随着社交直播类手机应用(Application,App)软件的兴起,它们所面临的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击威胁也日益严重。传统的高防服务器虽然能够抵御部分攻击,但存在严重的延迟和卡顿问题,容易误封正常用户,难...随着社交直播类手机应用(Application,App)软件的兴起,它们所面临的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击威胁也日益严重。传统的高防服务器虽然能够抵御部分攻击,但存在严重的延迟和卡顿问题,容易误封正常用户,难以满足当下的安全需求。针对这些问题,文章提出了一种基于软件开发工具套件(Software Development Kit,SDK)的分布式云集群防护方案,通过部署大量分布式节点和SDK集成,实现了无上限防御DDoS和挑战黑洞(Challenge Collapsar,CC)攻击的能力,同时提升了用户的访问速度与体验。展开更多
为提高分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检出率,设计基于机器学习的无线网络DDoS攻击检测方法。首先,结合攻击时间序列构建无线网络DDoS攻击检测模型,利用深度学习设计无线网络DDoS攻击检测机制;其次,通过异常...为提高分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检出率,设计基于机器学习的无线网络DDoS攻击检测方法。首先,结合攻击时间序列构建无线网络DDoS攻击检测模型,利用深度学习设计无线网络DDoS攻击检测机制;其次,通过异常流量判断,对照相应的流表特征信息完成分类检测;最后,进行实验分析。实验结果表明,该方法的DDoS攻击检出率较低,优于对照组。展开更多
分布式拒绝服务攻击(distributed denial of service,DDoS)是网络安全领域的一大威胁.作为新型网络架构,软件定义网络(software defined networking,SDN)的逻辑集中和可编程性为抵御DDoS攻击提供了新的思路.本文设计并实现了一个轻量级...分布式拒绝服务攻击(distributed denial of service,DDoS)是网络安全领域的一大威胁.作为新型网络架构,软件定义网络(software defined networking,SDN)的逻辑集中和可编程性为抵御DDoS攻击提供了新的思路.本文设计并实现了一个轻量级的SDN环境下的DDoS攻击检测和缓解系统.该系统使用熵值检测方法,并通过动态阈值进行异常判断.若异常,系统将使用更精确的决策树模型进行检测.最后,控制器通过计算流的包对称率确定攻击源,并下发阻塞流表项.实验结果表明,该系统能够及时响应DDoS攻击,具有较高的检测成功率,并能够有效遏制攻击.展开更多
分布式拒绝攻击(distributed denial of service,DDoS)作为一种传统的网络攻击方式,依旧对网络安全存在着较大的威胁.本文研究基于高性能网络安全芯片SoC+IP的构建模式,针对网络层DDoS攻击,提出了一种从硬件层面实现的DDoS攻击识别方法...分布式拒绝攻击(distributed denial of service,DDoS)作为一种传统的网络攻击方式,依旧对网络安全存在着较大的威胁.本文研究基于高性能网络安全芯片SoC+IP的构建模式,针对网络层DDoS攻击,提出了一种从硬件层面实现的DDoS攻击识别方法.根据硬件协议栈设计原理,利用逻辑电路门处理网络数据包进行拆解分析,随后对拆解后的信息进行攻击判定,将认定为攻击的数据包信息记录在攻击池中,等待主机随时读取.并通过硬件逻辑电路实现了基于该方法的DDoS攻击识别IP核(intellectual property core),IP核采用AHB总线配置寄存器的方式进行控制.在基于SV/UVM的仿真验证平台进行综合和功能性测试.实验表明,IP核满足设计要求,可实时进行DDoS攻击识别检测,有效提高高性能网络安全芯片的安全防护功能.展开更多
Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks...Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.展开更多
Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global infor...Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.展开更多
当前的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检测矩阵多为单向的,攻击检测的范围会受到限制。为此,提出基于深度强化学习的DDoS攻击检测方法。首先,根据实际的攻击检测需求及标准,提取初始DDoS攻击特征;其次,打破攻...当前的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检测矩阵多为单向的,攻击检测的范围会受到限制。为此,提出基于深度强化学习的DDoS攻击检测方法。首先,根据实际的攻击检测需求及标准,提取初始DDoS攻击特征;其次,打破攻击检测范围的限制,设计多阶深度检测矩阵;最后,构建深度强化学习DDoS攻击检测模型,采用自适应判别的方法实现DDoS攻击检测处理。测试结果表明,最终得出的DDoS攻击检测F1值均可以达到0.5以上。展开更多
在互联网时代,应用层的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击已经成为公共网络的一大威胁,导致许多服务器无法提供服务并遭受严重破坏。为了应对这类攻击,提出一种综合防范策略。分析攻击行为的原理和方式,了解用户...在互联网时代,应用层的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击已经成为公共网络的一大威胁,导致许多服务器无法提供服务并遭受严重破坏。为了应对这类攻击,提出一种综合防范策略。分析攻击行为的原理和方式,了解用户行为的差异性,设计流量监控系统,实时监测网络流量,并在检测到异常流量时及时警示管理员采取应对措施。此外,通过维护Web服务器的黑名单和使用数据过滤等技术,有效屏蔽不必要的流量。通过综合运用这些策略,可以有效防范应用层的分布式拒绝服务攻击,确保服务器的正常运行。展开更多
分布式拒绝服务(Distributed Denial of Service,DDoS)攻击在网络中较为常见,但普通的DDos攻击检测方法难以对其追踪和防范,无法充分地考虑算法误差调整参数,导致检测精度较低。为此,提出基于反向传播(Back Propagation,BP)神经网络的D...分布式拒绝服务(Distributed Denial of Service,DDoS)攻击在网络中较为常见,但普通的DDos攻击检测方法难以对其追踪和防范,无法充分地考虑算法误差调整参数,导致检测精度较低。为此,提出基于反向传播(Back Propagation,BP)神经网络的DDos攻击自主检测方法,分析DDos攻击特点,采用信源地址、目标地址、包协议等数据包信息,提取DDoS攻击网络特征。采用误差BP算法进行参数训练,采用梯度下降法对各参数进行更新,利用BP神经网络进行DDos攻击自主检测。实验结果表明,通过对DDoS攻击的检测,该方法的检测准确率达到93.87%,并且具有良好的泛化性能。展开更多
分析了非结构化P2P网络DDoS攻击的原理,借鉴蚁群算法的思想,为每个节点建立了一个资源相似度信息素表,利用这个信息素表,构建了一种防御DDoS攻击的联盟模型——AntDA(ant colony based defense-association),并讨论了应用AntDA模型进行...分析了非结构化P2P网络DDoS攻击的原理,借鉴蚁群算法的思想,为每个节点建立了一个资源相似度信息素表,利用这个信息素表,构建了一种防御DDoS攻击的联盟模型——AntDA(ant colony based defense-association),并讨论了应用AntDA模型进行防御的整个过程。在查询周期模型平台上实现了该模型,通过实验分析,验证了AntDA模型的有效性。展开更多
文摘随着社交直播类手机应用(Application,App)软件的兴起,它们所面临的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击威胁也日益严重。传统的高防服务器虽然能够抵御部分攻击,但存在严重的延迟和卡顿问题,容易误封正常用户,难以满足当下的安全需求。针对这些问题,文章提出了一种基于软件开发工具套件(Software Development Kit,SDK)的分布式云集群防护方案,通过部署大量分布式节点和SDK集成,实现了无上限防御DDoS和挑战黑洞(Challenge Collapsar,CC)攻击的能力,同时提升了用户的访问速度与体验。
文摘为提高分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检出率,设计基于机器学习的无线网络DDoS攻击检测方法。首先,结合攻击时间序列构建无线网络DDoS攻击检测模型,利用深度学习设计无线网络DDoS攻击检测机制;其次,通过异常流量判断,对照相应的流表特征信息完成分类检测;最后,进行实验分析。实验结果表明,该方法的DDoS攻击检出率较低,优于对照组。
文摘分布式拒绝服务攻击(distributed denial of service,DDoS)是网络安全领域的一大威胁.作为新型网络架构,软件定义网络(software defined networking,SDN)的逻辑集中和可编程性为抵御DDoS攻击提供了新的思路.本文设计并实现了一个轻量级的SDN环境下的DDoS攻击检测和缓解系统.该系统使用熵值检测方法,并通过动态阈值进行异常判断.若异常,系统将使用更精确的决策树模型进行检测.最后,控制器通过计算流的包对称率确定攻击源,并下发阻塞流表项.实验结果表明,该系统能够及时响应DDoS攻击,具有较高的检测成功率,并能够有效遏制攻击.
文摘Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.
文摘Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.
文摘当前的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击检测矩阵多为单向的,攻击检测的范围会受到限制。为此,提出基于深度强化学习的DDoS攻击检测方法。首先,根据实际的攻击检测需求及标准,提取初始DDoS攻击特征;其次,打破攻击检测范围的限制,设计多阶深度检测矩阵;最后,构建深度强化学习DDoS攻击检测模型,采用自适应判别的方法实现DDoS攻击检测处理。测试结果表明,最终得出的DDoS攻击检测F1值均可以达到0.5以上。
文摘在互联网时代,应用层的分布式拒绝服务(Distributed Denial of Service,DDoS)攻击已经成为公共网络的一大威胁,导致许多服务器无法提供服务并遭受严重破坏。为了应对这类攻击,提出一种综合防范策略。分析攻击行为的原理和方式,了解用户行为的差异性,设计流量监控系统,实时监测网络流量,并在检测到异常流量时及时警示管理员采取应对措施。此外,通过维护Web服务器的黑名单和使用数据过滤等技术,有效屏蔽不必要的流量。通过综合运用这些策略,可以有效防范应用层的分布式拒绝服务攻击,确保服务器的正常运行。
文摘分布式拒绝服务(Distributed Denial of Service,DDoS)攻击在网络中较为常见,但普通的DDos攻击检测方法难以对其追踪和防范,无法充分地考虑算法误差调整参数,导致检测精度较低。为此,提出基于反向传播(Back Propagation,BP)神经网络的DDos攻击自主检测方法,分析DDos攻击特点,采用信源地址、目标地址、包协议等数据包信息,提取DDoS攻击网络特征。采用误差BP算法进行参数训练,采用梯度下降法对各参数进行更新,利用BP神经网络进行DDos攻击自主检测。实验结果表明,通过对DDoS攻击的检测,该方法的检测准确率达到93.87%,并且具有良好的泛化性能。
文摘分析了非结构化P2P网络DDoS攻击的原理,借鉴蚁群算法的思想,为每个节点建立了一个资源相似度信息素表,利用这个信息素表,构建了一种防御DDoS攻击的联盟模型——AntDA(ant colony based defense-association),并讨论了应用AntDA模型进行防御的整个过程。在查询周期模型平台上实现了该模型,通过实验分析,验证了AntDA模型的有效性。