Injection attack is the first of the top 10 security threats announced by the OWASP. Meanwhile, SQL injection is one of the most important types among the injection attacks. Because of its various types and fast variations, SQL injection can cause great harm to the network, resulting in data leakage and website paralysis. Due to the heterogeneity of attack load, the diversity of attack methods and the variety of attack modes, SQL injection detection is still a challenging problem. How to defense SQL injection attack effectively becomes the focus and frontier of web security nowadays. Therefore, this paper proposes an adaptive deep forest-based method to detect the complex SQL injection attacks. Firstly, the structure of deep forest is optimized in our paper, the input of each layer is concatenated by the raw feature vector and average of previous outputs. Experiments show that our proposed method effectively solves the problem that the original features of deep forests are degraded with the increasing number of layers. Then, we introduce an AdaBoost algorithm based deep forest model which utilizes error rate to update the weights of features on each layer. That is, in the process of training, different features are assigned with different weights based on their influence on the result. Our model can automatically adjust the structure of the tree model and deal with multi-dimensional fine-grained features to avoid over-fitting problem effectively. The experimental results show that the proposed method has a better performance than classical machine learning methods and deep learning methods.

In recent years, cyber criminals have successfully invaded many important information systems by using phishing mail, causing huge losses. The detection of phishing mail from big email data has been paid public attention. However, the camouflage technology of phishing mail is becoming more and more complex, and the existing detection methods are unable to confront with the increasingly complex deception methods and the growing number of emails. In this article, we proposed an LSTM based phishing detection method for big email data. The new method includes two important stages, sample expansion stage and testing stage under sufficient samples. In the sample expansion stage, we combined KNN with K-Means to expand the training data set, so that the size of training samples can meet the needs of in-depth learning. In the testing stage, we first preprocess these samples, including generalization, word segmentation and word vector generation. Then, the preprocessed data is used to train a LSTM model. Finally, on the basis of the trained model, we classify the phishing emails. By experiment, we evaluate the performance of the proposed method, and experimental results show that the accuracy of our phishing detection method can reach 95 percent.

In the digital era of increasing software complexity, improving the development efficiency of safety-critical software is a challenging task faced by academia and industry in domains such as nuclear energy, aviation, the automotive industry, and rail transportation. Recently, people have been excited about using pre-trained large language models (LLMs) such as ChatGPT and GPT-4 to generate code. Professionals in the safety-critical software field are intrigued by the code generation capabilities of LLMs. However, there is currently a lack of systematic case studies in this area. Aiming at the need for automated code generation in safety-critical domains such as nuclear energy and the automotive industry, this paper conducts a case study on generating safety-critical software code using GPT-4 as the tool. Practical engineering cases from the industrial domain are employed. We explore different approaches, including code generation based on overall requirements, specific requirements, and augmented prompts. We propose a novel prompt engineering method called Prompt-FDC that integrates basic functional requirements, domain feature generalization, and domain constraints. This method improves code completeness from achieving 30% functions to 100% functions, increases the code comment rate to 26.3%, and yields better results in terms of code compliance, readability, and maintainability. The code generation approach based on LLMs also introduces a new software development process and V-model lifecycle for safety-critical software. Through systematic case studies, we demonstrate that, with appropriate prompt methods, LLMs can auto-generate safety-critical software code that meets practical engineering application requirements. It is foreseeable that LLMs can be applied to various engineering domains to improve software safety and development efficiency.

Malware has become one of the most serious security threats to the Internet of Things (IoT). Detection of malware variants can inhibit the spread of malicious code from the traditional network to the IoT, and can also inhibit the spread of malicious code within the IoT, which is of great significance to the security detection and defense of the IoT. Since the terminals and the operating systems of IoT are very different from the traditional network, when malicious code is transferred from the traditional network to the IoT platform, the characteristics of the variants may change significantly. As a result, malicious code variant detection methods for traditional platforms cannot be directly applied to the IoT. In this article, a malware variant detection method for the IoT is proposed. First, we propose a feature representation method based on RGB image for IoT to solve the problem of representation difficulty caused by platform difference, which pays more attention to the assembly code and developer information of the malware. The generated image has richer texture information, which can dig out the deep association between the IoT variants and the original malicious code. Moreover, this article improves the convolutional neural network model by combining the self-attention mechanism and spatial pyramid pooling to solve the problem of large differences in the size of IoT malware. Experimental results show that our method can be used in cross-platform to detect malware variants in the IoT effectively.

Internet of Things (IoT) is hard to deploy adequate security defenses due to the diversity of architectures as well as the limited computing and storage capabilities, which makes it more vulnerable to malware. With the massive deployment of IoT devices, how to accurately identify and classify the malware variants is crucial to IoT security. However, existing methods of IoT malware classification generally support specific platform or require complex models to achieve higher accuracies. To solve these problems, this article proposes an IoT malware classification method based on lightweight convolutional neural networks (LCNNs). First, the malware binaries are converted into multidimensional Markov images. Then, the LCNN is designed with two new operations, depthwise convolution and channel shuffle, for malware images classification. Compared with other deep learning-based methods such as VGG16, the designed LCNN can greatly reduce trainable parameters while maintaining accuracy. The generated model of LCNN is only about 1 MB, while that of VGG16 is 552.57 MB. The average accuracies of the proposed method are higher than that of gray images on multiple IoT malware data sets, all of which are over 95%. Compared with the state-of-the-art low-level features-based methods, the average accuracy of the proposed method is 99.356% on the Microsoft data set even if the model is tiny. The results show that the proposed method is not only suitable for IoT environments but also has high accuracy.