Biblio
The existing network intrusion detection methods have less label samples in the training process, and the detection accuracy is not high. In order to solve this problem, this paper designs a network intrusion detection method based on the GAN model by using the adversarial idea contained in the GAN. The model enhances the original training set by continuously generating samples, which expanding the label sample set. In order to realize the multi-classification of samples, this paper transforms the previous binary classification model of the generated adversarial network into a supervised learning multi-classification model. The loss function of training is redefined, so that the corresponding training method and parameter setting are obtained. Under the same experimental conditions, several performance indicators are used to compare the detection ability of the proposed method, the original classification model and other models. The experimental results show that the method proposed in this paper is more stable, robust, accurate detection rate, has good generalization ability, and can effectively realize network intrusion detection.
Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names up-to-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.
With widely applied in various fields, deep learning (DL) is becoming the key driving force in industry. Although it has achieved great success in artificial intelligence tasks, similar to traditional software, it has defects that, once it failed, unpredictable accidents and losses would be caused. In this paper, we propose a test cases generation technique based on an adversarial samples generation algorithm for image classification deep neural networks (DNNs), which can generate a large number of good test cases for the testing of DNNs, especially in case that test cases are insufficient. We briefly introduce our method, and implement the framework. We conduct experiments on some classic DNN models and datasets. We further evaluate the test set by using a coverage metric based on states of the DNN.
In the modern retailing industry, many enterprise resource planning (ERP) systems are considered legacy software systems that have become too expensive to replace and too costly to re-engineer. Countering the need to maintain and extend the business value of these systems is the need to do so in the simplest, cheapest, and least risky manner available. There are a number of approaches used by software engineers to mitigate the negative impact of evolving a legacy systems, including leveraging service-oriented architecture to automate manual tasks previously performed by humans. A relatively recent approach in software engineering focuses upon implementing self-managing attributes, or “autonomic” behavior in software applications and systems of applications in order to reduce or eliminate the need for human monitoring and intervention. Entire systems can be autonomic or they can be hybrid systems that implement one or more autonomic components to communicate with external systems. In this paper, we describe a commercial development project in which a legacy multi-channel commerce enterprise resource planning system was extended with service-oriented architecture an autonomic control loop design to communicate with an external third-party security screening provider. The goal was to reduce the cost of the human labor necessary to screen an ever-increasing volume of orders and to reduce the potential for human error in the screening process. The solution automated what was previously an inefficient, incomplete, and potentially error-prone manual process by inserting a new autonomic software component into the existing order fulfillment workflow.