由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定...由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。展开更多
物联网(internet of things,IoT)设备漏洞带来的安全问题引发了研究人员的广泛关注,出于系统稳定性的考虑,设备厂商往往不会及时更新IoT固件中的补丁,导致漏洞对设备安全性影响时间更长;同时,大部分IoT固件文件源码未知,对其进行漏洞检...物联网(internet of things,IoT)设备漏洞带来的安全问题引发了研究人员的广泛关注,出于系统稳定性的考虑,设备厂商往往不会及时更新IoT固件中的补丁,导致漏洞对设备安全性影响时间更长;同时,大部分IoT固件文件源码未知,对其进行漏洞检测的难度更大。基于机器学习的代码比较技术可以有效应用于IoT设备的漏洞检测,但是这些技术存在因代码特征提取粒度粗、提取的语义特征不充分和代码比较范围未进行约束而导致的高误报问题。针对这些问题,提出一种基于神经网络的两阶段IoT固件漏洞检测方法。基于代码的多维特征缩小代码比较范围,提高比较的效率和精确度;再基于代码特征,用神经网络模型对代码相似程度进行学习,从而判断二进制IoT固件的代码与漏洞代码的相似程度,以检测IoT固件中是否存在漏洞,最后实验证明了所提方法在IoT固件检测中的有效性。展开更多
Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly,...Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is increased.The simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.展开更多
文摘由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。
文摘物联网(internet of things,IoT)设备漏洞带来的安全问题引发了研究人员的广泛关注,出于系统稳定性的考虑,设备厂商往往不会及时更新IoT固件中的补丁,导致漏洞对设备安全性影响时间更长;同时,大部分IoT固件文件源码未知,对其进行漏洞检测的难度更大。基于机器学习的代码比较技术可以有效应用于IoT设备的漏洞检测,但是这些技术存在因代码特征提取粒度粗、提取的语义特征不充分和代码比较范围未进行约束而导致的高误报问题。针对这些问题,提出一种基于神经网络的两阶段IoT固件漏洞检测方法。基于代码的多维特征缩小代码比较范围,提高比较的效率和精确度;再基于代码特征,用神经网络模型对代码相似程度进行学习,从而判断二进制IoT固件的代码与漏洞代码的相似程度,以检测IoT固件中是否存在漏洞,最后实验证明了所提方法在IoT固件检测中的有效性。
基金Supported by the Foundation of Tianjin for Science and Technology Innovation(10FDZDGX00400,11ZCKFGX00900)Key Project of Educational Reform Foundation of Tianjin Municipal Education Commission(C03-0809)
文摘Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is increased.The simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.
