Linux内核提权攻击研究

提权攻击是针对Linux系统的一种重要攻击手段。根据提权攻击所利用的漏洞类型,一般可将其分为应用层提权攻击和内核提权攻击。现有的防御技术已经能够防御基本的应用层提权攻击,但是并不能完全防御内核提权攻击,内核提权攻击仍是Linux系统面临的一个重要威胁。内核提权攻击一般通过利用内核提权漏洞进行攻击。针对内核提权攻击,分析研究了基本的内核提权漏洞利用原理以及权限提升方法,并对典型的内核提权攻击防御技术进行了分析。最后通过实验对SELinux针对内核提权攻击的防御效果进行了分析验证,并针对发现的问题指出了下一步具有可行性的研究方向。     

The governance strategies for public emergencies on social media and their effects: a case study based on the microblog data

Since the 1980s, the development of information and communication technologies (ICTs) has greatly changed people’s modes of production and lifestyle, and it has also had a significant influence on traditional social structures. Microblogs – a type of social media application such as Twitter or Weibo – have served as an important platform for network governance in some local governments in China. This study makes an attempt to answer the following questions: What types of strategies should governments implement on social media platforms during public emergencies? What are the effects of these strategies? Based on the case of the Shifang Incident, which was a large-scale environmental protest that occurred in Shifang, China in 2012, we analyze all the messages posted during the incident on the official microblog of the Shifang government and examine the public feedback by using an online big data analysis tool. In line with the time sequence and the extent of the conflict, we divide the Shifang Incident into three phases: the fermentation period, the confrontation period, and the digestion period. In addition, we classify government strategies on social media into five categories: introducing, appealing, explaining, rumor-refuting, and decision-making. The analysis results show that different government strategies are applied to different phases of the incident and that the responses of the public also vary during different periods.     

Finite‐time asynchronous filtering for discrete‐time Markov jump systems over a lossy network

This paper is concerned with the problem of finite‐time asynchronous filtering for a class of discrete‐time Markov jump systems. The communication links between the system and filter are assumed to be unreliable, which lead to the simultaneous occurrences of packet dropouts, time delays, sensor nonlinearity and nonsynchronous modes. The objective is to design a filter that ensures not only the mean‐square stochastic finite‐time bounded but also a prescribed level of performance for the underlying error system over a lossy network. With the help of the Lyapunov–Krasovskii approach and stochastic analysis theory, sufficient conditions are established for the existence of an admissible filter. By using a novel simple matrix decoupling approach, a desired asynchronous filter can be constructed. Finally, a numerical example is presented and a pulse‐width‐modulation‐driven boost converter model is employed to demonstrate the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Exact and approximate flexible aggregate similarity search

Aggregate similarity search, also known as aggregate nearest-neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves from a database the objects most similar to Q, where the similarity is an aggregation (e.g., \({{\mathrm{sum}}}\), \(\max \)) of the distances between each retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of \(\phi M\) objects in Q for some support \(0< \phi \le 1\). We call this new definition flexible aggregate similarity search and accordingly refer to a query as a flexible aggregate nearest-neighbor ( Fann ) query. We present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.     

Re-ranking for microblog retrieval via multiple graph model

As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.     

The design and test of dual/triple‐mode bandpass filter based on temporal coupled‐mode theory

In this article, the design and test of both a K‐band dual‐mode bandpass filter (BPF) pair and a K‐band triple‐mode BPF are presented based on N‐mode temporal coupled‐mode theory (CMT). The expressions of transmission and reflection responses are analytically derived. All the parameters in the expressions have clear physical meanings and are easily optimized to reach the required filter performance. Aided by eigenmode simulations, concrete structures of the three integrated BPFs are designed and optimized to approach the calculated physical parameters. After the fabrications and measurements of the three BPFs, extended upper/lower stopband with high stopband rejections are achieved, and by increasing the number of resonant modes, improved frequency selectivity and better passband flatness are obtained. The analytical analysis well predicts the simulation and measurement results, which provides an efficient way for BPF designs. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:609–622, 2016.     

Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable

This paper aims to design an appropriate switching law to stabilize the switched neural networks with time-varying delays when all subsystems are unstable. By using the discretized Lyapunov function approach and the extended comparison principle for impulsive systems, the stability of switched delayed neural networks composed full of unstable subsystems is analyzed and a computable sufficient condition is derived in the framework of dwell time. The effectiveness of the proposed results is illustrated by a numerical example.     

Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.