As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics feature mining (PSFM) method. Specifically, the semantic information is first extracted from the code grammatical structure information and code text information. Then, the defect feature is mined through the semantic information. Finally, software defects are predicted by using the mined defect features. The experimental results show that, compared with the existing software defect prediction methods, the method in this paper (PSFM method) obtained a higher F-measure value.

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Power Internet of Things (PIoT) is the key technology to build a new power system based on new energy. Focusing on the problem that the large amount of data leads to the long computation delay of cloud computing in the operation control process of PIoT including renewable energy sources, this paper establishes a cloud-edge-end collaborative optimization calculation model for PIoT based on edge computation. Combined with data collected by a variety of smart terminal devices (STDs) arranged around the power generation equipment, the edge computation framework of PIoT is analyzed, and a computing task allocation model based on minimizing average system latency is established. The corresponding simulation model is built for simulation verification. Compared with other baseline schemes, it has been demonstrated that the average system latency of all tasks can be significantly decreased by using the proposed optimization scheme.