Anomaly events indicating the unhealthy status of the computer system are recorded in the system log (Syslog). Therefore, Syslog-based anomaly event detection is crucial for diagnosing system issues and problems. However, existing log-based anomaly detection approaches use raw and unstructured log entries independently and incompletely , i.e., without considering the context of each event and event metadata in the logs. They employ incomplete representation of unstructured log data, limiting the deep learning model’s capacity in the early stage, which tends to omit anomaly events and cause false alarms. In this work, we propose DeepSyslog, which represents Syslog with the context of log events and event metadata in the logs. Inspired by the sequence nature of the log stream, we employ unsupervised sentence embedding to extract the semantic and context information hidden in the log stream, rather than word embedding or one-hot embedding, which only capture the similarities between log words. The sentence embedding is further integrated with event metadata to form complete representations of Syslog, which can distinguish the anomaly caused by the correlated log entries and exceptional event metadata in the log. The simulation results on widely used log datasets show that DeepSyslog achieves high performance compared with the existing log-based anomaly event detection approaches.

Software aging, which is caused by the accumulation of errors in the system and the consumption of computing resources, tends to occur in long-running cloud service software systems. In practice, software aging prediction has proven to be useful in planning the time to trigger rejuvenation because it provides a prior estimate of future resource consumption. However, aging indicators (e.g., physical memory) in cloud may have the characteristics of long-term slow growth, medium-term seasonality variations (alternating peaks and troughs), and short-term irregular fluctuations. Unfortunately, most of the existing aging prediction methods (e.g., a statistical or single machine learning model) only focus on the accuracy of short-term prediction, while lacking the cognition of the medium and long-term variations of aging indicators and their functions in formulating the rejuvenation schedule (e.g., performing rejuvenation when the load is low can minimize interference to users). To address the above problems, this paper proposes a novel hybrid aging prediction framework to work on the prediction of memory resource consumption in cloud by applying a seasonal-trend decomposition procedure based on loess (STL) method and Gate Recurrent Unit (GRU) neural network, called the decomposition-based GRU (DGRU). The effectiveness of DGRU mainly has two aspects. One is the STL method as a preprocessing technology that can extract the trend, seasonality, and residual characteristics from memory utilization data. The other is that these characteristics can be well predicted separately by a well-designed GRU model, which can model the time-series relationship between the data. Experimental results show that our DGRU framework has superior performance compared with its competitors, including seven single models and six hybrid models. Our study illustrates that the DGRU is a promising solution for high-precision software aging prediction.

Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

To prevent the same known vulnerabilities from affecting different firmware, searching known vulnerabilities in binary firmware across different architectures is crucial. Because the accuracy of existing cross-architecture vulnerability search methods is not high, we propose a staged approach based on support vector machine (SVM) and attributed control flow graph (ACFG) at the function level to improve the accuracy using prior knowledge. Furthermore, for efficiency, we utilize the k-nearest neighbor (kNN) algorithm to prune and SVM to refine in the function prefilter stage. Although the accuracy of the proposed method using kNN-SVM approach is slightly lower than the accuracy of the method using only SVM, its efficiency is significantly enhanced. We have implemented our approach CVSkSA to search several vulnerabilities in real-world firmware images. The experimental results show that the accuracy of the proposed method using kNN-SVM approach is close to the accuracy of the method using only SVM in most cases, while the former is approximately four times faster than the latter.

The sending/receiving of data (data communication) is the most power consuming in wireless sensor networks (WSN) since the sensor nodes are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount of the exchanged data between wireless sensor nodes resulting in power saving. Nevertheless, there is a lack of effective methods to improve the efficiency of data compression algorithms and to increase nodes’ energy efficiency. In this paper, we proposed a novel lossless compression approach based on delta encoding and two occurrences character solving (T-RLE) algorithms. T-RLE is an optimization of the RLE algorithm, which aims to improve the compression ratio. This method will lead to less storage cost and less bandwidth to transmit the data, which positively affects the sensor nodes’ lifetime and the network lifetime in general. We used real deployment data (temperature and humidity) from the sensor scope project to evaluate the performance of our approach. The results showed a significant improvement compared with some traditional algorithms.