Biblio

Transient stability accidents induced by converter-based resources have been emerging frequently around the world. In this paper, the transient stability of the grid-tied voltage source converter (VSC) system is studied through estimating the basin of attraction (BOA) based on the hyperplane or hypersurface method. Meanwhile, fault critical clearing times are estimated, based on the approximated BOA and numerical fault trajectory. Further, the dynamic security region (DSR), an important index in traditional power systems, is extended to power-electronics-dominated power systems in this paper. The DSR of VSC is defined in the space composed of active current references. Based on the estimated BOA, the single-VSC-infinite-bus system is taken as an example and its DSR is evaluated. Finally, all these analytical results are well verified by several numerical simulations in MATLAB/Simulink.

The ever-growing threats of cascading failures due to cyber-attacks pose a significant challenge to power grid security. A wrong system state estimate caused by a false data injection attack could lead to a wrong control actions and take the system into a more insecure operating condition. As a consequence, an attack-resilient failure mitigation strategy needs to be developed to correctly determine control actions to prevent the propagation of cascades. In this paper, a recovery-based model predictive control methodology is developed to eliminate power system component violations following coordinated cyber-physical attacks where physical attacks are masked by targeted false data injection attacks. Specifically, to address the problem of wrong system state estimation with compromised data, a developed methodology recovers the incorrect states from historical data rather than utilizing the tampered data, and thus allowing control centers to identify proper control actions. Additionally, instead of using a one-step method to optimize control actions, the recovery-based model predictive control methodology scheme incorporates the effect of controls over a finite time horizon and the attack detection delay to make appropriate control decisions. Case studies, performed on IEEE 30-bus and Illinois 200-bus systems, show that the developed recovery-based model predictive control methodology scheme is robust to coordinated attacks and efficient in mitigating cascades.

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

With the continuous development of the Internet of Things, intelligent manufacturing has gradually entered the application stage, which urgently needs to solve the problem of information transmission security. In order to realize data security with transmission encryption, the UHF RFID tag based on domestic cryptographic algorithm SM7 is proposed. By writing the anti-counterfeiting authentication identification code when the tag leaves the factory, verifying the identification code when the tag is issued, and using the authentication code of the tag to participate in the sectoral key dispersion, the purpose of data security protection is achieved. Through this scheme, the security of tag information and transmission is guaranteed, and a new idea is provided for the follow-up large-scale extension of intelligent manufacturing.

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.