SEDP‐based detection of low‐rate DoS attacks

Low‐rate Denial of Service (LDoS) is a new type of TCP‐targeted attacks, which attempt to deny bandwidth to TCP flows while sending at sufficiently low‐average rate to elude detection of DoS defense system. Therefore, LDoS attacks are difficult to be detected by routers and counter‐DoS mechanisms. In this paper, an approach of detecting LDoS attacks is proposed by using the technology of signal processing based on the model of spectral energy distribution probability. The proposed approach calculates variances between the incoming traffic of normal TCP and attack flows to a server by using packet sampling sequence within a certain period. The network traffic is converted from the time domain to the frequency domain forming a spectral signal, and the distribution probability of spectral energy is estimated based on spectrum characteristics of rectangular pulses. This approach explores that the energy of LDoS attacks is mostly distributed in the main lobe width while that of normal TCP traffic is just concentrated near zero in frequency domain. Both the spectral energy of normal TCP traffic and LDoS attacks distributed in main lobe are calculated, and an energy threshold is set as decision value based on statistical results according to energy distribution properties. The existence of LDoS attacks is determined and detected by comparing calculated variances with the preset decision threshold value. Tests on the detection performance of the proposed approach were performed in NS‐2 simulation environment, and detection rate was obtained by Hypothesis test. Experiment results show that the proposed approach has higher detection accuracy and less computation consuming. Copyright © 2014 John Wiley & Sons, Ltd.     

The detection method of low-rate DoS attack based on multi-feature fusion

As a new type of Denial of Service (DoS) attacks, the Low-rate Denial of Service (LDoS) attacks make the traditional method of detecting Distributed Denial of Service Attack (DDoS) attacks useless due to the characteristics of a low average rate and concealment. With features extracted from the network traffic, a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper. An attack feature set containing the Acknowledge character(ACK) sequence number, the packet size, and the queue length is used to classify normal and LDoS attack traffics. Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor (KNN) classifier separately, and to obtain the decision contour matrix. Then a posteriori probability in the matrix is fused, and the fusion decision index D is used as the basis of detecting the LDoS attacks. Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.     

DDoS攻击恶意行为知识库构建

针对分布式拒绝服务（distributed denial of service,DDoS）网络攻击知识库研究不足的问题,提出了DDoS攻击恶意行为知识库的构建方法。该知识库基于知识图谱构建,包含恶意流量检测库和网络安全知识库两部分：恶意流量检测库对 DDoS 攻击引发的恶意流量进行检测并分类;网络安全知识库从流量特征和攻击框架对DDoS 攻击恶意行为建模,并对恶意行为进行推理、溯源和反馈。在此基础上基于DDoS 开放威胁信号（DDoS open threat signaling,DOTS）协议搭建分布式知识库,实现分布式节点间的数据传输、DDoS攻击防御与恶意流量缓解功能。实验结果表明,DDoS攻击恶意行为知识库能在多个网关处有效检测和缓解DDoS攻击引发的恶意流量,并具备分布式知识库间的知识更新和推理功能,表现出良好的可扩展性。     

Differentiating Malicious DDoS Attack Traffic from Normal TCP Flows by Proactive Tests

To defend against distributed denial of service (DDoS) attacks, one critical issue is to effectively isolate the attack traffic from the normal ones. A novel DDoS defense scheme based on TCP is hereby contrived because TCP is the dominant traffic for both the normal and lethal flows in the Internet. Unlike most of the previous DDoS defense schemes that are passive in nature, the proposal uses proactive tests to identify and isolate the malicious traffic. Simulation results validate the effectiveness of our proposed scheme     

Detection method of LDoS attacks based on combination of ANN ＆ KPCA

Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol.Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods.According to characteristics of LDoS attacks,a new LDoS queue future was proposed from the router queue,the kernel principal component analysis (KPCA) method was combined with neural network,and a new method was present to detect LDoS attacks.The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network.For the good sell-learning ability,BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack.Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity,which helps the design of high performance router.     

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

As a special type of denial of service (DoS) attacks, the TCP‐targeted low‐rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test‐bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.     

基于IXP 1200平台的DDoS攻击防御研究

分布式拒绝服务攻击（DDoS）已经成为互联网最大的威胁之一.提出了一种基于Intel IXP1200网络处理器平台的DDoS防御系统的设计方案,并实际实现了一个防御系统D-Fighter.提出了解决DDoS攻击的两个关键技术：数据包认证和细微流量控制的原理和方法,并在D-Fighter中设计实现.经过实际网络测试环境的应用测试表明,D-Fighter达到了设计目标,对DDoS攻击的防御有较好的效果.     

LDDoS attack detection method based on wavelet decomposition and sliding windows

As a special type of distributed denial of service (DDoS) attacks, the low-rate DDoS (LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.     

ALPi: A DDoS Defense System for High-Speed Networks

Distributed denial-of-service (DDoS) attacks pose a significant threat to the Internet. Most solutions proposed to-date face scalability problems as the size and speed of the network increase, with no widespread DDoS solution deployed in the industry. PacketScore has been proposed as a proactive DDoS defense scheme, which detects DDoS attacks, differentiates attack packets from legitimate ones with the use of packet scoring (where the score of a packet is calculated based on attribute values it possesses), and discards packets whose scores are lower than a dynamic threshold. In this paper, we propose ALPi, a new scheme which extends the packet scoring concept with reduced implementation complexity and enhanced performance. More specifically, a leaky-bucket overflow control scheme simplifies the score computation, and facilitates high-speed implementation. An attribute-value-variation scoring scheme analyzes the deviations of the current traffic attribute values, and increases the accuracy of detecting and differentiating attacks. An enhanced control-theoretic packet discarding method allows both schemes to be more adaptive to challenging attacks such as those with ever-changing signatures and intensities. When combined together, the proposed extensions not only greatly reduce the memory requirement and implementation complexity but also substantially improve the accuracies in attack detection and packet differentiation. This makes ALPi an attractive DDoS defense system amenable for high-speed hardware implementation.     

Low-rate TCP-targeted denial of service attacks and counter strategies

Denial of Service attacks are presenting an increasing threat to the global inter-networking infrastructure. While TCP's congestion control algorithm is highly robust to diverse network conditions, its implicit assumption of end-system cooperation results in a well-known vulnerability to attack by high-rate non-responsive flows. In this paper, we investigate a class of low-rate denial of service attacks which, unlike high-rate attacks, are difficult for routers and counter-DoS mechanisms to detect. Using a combination of analytical modeling, simulations, and Internet experiments, we show that maliciously chosen low-rate DoS traffic patterns that exploit TCP's retransmission timeout mechanism can throttle TCP flows to a small fraction of their ideal rate while eluding detection. Moreover, as such attacks exploit protocol homogeneity, we study fundamental limits of the ability of a class of randomized timeout mechanisms to thwart such low-rate DoS attacks.     

基于HMM的分布式拒绝服务攻击检测方法

文章根据分布式拒绝服务攻击（DDoS）的本质特点，提出了一种基于隐马尔可夫模型（HMM）的DDoS攻击检测方法。该方法通过IP地址信息库．保存当前常用服务的源IP地址，然后对新到数据包的IP地址用HMM建模。通过离线训练，更新IP地址信息库，优化HMM参数。在线检测时，IP地址信息库在线学习更新，HMM实时检测．并根据检测结果通过边界路由器进行积极响应。实验结果显示，该方法具有很好的检测效果，并能及时响应，保持常用服务的延续性。     

Efficient authenticated key agreement protocols resistant to a denial‐of‐service attack

Malicious intruders may launch as many invalid requests as possible without establishing a server connection to bring server service to a standstill. This is called a denial‐of‐service (DoS) or distributed DoS (DDoS) attack. Until now, there has been no complete solution to resisting a DoS/DDoS attack. Therefore, it is an important network security issue to reduce the impact of a DoS/DDoS attack. A resource‐exhaustion attack on a server is one kind of denial‐of‐service attack. In this article we address the resource‐exhaustion problem in authentication and key agreement protocols. The resource‐exhaustion attack consists of both the CPU‐exhaustion attack and the storage‐exhaustion attack. In 2001, Hirose and Matsuura proposed an authenticated key agreement protocol (AKAP) that was the first protocol simultaneously resistant to both the CPU‐exhaustion attack and the storage‐exhaustion attack. However, their protocol is time‐consuming for legal users in order to withstand the DoS attack. Therefore, in this paper, we propose a slight modification to the Hirose–Matsuura protocol to reduce the computation cost. Both the Hirose–Matsuura and the modified protocols provide implicit key confirmation. Also, we propose another authenticated key agreement protocol with explicit key confirmation. The new protocol requires less computation cost. Because DoS/DDoS attacks come in a variety of forms, the proposed protocols cannot fully disallow a DoS/DDoS attack. However, they reduce the effect of such an attack and thus make it more difficult for the attack to succeed. Copyright © 2005 John Wiley & Sons, Ltd.     

Dual‐collaborative DoS/DDoS mitigation approach in information‐centric mobile Internet

The amount of wireless traffic is increasing at an overwhelming speed. Information‐centric networking (ICN) has been proposed as a promising Future Internet Architecture, which can reduce network traffic by putting data objects toward the edge. It is expected that in information‐centric mobile Internet (ICMI), the wireless traffic can be significantly reduced. Yet, DoS/DDoS attack becomes a critical issue in ICMI by causing wireless gateway blockade. To tackle the problem, we propose a dual‐collaborative DoS/DDoS mitigation approach (DCMA) and advanced DCMA to protect wireless gateways. In the algorithm, the attackers' visiting information including international mobile equipment identity (IMEI) and data object name (DON) are analyzed jointly to accurately identify potential attackers through the collaboration between the Internet and mobile network. In addition, the attacker's behaviors are analyzed centrally, and security strategies are applied distributively throughout wireless edge through the collaboration between wireless core network (CN) and radio access network (RAN). Extensive simulations are performed to verify the effectiveness of the proposed algorithms. The results demonstrate that advanced DCMA can achieve high DDoS and attacker detection probability and small false positive probability.     

Design and analysis of a denial-of-service-resistant quality-of-service signaling protocol for MANETs

Quality-of-service (QoS) signaling protocols for mobile ad hoc networks (MANETs) are highly vulnerable to attacks. In particular, a class of denial-of-service (DoS) attacks can severely cripple network performance with relatively little effort expended by the attacker. A distributed QoS signaling protocol that is resistant to a class of DoS attacks on signaling is proposed. The signaling protocol provides QoS for real-time traffic and employs mechanisms at the medium access control (MAC) layer, which serve to avoid potential attacks on network resource usage. The key MAC layer mechanisms that provide support for the QoS signaling scheme include sensing of available bandwidth, traffic policing, and rate monitoring, all of which are performed in a distributed manner by the mobile nodes. The proposed signaling scheme achieves a compromise between signaling protocols that require the maintenance of per-flow state and those that are completely stateless. The signaling scheme scales gracefully in terms of the number of nodes and/or traffic flows in the MANET. The authors analyze the security properties of the protocol and present simulation results to demonstrate its resistance to DoS attacks.     

Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

Cloud computing is one of the most tempting technologies in today's computing scenario as it provides a cost‐efficient solutions by reducing the large upfront cost for buying hardware infrastructures and computing power. Fog computing is an added support to cloud environment by leveraging with doing some of the less compute intensive task to be done at the edge devices, which reduces the response time for end user computing. But the vulnerabilities to these systems are still a big concern. Among several security needs, availability is one that makes the demanded services available to the targeted customers all the time. Availability is often challenged by external attacks like Denial of service (DoS) and distributed denial of service (DDoS). This paper demonstrates a novel source‐based DDoS mitigating schemes that could be employed in both fog and cloud computing scenarios to eliminate these attacks. It deploys the DDoS defender module which works on a machine learning–based light detection method, present at the SDN controller. This scheme uses the network traffic data to analyze, predict, and filter incoming data, so that it can send the filtered legitimate packets to the server and blocking the rest.     

SOS: an architecture for mitigating DDoS attacks

We propose an architecture called secure overlay services (SOS) that proactively prevents denial of service (DoS) attacks, including distributed (DDoS) attacks; it is geared toward supporting emergency services, or similar types of communication. The architecture uses a combination of secure overlay tunneling, routing via consistent hashing, and filtering. We reduce the probability of successful attacks by: 1) performing intensive filtering near protected network edges, pushing the attack point perimeter into the core of the network, where high-speed routers can handle the volume of attack traffic and 2) introducing randomness and anonymity into the forwarding architecture, making it difficult for an attacker to target nodes along the path to a specific SOS-protected destination. Using simple analytical models, we evaluate the likelihood that an attacker can successfully launch a DoS attack against an SOS-protected network. Our analysis demonstrates that such an architecture reduces the likelihood of a successful attack to minuscule levels. Our performance measurements using a prototype implementation indicate an increase in end-to-end latency by a factor of two for the general case, and an average heal time of less than 10 s.     

DDoS attack detection in IEEE 802.16 based networks

Achieving high data rate transmission, WiMAX has acquired noticeable attention by communication industry. One of the vulnerabilities of the WiMAX network which leads to DDoS attack is sending a high volume of ranging request messages to base station (BS) in the initial network entry process. In the initial network entry process, BS and subscriber station (SS) exchange management messages. Since some of these messages are not authenticated, malicious SSs can attack the network by exploiting this vulnerability which may increase the traffic load of the BS and prevent it from serving the SSs. So, detecting such attacks is one of the most important issues in such networks. In this research, an artificial neural network (ANN) based approach is proposed in order to detect DDoS attacks in IEEE 802.16 networks. Although lots of studies have been devoted to the detection of DDoS attack, some of them focus just on some statistical features of the traffic and some other focus on packets’ headers. The proposed approach exploits both qualitative and quantitative methods. It detects the attack by feeding some features of the network traffic under attack to an appropriate ANN structure. To evaluate the method, first a typical attacked network is implemented in OPNet simulator, and then by using the proposed system, the efficiency of the method is evaluated. The results show that by choosing suitable time series we can classify 93 % of normal traffic and 91 % of attack traffic.     

基于多层检测的网络安全防范系统

提出了一种基于多层的网络安全防范系统,该系统采用多层检测技术：在IP层采用基于聚集的拥塞控制算法（ABCC）,通过限制拥塞信号的宽度,使间接损害达到最小;然后在TCP和UPD层采用基于人体免疫原理的检测技术AIPT,通过建立规则集,将来自网络访问活动与规则集中的规则匹配,以检测出网络入侵行为。仿真实验结果表明,基于本模型的系统不仅能合理地缓解DoS/DDoS攻击,而且能够解决现有的防范系统中高误报率和漏报率以及实时性差、人工干预多的问题。     

基于SNMP和神经网络的DDoS攻击检测

DDoS（Distributed Denial of Service）已经严重威胁计算机网络安全。对DDoS攻击检测的关键是找到能反映攻击流和正常流区别的特征,设计简单高效的算法,实时检测。通过对攻击特点的分析,总结出15个基于SNMP（Simple Network Management Protocol）的检测特征。利用BP神经网络高效的计算性能,设计了基于SNMP和神经网络的DDoS攻击检测模型,提高了检测实时性和准确性。实验表明：该检测模型对多种DDoS攻击都具有很好的检测效果。     

SDN跨层回环攻击的检测与防御

软件定义网络（Software Define Network,SDN）将控制层和数据层进行分离,给网络带来灵活性、开放性以及可编程性.然而,分离引入了新的网络安全问题.我们发现通过构造特定规则可以构造跨层回环攻击,使得数据包在控制器和交换机之间不断循环转发.跨层回环会造成控制器拥塞,并导致控制器无法正常工作.现有的策略一致性检测方案并不能检测跨层回环攻击.为此,本文提出了一种实时检测和防御跨层回环的方法.通过构造基于Packet-out的转发图分析规则路径,从而快速检测和防御回环.我们在开源控制器Floodlight上实现了我们提出的回环检测和防御方案,并在Mininet仿真器上对其性能进行了评估,结果表明本方案能够实时检测并有效防御跨层回环攻击.