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软件与网络安全研究综述   总被引:1,自引:0,他引:1       下载免费PDF全文
互联网已经渗入人类社会的各个方面,极大地推动了社会进步。与此同时,各种形式的网络犯罪、网络窃密等问题频频发生,给社会和国家安全带来了极大的危害。网络安全已经成为公众和政府高度关注的重大问题。由于互联网的大量功能和网络上的各种应用都是由软件实现的,软件在网络安全的研究与实践中扮演着至关重要的角色。事实上,几乎所有的网络攻击都是利用系统软件或应用软件中存在的安全缺陷实施的。研究新形势下的软件安全问题日益迫切。本文从恶意软件、软件漏洞和软件安全机制三个方面综述国内外研究现状,进而分析软件生态系统面临的全新安全挑战与发展趋势。  相似文献
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重复数据删除技术受到工业界和学术界的广泛关注.研究者致力于将云服务器中的冗余数据安全的删除,明文数据的重复删除方法较为简单.而用户为了保护隐私,会使用各自的密钥将数据加密后上传至云服务器,形成不同的加密数据.在保证安全性的前提下,加密数据的重复删除较难实现.目前已有的方案较多依赖在线的可信第三方.提出一种基于离线密钥分发的加密数据重复删除方案,通过构造双线性映射,在不泄露数据隐私的前提下,验证加密数据是否源自同一明文.利用广播加密技术实现加密密钥的安全存储与传递.任意数据的初始上传者能够借助云服务器,以离线方式验证后继上传者的合法性并传递数据加密密钥.无需可信第三方在线参与,实现云服务器对加密数据的重复删除.分析并证明了方案的安全性.仿真实验验证了方案的可行性与高效性.  相似文献
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模糊测试是一种有效的漏洞挖掘技术.为改善模糊测试因盲目变异而导致的效率低下的问题,需要围绕输入特征、变异策略、种子样本筛选、异常样本发现与分析等方面不断定制模糊测试器,从而花费了大量的定制成本.针对通用型模糊测试器(即支持多类输入格式及目标软件的模糊测试器)的低成本定制和高可扩展性需求,本文首次提出了一种可编程模糊测试框架,基于该框架漏洞挖掘人员仅需编写模糊测试制导程序即可完成定制化模糊测试,在不降低模糊测试效果的基础上可大幅提高模糊测试器开发效率.该框架包含一组涉及变异、监控、反馈等环节的模糊测试原语,作为制导程序的基本语句;还包含一套编程规范(FDS)及FDS解析器,支持制导程序的编写、解析和模糊测试器的生成.基于实现的可编程模糊测试框架原型Puzzer,在26个模糊测试原语的支持下,漏洞挖掘人员平均编写54行代码即可实现当前主流的5款万级代码模糊测试器的核心功能,并可覆盖总计87.8%的基本操作.基于Puzzer实现的AFL等价模糊测试器,仅用51行代码即可达到与AFL相当的模糊测试效果,具有良好的有效性.  相似文献
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模糊测试被广泛应用于浏览器的漏洞挖掘,其效果好坏的决定因素之一是测试者编写的测试模式.针对特定测试模式实现成本高、生存时间短等问题,本文提出了一种基于模式生成的浏览器模糊测试器自动构造方法,通过解析已知漏洞触发样本,自动提取测试模式,对模式中每个模块应用传统的变异策略,完成畸形样本的自动生成.实验表明,针对5款浏览器的1089个已知漏洞触发样本,平均仅用时11.168秒即可完成1089个不同模糊测试器的自动构建,远低于人为编写的时间消耗;随机选取其中10个模糊测试器分别对IE 10、IE 11、Firefox 54.0的全补丁版本进行测试,共产生57个不同的崩溃样本,发现1个高危未知漏洞,证明本方法具有较好的未知漏洞发现能力.  相似文献
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Loadable kernel modules (LKMs) that contain vulnerabilities are a big threat to modern operating systems (OSs). The primary reason is that there is no protection mechanism inside the kernel space when the LKM is executed. As a result, kernel module exploitation can seriously affect the OS kernel security. Although many protection systems have been developed to address this problem in the past few years, there still remain some challenges: (1) How to automatically generate a security policy before the kernel module is enforced? (2) How to properly mediate the interactions between the kernel module and the OS kernel without modifications on the existing OS, hardware, and kernel module structure? To address these challenges, we present LKM guard (LKMG), a policy‐centric system that can protect commodity OS kernel from vulnerable LKMs. Compared with previous systems, LKMG is able to generate a security policy from a kernel module and then enforce the policy during the run time. Generally, the working process of LKMG can be divided into 2 stages. First, we utilize static analysis to extract the kernel code and data access patterns from a kernel module's source code and then combine these patterns with the related memory address information to generate a security policy. Second, by leveraging the hardware‐assisted virtualization technology, LKMG isolates the kernel module from the rest of the kernel and then enforces the kernel module's execution to obey the derived policy. The experiments show that our system can defend against various attacks launched by the compromised kernel module effectively with moderate performance cost.  相似文献
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Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers’ understanding of their own postings but also on other factors, including developers’ English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.  相似文献
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