DDoS attack detection and defense based on hybrid deep learning model in SDN

Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.     

Research on low-rate DDoS attack of SDN network in cloud environment

Aiming at the problems of low-rate DDoS attack detection accuracy in cloud SDN network and the lack of unified framework for data plane and control plane low-rate DDoS attack detection and defense,a unified framework for low-rate DDoS attack detection was proposed.First of all,the validity of the data plane DDoS attacks in low rate was analyzed,on the basis of combining with low-rate of DDoS attacks in the aspect of communications,frequency characteristics,extract the mean value,maximum value,deviation degree and average deviation,survival time of ten dimensions characteristics of five aspects,to achieve the low-rate of DDoS attack detection based on bayesian networks,issued by the controller after the relevant strategies to block the attack flow.Finally,in OpenStack cloud environment,the detection rate of low-rate DDoS attack reaches 99.3% and the CPU occupation rate is 9.04%.It can effectively detect and defend low-rate DDoS attacks.     

DDoS attack detection method based on conditional entropy and GHSOM in SDN

Software defined networking (SDN) simplifies the network architecture,while the controller is also faced with a security threat of “single point of failure”.Attackers can send a large number of forged data flows that do not exist in the flow tables of the switches,affecting the normal performance of the network.In order to detect the existence of this kind of attack,the DDoS attack detection method based on conditional entropy and GHSOM in SDN (MBCE&G) was presented.Firstly,according to the phased features of DDoS,the damaged switch in the network was located to find the suspect attack flows.Then,according to the diversity characteristics of the suspected attack flow,the quaternion feature vector was extracted in the form of conditional entropy,as the input features of the neural network for more accurate analysis.Finally,the experimental environment was built to complete the verification.The experimental results show that MBCE&G detection method can effectively detect DDoS attacks in SDN network.     

UDM:NFV-based prevention mechanism against DDoS attack on SDN controller

DDoS attack extensively existed have been mortal threats for the software-defined networking (SDN) controllers and there is no any security mechanism which can prevent them yet.Combining SDN and network function virtualization (NFV),a novel preventing mechanism against DDoS attacks on SDN controller called upfront detection middlebox (UDM) was proposed.The upfront detection middlebox was deployed between SDN switch interfaces and user hosts distributed,and DDoS attack packets were detected and denied.An NFV-based method of implementing the upfront middlebox was put forward,which made the UDM mechanism be economical and effective.A prototype system based on this mechanism was implemented and lots experiments were tested.The experimental results show that the UDM mechanism based on NFV can real-time and effectively detect and prevent against DDoS attacks on SDN controllers.     

Efficient and low‐cost defense against distributed denial‐of‐service attacks in SDN‐based networks

Distributed are common threats in many networks, where attackers attempt to make victim servers unavailable to other users by flooding them with worthless requests. These attacks cannot be easily stopped by firewalls, since they forge lots of connections to victims with various IP addresses. The paper aims to exploit the software‐defined networking (SDN) technique to defend against DDoS attacks. However, the controller has to handle lots of connections launched by DDoS attacks, which burdens it with a heavy load and degrades SDN's performance. Therefore, the paper proposes an efficient and low‐cost DDoS defense (ELD) mechanism for SDN. It adopts a nested reverse‐exponential data storage scheme to help the controller efficiently record the information of packets in the limited memory. Once there are many packets with high IP variability sent to a certain server and this situation lasts for a while, then a DDoS attack is likely happening. In this case, the controller asks switches to block malicious connections by installing flow rules. Experimental results verify that the ELD mechanism rapidly recognizes protocol‐based DDoS attacks and stops them in time, including TCP SYN flood, UDP flood, and ICMP flood, and also greatly reduces the overhead for the controller to defend against attacks. Moreover, ELD can distinguish DDoS flows from legitimate ones with similar features such as elephant flows and impulse flows, thereby eliminating false alarms.     

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.     

Secure access of resources in software‐defined networks using dynamic access control list

Software‐defined networking (SDN) creates a platform to dynamically configure the networks for on‐demand services. SDN can easily control the data plane and the control plane by implementing the decoupling concept. SDN controller will regulate the traffic flow and creates the new flow label based on the packet dump received from the OpenFlow virtual switches. SDN governs both data information and control information toward the destination based on flow label, but it does not contain security measure to restrict the malicious traffic. The malicious denial‐of‐service (DoS) attack traffic is generated inside the SDN environment; it leads to the service unavailability. This paper is mainly focused on the detection of DoS attacks and also mitigates the malicious traffic by dynamically configuring the firewall. The SDN with dynamic access control list properties is emulated by mininet, and the experimental results exemplify the service unavailable gap between acceptance and rejection ratio of the packets.     

基于SDN的DDoS攻击检测和防护机制研究

随着云计算和大数据等技术的发展,传统网络已经无法满足飞速发展的需求,软件定义网络(SDN)的出现带来了网络发展的变革,虽然SDN已经得到一定的应用,但是其仍处在研究完善阶段.本文阐述了SDN的关键技术以及主要协议,分析了SDN面临的安全问题,提出了一种基于流表特征的DDoS攻击检测方法,并给出了对应的攻击缓解方案.     

Analysis of area-congestion-based DDoS attacks in ad hoc networks

Increased instances of distributed denial of service (DDoS) attacks on the Internet have raised questions on whether and how ad hoc networks are vulnerable to such attacks. This paper studies the special properties of such attacks in ad hoc networks. We examine two types of area-congestion-based DDoS attacks – remote and local attacks – and present in-depth analysis on various factors and attack constraints that an attacker may use and face. We find that (1) there are two types of congestion – self congestion and cross congestion – that need to be carefully monitored; (2) the normal traffic itself causes significant packet loss in addition to the attack impacts in both remote and local attacks; (3) the number of flooding nodes has major impacts on remote attacks while, the load of normal traffic and the position of flooding nodes are critical to local attacks; and (4) given the same number of flooding nodes and attack loads, a remote DDoS attack can cause more damage to the network than a local DDoS attack.     

TPAAD: Two-phase authentication system for denial of service attack detection and mitigation using machine learning in software-defined network

Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial-of-service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two-Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K-nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two-Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.     

DDoS protection with stateful software‐defined networking

Distributed denial of service (DDoS) attacks represent one of the most critical security challenges facing network operators. Software‐defined networking (SDN) permits fast reactions to such threats by dynamically enforcing simple forwarding/blocking rules as countermeasures. However, the centralization of the control plane requires that the SDN controller, besides network management operations, should also collect information to identify and mitigate the security menaces. A major drawback of this approach is that it may overload the controller and the control channel. On the other hand, stateful SDN represents a new concept, developed to improve reactivity and offload the controller by delegating local treatments to the switches. In this article, we embrace this paradigm to protect end‐hosts from DDoS attacks. We propose StateSec, a novel approach based on in‐switch processing capabilities to detect and mitigate flooding threats. StateSec monitors packets matching configurable traffic features without resorting to the controller. By feeding an entropy‐based detection algorithm with such monitoring features, it detects and mitigates several threats such as (D)DoS with high accuracy. We implemented StateSec in an SDN platform comparing it with state‐of‐the‐art approaches. We show that StateSec is far more efficient: It achieves very accurate detection levels, reducing at the same time the control plane overhead. We have also evaluated the memory footprint of StateSec for a possible use in production. Finally, we deployed StateSec over a real network to tune its parameters and assess its suitability to real‐world deployments.     

软件定义网络中基于密码标识的报文转发验证机制

针对软件定义网络(SDN)中缺乏安全高效的数据来源验证机制问题,该文提出基于密码标识的报文转发验证机制。首先,建立基于密码标识的报文转发验证模型,将密码标识作为IP报文进出网络的通行证。其次,设计SDN批量匿名认证协议,将SDN控制器的验证功能下放给SDN交换机,由SDN交换机进行用户身份验证和密码标识验证,快速过滤伪造、篡改等非法报文,提高SDN控制器统一认证与管理效率,同时可为用户提供条件隐私保护。提出基于密码标识的任意节点报文抽样验证方案,任何攻击者无法通过推断采样来绕过报文检测,确保报文的真实性的同时降低其处理延迟。最后,进行安全性分析和性能评估。结果表明该机制能快速检测报文伪造和篡改及抵抗ID分析攻击,但同时引入了大约9.6%的转发延迟和低于10%的通信开销。     

基于深度学习的实时DDoS攻击检测

分布式拒绝服务(DDoS)攻击是一种分布式、协作式的大规模网络攻击方式,提出了一种基于深度学习的DDoS攻击检测方法,该方法包含特征处理和模型检测两个阶段:特征处理阶段对输入的数据分组进行特征提取、格式转换和维度重构;模型检测阶段将处理后的特征输入深度学习网络模型进行检测,判断输入的数据分组是否为DDoS攻击分组.通过ISCX2012数据集训练模型,并通过实时的DDoS攻击对模型进行验证.结果表明,基于深度学习的DDoS攻击检测方法具有高检测精度、对软硬件设备依赖小、深度学习网络模型易于更新等优点.     

Lightweight solutions to counter DDoS attacks in software defined networking

Wireless Networks - A distributed denial of service (DDoS) attack on any of the major components (e.g., controller, switches, and southbound channel) of software defined networking (SDN)...     

分布式DNS反射DDoS攻击检测及控制技术

分布式DNS反射DDoS攻击已经成为拒绝服务攻击的主要形式之一,传统的基于网络流量统计分析和网络流量控制技术已经不能满足防护需求。提出了基于生存时间值（TTL）智能研判的DNS反射攻击检测技术,能够准确发现伪造源IP地址分组;基于多系统融合的伪造源地址溯源阻断技术,从源头上阻断攻击流量流入网络。     

一种SDN中基于熵值计算的异常流量检测方法

摘要：软件定义网络（software defined networking,SDN）是一种新型网络创新架构,其分离了控制平面与转发平面,使得网络管理更为灵活。借助SDN控制与转发分离的思想,在SDN基础上引入一个集中式安全中心,在数据平面设备上采集数据,用于对网络流量进行分析,通过熵值计算和分类算法判断异常流量行为。对于检测到的网络异常情况,安全中心通过与SDN控制器的接口通告SDN控制器上的安全处理模块,进行流表策略的下发,进而缓解网络异常行为。通过本系统可以在不影响SDN控制器性能的情况下,快速检测网络中的异常行为,并通过SDN下发流表策略对恶意攻击用户进行限制,同时对SDN控制器进行保护。     

Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

     

一种新型拒绝服务攻击的研究

Shrew DDoS(Distributed Denial of Service)攻击是一种新型的DDoS攻击,也称低速率DDoS攻击。它是利用TCP超时重传机制的漏洞,通过估计合法TCP流的RTO(Retransmission timeout)作为低速率攻击发包的周期T,周期性的发送短脉冲,使得攻击流可以周期性地占用网络带宽,这样就会让合法的TCP流总是认为网络的负担很重,造成所有受其影响的TCP流进入超时重传状态,最终使得受害主机的吞吐量大幅度降低,从而达到攻击目的。由于其攻击速率低,可以躲避传统的高速率攻击防御机制。这种新型拒绝服务攻击具有隐蔽性好、效果明显的特点。     

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。     

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.