Sequence-based deep learning models are commonly used to detect anomalies in system logs to ensure the security of communication and information systems. However, recent research has shown that adversarial attack methods against these detection models reveal their vulnerabilities. Attackers can bypass these sequence-based classifiers by tampering with the sequence (e.g., adding or replacing sequence events). In this paper, we propose a novel Multi-Stage Defensive strategy for sequence-based log anomaly detection aimed at combating adversarial attacks. Systematically integrated, this strategy spans the entire detection process, from data embedding representation to anomaly classification, thereby forming a robust defensive approach that is strategically orchestrated to ensure comprehensive protection against a variety of adversarial attacks. Firstly, we compress the semantic feature space of sequences to enhance the anti-interference ability of attack operations on substitution sequences. Then, we propose a novel adaptive sparse multi-head attention mechanism to improve the Transformer model, allowing it to adaptively extract different key patterns in the sequence, thus eliminating irrelevant sequence events added in adversarial sequences. Our approach also integrates the learning of temporal patterns, offering enhanced robustness against deletion operations. Through extensive experiments on two public datasets, the experimental results demonstrate that our approach has high detection performance and robustness against different adversarial attacks.

With the development of Internet of Things (IoT), it is gaining a lot of attention. It is important to secure the embedded systems with low overhead. The Linux Seccomp is widely used by developers to secure the kernels by blocking the access of unused syscalls, which introduces less overhead. However, there are no systematic Seccomp configuration approaches for IoT applications without the help of developers. In addition, the existing Seccomp configuration approaches are coarse-grained, which cannot analyze and limit the syscall arguments. In this article, a novel static dependent syscall analysis approach for embedded applications is proposed, which can obtain all of the possible dependent syscalls and the corresponding arguments of the target applications. So, a fine-grained kernel access limitation can be performed for the IoT applications. To this end, the mappings between dynamic library APIs and syscalls according with their arguments are built, by analyzing the control flow graphs and the data dependency relationships of the dynamic libraries. To the best of our knowledge, this is the first work to generate the fine-grained Seccomp profile for embedded applications.

Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program developers. Docker containers block about only 50 syscalls by default, and lots of unblocked useless syscalls introduce a big kernel attack surface. To obtain the dependent syscalls, dynamic tracking is a straight-forward approach but it cannot get the full syscall list. Static analysis can construct an over-approximated syscall list, but the list contains many false positives. In this paper, a systematic dependent syscall analysis approach, sysverify, is proposed by combining static analysis and dynamic verification together to shrink the kernel attack surface. The semantic gap between the binary executables and syscalls is bridged by analyzing the binary and the source code, which builds the mapping between the library APIs and syscalls systematically. To further reduce the attack surface at best effort, we propose a dynamic verification approach to intercept and analyze the security of the invocations of indirect-call-related or rarely invoked syscalls with low overhead.

Time series forecasting has historically been a popular research area, attracting widespread interest in academia and industry. Recently, Deep Learning based models like RNN, LSTM, and NeuralProphet have been successfully applied for time series forecasting. However, single forecasting models are unable to capture full information and learn complex patterns in data. The combination of models has proved to be an effective strategy to address this issue. Traditional hybrid structures still limit the forecasting ability of hybrid methods. In this paper, we propose a novel hybrid forecasting model based on LSTM and NeuralProphet (NP-LSTM), which is constructed by a parallel-series hybrid structure. The proposed model uses LSTM to model diverse nonlinear relationships and NeuralProphet is designed to extract primary trends and seasonal effects and provide interpretability. In the experiments of this paper, we compare our model with other single models and hybrid models of traditional structures using four real-world datasets. The experimental results show that our NP-LSTM hybrid model obtains superior performance in various metrics for time series forecasting.