Biblio

In order to protect the safety of power grid, improve the early warning precision of false data injection. This paper presents a dynamic detection model for false data injection attacks. Based on the characteristics of APT attacks, a model of attack characteristics for trusted regions is constructed. In order to realize the accurate state estimation, unscented Kalman filtering algorithm is used to estimate the state of nonlinear power system and realize dynamic attack detection. Experimental results show that the precision of this method is higher than 90%, which verifies the effectiveness of this paper in attack detection.

At present, when the device management application programs the devices (such as mobile terminals, Internet of things terminals and devices, etc.), buffer overflow will inevitably occur due to the defects of filter input condition setting, variable type conversion error, logical judgment error, pointer reference error and so on. For this kind of software and its running environment, it is difficult to reduce the false positive rate and false negative rate with traditional static detection method for buffer overflow vulnerability, while the coverage rate of dynamic detection method is still insufficient and it is difficult to achieve full automation. In view of this, this paper proposes an automatic dynamic detection method based on boundary testing, which has complete test data set and full coverage of defects. With this method, the input test points of the software system under test are automatically traversed, and each input test point is analyzed automatically to generate complete test data; driven by the above complete test data, the software under test runs automatically, in which the embedded dynamic detection code automatically judges the conditions of overflow occurrence, and returns the overflow information including the location of the error code before the overflow really occurs. Because the overflow can be located accurately without real overflow occurrence, this method can ensure the normal detection of the next input test point, thus ensuring the continuity of the whole automatic detection process and the full coverage of buffer overflow detection. The test results show that all the indexes meet the requirements of the method and design.

Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild.

Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.

This paper studies the principle of vulnerability generation and mechanism of cross-site scripting attack, designs a dynamic cross-site scripting vulnerabilities detection technique based on existing theories of black box vulnerabilities detection. The dynamic detection process contains five steps: crawler, feature construct, attacks simulation, results detection and report generation. Crawling strategy in crawler module and constructing algorithm in feature construct module are key points of this detection process. Finally, according to the detection technique proposed in this paper, a detection tool is accomplished in Linux using python language to detect web applications. Experiments were launched to verify the results and compare with the test results of other existing tools, analyze the usability, advantages and disadvantages of the detection method above, confirm the feasibility of applying dynamic detection technique to cross-site scripting vulnerabilities detection.

Recent years, HTML5 is widely adopted in popular browsers. Unfortunately, as a new Web standard, HTML5 may expand the Cross Site Scripting (XSS) attack surface as well as improve the interactivity of the page. In this paper, we identified 14 XSS attack vectors related to HTML5 by a systematic analysis about new tags and attributes. Based on these vectors, a XSS test vector repository is constructed and a dynamic XSS vulnerability detection tool focusing on Webmail systems is implemented. By applying the tool to some popular Webmail systems, seven exploitable XSS vulnerabilities are found. The evaluation result shows that our tool can efficiently detect XSS vulnerabilities introduced by HTML5.