基于802.1x协议的校园网ARP欺骗防御

针对校园网等局域网络中,利用网络协议中的基础协议——ARP协议使用漏洞的ARP欺骗攻击,提出定期更新客户端主机ARP列表和利用对802.1x协议的认证功能进行适当扩展,设计客户端异常流量检测模块的防御措施,并用实验方法验证对ARP欺骗攻击防御效果。     

ARP欺骗/MITM攻击在二层交换机上的防范

本文主要针对目前非常典型的二层攻击和欺骗说明如何在思科交换机上组合运用和部署Dynamic ARP Inspection(DAI)技术,从而实现防止在交换环境中实施"中间人"攻击、地址欺骗等,更具意义的是通过Dynamic ARP Inspection(DAI)技术的部署可以简化地址管理,直接跟踪用户IP和对应的交换机端口;防止IP地址冲突。同时对于大多数对二层网络造成很大危害的具有地址扫描、欺骗等特征的病毒可以有效的报警和隔离。     

以太网安全机制

以太网是目前最常见的用作接入网和校园网的传输网络,因此,大多数用户终端通过以太网接入Internet,以太网的安全性直接影响着整个网络的安全性,本文详细讨论了以太网多种用于防止黑客攻击的安全机制。     

基于 TimeGAN-LSTM 的无人机 GPS 欺骗干扰检测模型

针对无人机易受GPS欺骗干扰的问题,提出一种基于长短时记忆法(LSTM)的无人机全球定位系统(GPS)欺骗干扰检测模型。为了提高模型训练精度,首先利用时序生成对抗网络(TimeGAN)对训练数据集进行了数据增强工作,弥补了训练数据量的不足,还对比了增强数据集与原始数据集的性能差距。然后搭建了LSTM模型,在仿真实验下TimeGAN+LSTM模型获得的准确率、精确率、召回率和F1值分别为98.08%、98.55%、98.07%和98.31%。最后与传统机器学习模型进行比较,对比结果证明,提出的欺骗干扰检测模型拥有更好的性能指标。该模型可实现对无人机GPS欺骗干扰信号的有效检测。     

说话人验证系统攻击方法的研究现状及展望

自动说话人验证（Automatic speaker verification,ASV）技术的发展正在深刻地影响和改变着当前的人机交互系统,ASV作为一些智能设备的语音核心功能,可以接受目标说话人的语音并准确识别出该说话人的身份。近年来,人工智能技术的快速进展推动了ASV系统实现跨越式发展。然而,随着人工神经网络和深度学习技术的发展,越来越多的研究者开始研究如何攻击ASV系统。如何通过对原始语音进行一系列处理实现对ASV系统的攻击,是近年来语音领域研究的一个热点问题。目前,对ASV系统的攻击方法大致可分为欺骗攻击（Spoofing attack）和对抗攻击（Adversarial attack）两大类。本文对两大类的典型方法和基本原理进行综述,梳理了目前一些攻击手段中存在的若干问题,揭示了ASV系统存在的安全隐患,对今后ASV系统安全性的发展做了简要的展望,并为未来进一步提高ASV系统的安全性和可靠性提供了参考。     

Physiological-physical feature fusion for automatic voice spoofing detection

Biometric speech recognition systems are often subject to various spoofing attacks, the most common of which are speech synthesis and speech conversion attacks. These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks, which can compromise the security of this system. Researchers have made many efforts to address this problem, and the existing studies have used the physical features of speech to identify spoofing attacks. However, recent studies have shown that speech contains a large number of physiological features related to the human face. For example, we can determine the speaker’s gender, age, mouth shape, and other information by voice. Inspired by the above researches, we propose a spoofing attack recognition method based on physiological-physical features fusion. This method involves feature extraction, a densely connected convolutional neural network with squeeze and excitation block (SE-DenseNet), and feature fusion strategies. We first extract physiological features in audio from a pre-trained convolutional network. Then we use SE-DenseNet to extract physical features. Such a dense connection pattern has high parameter efficiency, and squeeze and excitation blocks can enhance the transmission of the feature. Finally, we integrate the two features into the classification network to identify the spoofing attacks. Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection. In the logical access scenario, our model improves the tandem decision cost function and equal error rate scores by 5% and 7%, respectively, compared to existing methods.     

基于深度残差神经网络的GNSS接收机干扰抑制方案

在各种压制式和欺骗式干扰随机存在的全球卫星导航系统（Global navigation satellite system, GNSS）复杂应用环境下,传统先估计干扰信号参数再抵消的干扰抑制方法需要针对不同类型的干扰设计专门的参数估计和抵消算法,设计工作量大且缺乏通用性。为此本文提出了一种基于深度残差神经网络的干扰抑制方案。首先,针对典型的GNSS干扰类型搭建和训练了相应的残差神经网络,实现从受扰接收信号中直接提取有用卫星信号。然后,结合干扰分类识别结果,将对一维接收信号进行短时傅里叶变换（Short-time fourier transform, STFT）预处理后的时频谱二维信号送入与干扰类型相对应的残差网络,网络输出消除了干扰信号影响的有用卫星信号的时频二维谱。该方案无需对不同类型的干扰采用不同的参数估计和干扰抵消方法,对各类压制干扰和欺骗信号均采用相同的处理流程。实验结果表明相比于先估计干扰信号参数再进行抵消的干扰抑制方案,所提方案对各种GNSS干扰类型均具有较好的抑制效果,具备一定的通用性。     

基于HTTP文件下载欺骗系统的设计与实现

文章提出了一种基于HTTP文件下载欺骗技术的设计方案,能监视局域网用户行为。当目标主机开始执行文件下载时,利用TCP/IP协议没有对源IP地址执行合法性认证的缺陷,创建源IP地址为Web服务器IP地址的HTTP 302重定向数据包。通过重定向目标主机下载的资源文件,和目标主机完成文件下载通信。重定向后的数据文件是后门程序,攻击主机能通过它得到目标主机的管理权限,进而控制目标主机的网络动作。该方案具有很好的隐蔽性。最后进行了测试,测试结果表明该系统的研究思路是正确的。     

3T‐FASDM: Linear discriminant analysis‐based three‐tier face anti‐spoofing detection model using support vector machine

In recent years, to solve the problem of face spoofing, momentous work has been done in this field, but still, there is a need for establishing counter measures to the biometric spoofing attacks. Although trained and evaluated on different databases, impressive results have been achieved in existing face anti‐spoofing techniques, but biometric authentication is a very significant problem as imposters are using lots of reconstructed samples or fake synthetic material or structure that can be used for various attack purposes. For the first time, to the best of our knowledge, this paper explains the security for face anti‐spoofing detection using linear discriminant analysis and validates the results by calculating HTER and accuracy on different databases (i.e., REPLAY ATTACK and CASIA). The proposed model, that is, three‐tier face anti‐spoofing detection model (3T‐FASDM), is used for the detection of the fake biometric user and works well for real‐time applications. The proposed methods tested on a set of state‐of‐the‐art anti‐spoofing features for the face mode gives a very low degree of complexity as 26 general image quality measures are applied to differentiate among legitimate and imposter samples. The outcomes obtained from publically available data show that this technique has improved performance and accuracy by analyzing the HTER and machine learning classifiers that are helpful to differentiate among real and fake traits.     

无人机上行链路抗欺骗干扰方案设计

电子对抗技术的发展使欺骗干扰日益成为无人机通信系统中面临的现实威胁,然而目前尚无公开文献研究无人机上行链路中的抗欺骗干扰技术.针对上述问题,研究了欺骗干扰存在时的上行链路接收信号模型,并从信号处理层面和信息处理层面相结合的角度,结合无人机上行链路的特点,提出了一种可靠的无人机上行链路抗欺骗干扰方案.并通过Matlab和C语言对所提方案进行了建模仿真,仿真结果显示,该方案可有效地对欺骗信号进行检测、分类和消除,并可通过认证技术来保证接收信息的真实性.研究表明,在信号处理阶段对欺骗干扰信号进行处理,在信息处理阶段对真实信息进行认证,可有效避免欺骗干扰的影响.