The border gateway protocol (BGP) is the default inter domain routing protocol used on the internet for exchanging information between autonomous systems. Available literature suggests that BGP is vulnerable to sessio...The border gateway protocol (BGP) is the default inter domain routing protocol used on the internet for exchanging information between autonomous systems. Available literature suggests that BGP is vulnerable to session hijacking attacks. There are a number of proposals aimed at improving BGP security which have not been fully implemented. This paper examines a number of approaches for securing BGP through a comparative study and identifies the reasons why these proposals have not been implemented commercially. This paper analyses the architecture of internet routing and the design of BGP while focusing on the problem of BGP session hijacking attacks. Using Graphical Network Simulator 3 (GNS-3), a session hijack is demonstrated and a solution which involves the implementation of route filtering, policy-maps and route-maps on CISCO routers representing ASes is carried out. In the end, a workable industry standard framework for securing and protecting BGP sessions and border routers from exploitation with little or no modification to the existing routing infrastructure is demonstrated.展开更多
Traffic hijacking is a common attack perpetrated on networked systems, where attackers eavesdrop on user transactions, manipulate packet data, and divert traffic to illegitimate locations. Similar attacks can also be ...Traffic hijacking is a common attack perpetrated on networked systems, where attackers eavesdrop on user transactions, manipulate packet data, and divert traffic to illegitimate locations. Similar attacks can also be unleashed in a NoC (Network on Chip) based system where the NoC comes from a third-party vendor and can be engrafted with hardware Trojans. Unlike the attackers on a traditional network, those Trojans are usually small and have limited capacity. This paper targets such a hardware Trojan;Specifically, the Trojan aims to divert traffic packets to unauthorized locations on the NoC. To detect this kind of traffic hijacking, we propose an authentication scheme in which the source and destination addresses are tagged. We develop a custom design for the packet tagging and authentication such that the implementation costs can be greatly reduced. Our experiments on a set of applications show that on average the detection circuitry incurs about 3.37% overhead in area, 2.61% in power, and 0.097% in performance when compared to the baseline design.展开更多
With the rapid development of operating systems,attacks on system vulnerabilities are increasing.Dynamic link library(DLL)hijacking is prevalent in installers on freeware platforms and is highly susceptible to exploit...With the rapid development of operating systems,attacks on system vulnerabilities are increasing.Dynamic link library(DLL)hijacking is prevalent in installers on freeware platforms and is highly susceptible to exploitation by malware attackers.However,existing studies are based solely on the load paths of DLLs,ignoring the attributes of installers and invocation modes,resulting in low accuracy and weak generality of vulnerability detection.In this paper,we propose a novel model,AB-DHD,which is based on an attention mechanism and a bi-directional gated recurrent unit(BiGRU)neural network for DLL hijacking vulnerability discovery.While BiGRU is an enhancement of GRU and has been widely applied in sequence data processing,a double-layer BiGRU network is introduced to analyze the internal features of installers with DLL hijacking vulnerabilities.Additionally,an attention mechanism is incorporated to dynamically adjust feature weights,significantly enhancing the ability of our model to detect vulnerabilities in new installers.A comprehensive“List of Easily Hijacked DLLs”is developed to serve a reference for future studies.We construct an EXEFul dataset and a DLLVul dataset,using data from two publicly available authoritative vulnerability databases,Common Vulnerabilities&Exposures(CVE)and China National Vulnerability Database(CNVD),and mainstream installer distribution platforms.Experimental results show that our model outperforms popular automated tools like Rattler and DLLHSC,achieving an accuracy of 97.79%and a recall of 94.72%.Moreover,17 previously unknown vulnerabilities have been identified,and corresponding vulnerability certifications have been assigned.展开更多
Activity hijacking is one of the most powerful attacks in Android. Though promising, all the prior activity hijacking attacks suffer from some limitations and have limited attack capabilities. They no longer pose secu...Activity hijacking is one of the most powerful attacks in Android. Though promising, all the prior activity hijacking attacks suffer from some limitations and have limited attack capabilities. They no longer pose security threats in recent Android due to the presence of effective defense mechanisms. In this work, we propose the first automated and adaptive activity hijacking attack, named VenomAttack, enabling a spectrum of customized attacks (e.g., phishing, spoofing, and DoS) on a large scale in recent Android, even the state-of-the-art defense mechanisms are deployed. Specifically, we propose to use hotpatch techniques to identify vulnerable devices and update attack payload without re-installation and re-distribution, hence bypassing offline detection. We present a newly-discovered flaw in Android and a bug in derivatives of Android, each of which allows us to check if a target app is running in the background or not, by which we can determine the right attack timing via a designed transparent activity. We also propose an automated fake activity generation approach, allowing large-scale attacks. Requiring only the common permission INTERNET, we can hijack activities at the right timing without destroying the GUI integrity of the foreground app. We conduct proof-of-concept attacks, showing that VenomAttack poses severe security risks on recent Android versions. The user study demonstrates the effectiveness of VenomAttack in real-world scenarios, achieving a high success rate (95%) without users’ awareness. That would call more attention to the stakeholders like Google.展开更多
缓冲区溢出漏洞广泛存在于由不安全的高级语言所编写的程序中.利用缓冲区溢出漏洞,攻击者可以实现控制流劫持等危险攻击方式.基于Canary的栈保护技术是处理缓冲区溢出漏洞的一种简单有效且广泛部署的防御手段,然而位置固定和取值相同的...缓冲区溢出漏洞广泛存在于由不安全的高级语言所编写的程序中.利用缓冲区溢出漏洞,攻击者可以实现控制流劫持等危险攻击方式.基于Canary的栈保护技术是处理缓冲区溢出漏洞的一种简单有效且广泛部署的防御手段,然而位置固定和取值相同的特点使其容易被攻击者分析和破解.本文提出一种基于软件多样性的栈保护技术,它以拥有随机化大小和偏移的异构Canary为核心,不仅能直接抵御常规Canary无法处理的泄漏类和覆盖类攻击,而且能构造出各种更加安全的多样性软件系统.实验结果表明,异构Canary在有效提升安全性的同时仅为SPEC CPU 2017基准程序集额外引入了不高于2%的编译开销和平均3.22%的运行开销.展开更多
文摘The border gateway protocol (BGP) is the default inter domain routing protocol used on the internet for exchanging information between autonomous systems. Available literature suggests that BGP is vulnerable to session hijacking attacks. There are a number of proposals aimed at improving BGP security which have not been fully implemented. This paper examines a number of approaches for securing BGP through a comparative study and identifies the reasons why these proposals have not been implemented commercially. This paper analyses the architecture of internet routing and the design of BGP while focusing on the problem of BGP session hijacking attacks. Using Graphical Network Simulator 3 (GNS-3), a session hijack is demonstrated and a solution which involves the implementation of route filtering, policy-maps and route-maps on CISCO routers representing ASes is carried out. In the end, a workable industry standard framework for securing and protecting BGP sessions and border routers from exploitation with little or no modification to the existing routing infrastructure is demonstrated.
文摘Traffic hijacking is a common attack perpetrated on networked systems, where attackers eavesdrop on user transactions, manipulate packet data, and divert traffic to illegitimate locations. Similar attacks can also be unleashed in a NoC (Network on Chip) based system where the NoC comes from a third-party vendor and can be engrafted with hardware Trojans. Unlike the attackers on a traditional network, those Trojans are usually small and have limited capacity. This paper targets such a hardware Trojan;Specifically, the Trojan aims to divert traffic packets to unauthorized locations on the NoC. To detect this kind of traffic hijacking, we propose an authentication scheme in which the source and destination addresses are tagged. We develop a custom design for the packet tagging and authentication such that the implementation costs can be greatly reduced. Our experiments on a set of applications show that on average the detection circuitry incurs about 3.37% overhead in area, 2.61% in power, and 0.097% in performance when compared to the baseline design.
基金supported by the National Natural Science Foundation of China under Grant Nos.62072253,62172258,62302238,and 62372245the CCF-Tencent Rhino-Bird Open Research Fund,the Major Science and Technology Demonstration Project of Jiangsu Provincial Key Research and Development Program under Grant No.BE2022798the Postgraduate Research and Practice Innovation Program of Jiangsu Province of China under Grant No.KYCX20_0829.
文摘With the rapid development of operating systems,attacks on system vulnerabilities are increasing.Dynamic link library(DLL)hijacking is prevalent in installers on freeware platforms and is highly susceptible to exploitation by malware attackers.However,existing studies are based solely on the load paths of DLLs,ignoring the attributes of installers and invocation modes,resulting in low accuracy and weak generality of vulnerability detection.In this paper,we propose a novel model,AB-DHD,which is based on an attention mechanism and a bi-directional gated recurrent unit(BiGRU)neural network for DLL hijacking vulnerability discovery.While BiGRU is an enhancement of GRU and has been widely applied in sequence data processing,a double-layer BiGRU network is introduced to analyze the internal features of installers with DLL hijacking vulnerabilities.Additionally,an attention mechanism is incorporated to dynamically adjust feature weights,significantly enhancing the ability of our model to detect vulnerabilities in new installers.A comprehensive“List of Easily Hijacked DLLs”is developed to serve a reference for future studies.We construct an EXEFul dataset and a DLLVul dataset,using data from two publicly available authoritative vulnerability databases,Common Vulnerabilities&Exposures(CVE)and China National Vulnerability Database(CNVD),and mainstream installer distribution platforms.Experimental results show that our model outperforms popular automated tools like Rattler and DLLHSC,achieving an accuracy of 97.79%and a recall of 94.72%.Moreover,17 previously unknown vulnerabilities have been identified,and corresponding vulnerability certifications have been assigned.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62072309 and 6171101225).
文摘Activity hijacking is one of the most powerful attacks in Android. Though promising, all the prior activity hijacking attacks suffer from some limitations and have limited attack capabilities. They no longer pose security threats in recent Android due to the presence of effective defense mechanisms. In this work, we propose the first automated and adaptive activity hijacking attack, named VenomAttack, enabling a spectrum of customized attacks (e.g., phishing, spoofing, and DoS) on a large scale in recent Android, even the state-of-the-art defense mechanisms are deployed. Specifically, we propose to use hotpatch techniques to identify vulnerable devices and update attack payload without re-installation and re-distribution, hence bypassing offline detection. We present a newly-discovered flaw in Android and a bug in derivatives of Android, each of which allows us to check if a target app is running in the background or not, by which we can determine the right attack timing via a designed transparent activity. We also propose an automated fake activity generation approach, allowing large-scale attacks. Requiring only the common permission INTERNET, we can hijack activities at the right timing without destroying the GUI integrity of the foreground app. We conduct proof-of-concept attacks, showing that VenomAttack poses severe security risks on recent Android versions. The user study demonstrates the effectiveness of VenomAttack in real-world scenarios, achieving a high success rate (95%) without users’ awareness. That would call more attention to the stakeholders like Google.
文摘缓冲区溢出漏洞广泛存在于由不安全的高级语言所编写的程序中.利用缓冲区溢出漏洞,攻击者可以实现控制流劫持等危险攻击方式.基于Canary的栈保护技术是处理缓冲区溢出漏洞的一种简单有效且广泛部署的防御手段,然而位置固定和取值相同的特点使其容易被攻击者分析和破解.本文提出一种基于软件多样性的栈保护技术,它以拥有随机化大小和偏移的异构Canary为核心,不仅能直接抵御常规Canary无法处理的泄漏类和覆盖类攻击,而且能构造出各种更加安全的多样性软件系统.实验结果表明,异构Canary在有效提升安全性的同时仅为SPEC CPU 2017基准程序集额外引入了不高于2%的编译开销和平均3.22%的运行开销.
