error probabilities go to zero. In contrast, existing multistage tests in the literature are shown to achieve this high-dimensional asymptotic optimality property only under additional sparsity or symmetry conditions. Specifically, it achieves the optimal, average over all streams, expected sample size, uniformly in the true number of signals, as the maximum possible numbers of signals and noises go to infinity at arbitrary rates, in the class of all sequential tests with the same global error control. A novel multistage testing procedure is proposed for this problem and is shown to enjoy a high-dimensional asymptotic optimality property. A decentralized formulation is adopted, according to which the sample size and the decision for each testing problem must be based only on observations from the corresponding data stream. It is not required that the exact number of signals be a priori known, only upper bounds on the numbers of signals and noises are assumed instead. null) hypothesis is correct, subject to prescribed bounds on classical or generalized familywise error probabilities of both types. noises), i.e., streams where the alternative (resp. The goal is to identify all signals (resp.

Moreover, the F1-score was increased by 4.15% under hybrid non-ideal conditions.Ī signal recovery problem is considered, where the same binary testing problem is posed over multiple, independent data streams. The simulation results showed that compared with existing methods, the proposed framework effectively improves the CPD performance at parameter-level. The framework further improves the label configuration and the loss function of training to mitigate the label sparsity problem effectively. Finally, temporal features are adopted to predict the probability of each pulse being a change point. Then, a multi-head attention-based bi-directional long short-term memory network is introduced to abstract high-order relationships between successive pulses. First, the fine-grained MFR work mode model is established. To address the challenges, a deep learning framework is proposed for fine-grained work mode CPD in this paper. (ii) Modern MFRs can produce a variety of parameter-level (fine-grained) work modes with complex and flexible patterns, which are challenging to detect through traditional statistical methods and basic learning models. There are two major challenges that must be addressed: (i) The received radar pulse stream may contain multiple work mode segments of unknown number and duration, which makes the Change Point Detection (CPD) difficult.

Detection of the changes in Multi-Functional Radar (MFR) work modes is a critical situation assessment task for Electronic Support Measure (ESM) systems.

Sequential analysis statistics