基于IAD的防DDoS攻击的实现

DDoS攻击是威胁因特网安全的重要手段,本文提出了一种基于IP地址数据库的实用方法来有效防御DDoS攻击,边界路由器保存所有以往在网络上出现的合法IP地址的记录,当边界路由器业务量过载时,利用这一记录来决定是否接受输入的IP包。     

A Flow Monitoring Scheme to Defend Reduction-of-Quality (RoQ) Attacks in Mobile Ad-hoc Networks

ABSTRACT

Reduction-of-Quality (RoQ) attack is a type of Distributed Denial-of-Service (DDoS) attack that is difficult to detect in current computing systems and networks. These RoQ attacks throttle the throughput heavily and reduce the Quality of Service (QoS) to end systems gradually rather than refusing the clients from the services completely. In this paper, we propose to develop a flow monitoring scheme to defend against such attacks in mobile Ad-hoc networks. Our proposed defense mechanism consists of a flow monitoring table (FMT) at each node to identify the attackers. If the channel continues to be congested because some sender nodes do not reduce their sending rate, it can be found by the destination using the updated FMT. Once the attackers are identified, all packets from those nodes will be blocked. By simulation results, we show that our proposed scheme achieves higher throughput and packet delivery ratio with reduced packet drop for legitimate users.     

Apple's Leopard Versus Microsoft's Windows XP: Experimental Evaluation of Apple's Leopard Operating System with Windows XP-SP2 under Distributed Denial of Service Security Attacks

ABSTRACT

Apple's iMac computers are promoted by Apple Inc. to be secure, safe, virus free, and fast computers. In this experimental paper, we evaluate the security offered by the iMac with its usual Leopard Operating System, against different Distributed Denial of Service (DDoS) attacks in a Gigabit LAN environment. We compared the effect of DDoS attacks on Leopard OS against those on the Window's XP-SP2 when installed on the same iMac platform under the same network attack environment. DDoS-based flooding attacks can originate in a LAN environment or can be from the Internet, which can have an impact on a victim computer with a barrage of Denial of Service (DoS) packet requests, thereby exhausting the resources of the victim computer in processing these requests. To study the impact on iMac computers, we created the corresponding DDoS traffic in a controlled lab environment to test against iMac computer that first deployed Leopard OS. Later, the same iMac platform was made to use Window's XP OS. We compared the behavior of Apple's Leopard OS with Windows's XP-SP2 OS under Ping Flood, ICMP Land, TCP-SYN, Smurf Flood, ARP Flood, and UDP Flood attacks. It was found that the Apple's iMac computer using its usual Leopard operating system crashed even under low bandwidth of ARP-based attack traffic, requiring forced reboot of the iMac computer. Interestingly, when compared with Microsoft's Windows XP-SP2 operating system, deployed on the same iMac platform, the computer was able to sustain the attack and did not crash. Our discovery of this vulnerability shows that Apple's popular operating systems, namely Leopards, commonly deployed on iMacs are prone to crash under ARP-based security attacks. Also in other attacks Windows XP-SP2 was found to have a better performance than Leopard in terms of resource consumption.     

Robust and efficient detection of DDoS attacks for large-scale internet

In recent years, distributed denial of service (DDoS) attacks have become a major security threat to Internet services. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. In this paper, we propose a novel framework to robustly and efficiently detect DDoS attacks and identify attack packets. The key idea of our framework is to exploit spatial and temporal correlation of DDoS attack traffic. In this framework, we design a perimeter-based anti-DDoS system, in which traffic is analyzed only at the edge routers of an internet service provider (ISP) network. Our framework is able to detect any source-address-spoofed DDoS attack, no matter whether it is a low-volume attack or a high-volume attack. The novelties of our framework are (1) temporal-correlation based feature extraction and (2) spatial-correlation based detection. With these techniques, our scheme can accurately detect DDoS attacks and identify attack packets without modifying existing IP forwarding mechanisms at routers. Our simulation results show that the proposed framework can detect DDoS attacks even if the volume of attack traffic on each link is extremely small. Especially, for the same false alarm probability, our scheme has a detection probability of 0.97, while the existing scheme has a detection probability of 0.17, which demonstrates the superior performance of our scheme.     

DDoS攻击和防御机制分类研究*

随着Internet的迅速发展,网络安全问题日益突出,其中分布式拒绝服务（DDoS）攻击对Internet构成巨大威胁。在分析DDoS攻击机理的基础上,对攻击和防御机制进行分类,以便有效地分析、认识分布式拒绝服务攻击行为。     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

防范TCP拒绝服务攻击的高速过滤器

DDoS攻击是因特网目前面临的最严峻的威胁之一.如何快速有效地对其进行防范已经成为一项十分有意义的工作.该文提出了一种TCP Proxy与待响应ACK队列相结合的、能够对TCP绝拒服务攻击进行有效过滤的方法,并用这种方法在Linux内核中实现了一个高速过滤器.实验结果表明,在为TCP传输单独分配带宽的情况下,这种高速过滤器可以有效保护T TCP支持的各种网络服务免受绝拒服务攻击     

基于OpenDayLight的恶意扫描防护技术

针对分布式拒绝服务（DDoS）攻击难以在危害产生之前被检测和防御的问题,提出了一种基于软件定义网络（SDN）的面向恶意扫描的控制层实时防护机制。首先,分析了SDN相比传统网络在网络层防护技术上的优势;其次,针对网络攻击手段——恶意扫描,提出了面向恶意扫描的控制层实时防护机制,该机制在SDN集中控制式架构的基础上,充分利用OpenDayLight （ODL）控制器所提供的表述性状态传递（REST）应用程序编程接口（API）开发外部应用,实现了对底层交换机端口的检测、判定、防护三个环节;最后,对给出的方案在ODL平台上进行了编程实现,并实验测试了恶意扫描的检测防御方案。实验结果表明：当有端口正在对网络进行恶意扫描时,面向恶意扫描的控制层实时防护机制可以及时禁用该端口,实时起到对恶意扫描攻击的防护作用,进而在分布式拒绝服务攻击当中具有破坏性的行为还未开始时就对其进行了预防。     

DoS-DDoS: TAXONOMIES OF ATTACKS,COUNTERMEASURES, AND WELL-KNOWN DEFENSE MECHANISMS IN CLOUD ENVIRONMENT

Cloud computing has become a suitable provider of services for organizations as well as individuals through the Internet. Generally, these services become unavailable because of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks that can deny the legitimate users access to the service delivered by cloud. Taxonomy is an important opportunity for researchers and cloud service providers. Therefore, it provides researchers with a general view about some contributions to understand and ameliorate their limitations and helps cloud service providers to select the best defense strategy to protect their cloud service against DoS and DDoS attacks. In this article, we present taxonomies of DoS and DDoS attacks in cloud environment, countermeasures, and highlight their solutions with another taxonomy of well-known defense mechanisms.     

基于SDS架构的多级DDoS防护机制

随着互联网的高速发展,网络安全的问题越来越严峻。软件定义网络（SDN）的出现为解决网络安全问题提供了全新的解决方案,如软件定义安全（SDS）。在SDS架构的基础上,针对分布式拒绝服务（DDoS）攻击的特点,提出一种新的DDoS防护机制SDS for DDoS。这种防护机制结合了以往检测方式和防护方式的优点,将安全服务原子化,并实现安全策略盒的多级防护策略。在受到DDoS攻击时,机制可以根据检测到的攻击力度进行动态决策,还能先验式地对攻击流量进行阻隔,不仅增加了决策的可信度,还解决了以往所采用的静态防护和后验式防护的不足。实验验证了机制的可行性,能有效地避免服务器受到DDoS攻击,更突出了它在决策时的灵活性和在遭受攻击时的先验性。     

Web服务中基于流量监控的DDoS攻击防范机制

提出一种基于流量监控的针对Web服务的DDoS攻击防范机制。使用Linux内核的安全选项、Linux虚拟服务器、iptables防火墙以及基于类的排队等技术搭建防范DDoS攻击的Web服务器系统环境,设计、实现了流量监控器和分析工具来检测可能发生的DDoS攻击,并降低其危害。实际测试表明,该机制能有效检测和防范常见的针对Web服务的DDoS攻击。     

利用路由器自适应限流防御分布拒绝服务攻击

提出一种自适应路由器限流算法防御分布拒绝服务攻击的机制.该算法的关键是由被攻击者要求经挑选的相距k跳(hop)的上游路由器对目的为被攻击者的数据流进行限流,从而将被攻击者的服务支援在各数据流之间达到一种类最大-最小公平的流量分配.还在一个实际的因特网拓扑上针对攻击数据流和合法数据流的不同分布和流量模型考察了算法的效果.结果表明这种以服务器为中心的路由器限流是对抗分布拒绝服务攻击的一种很有前途的方法.     

Distributed Denial of Service Attacks: A Threat or Challenge

In today’s cyber world, the Internet has become a vital resource for providing a plethora of services. Unavailability of these services due to any reason leads to huge financial implications or even consequences on society. Distributed Denial of Service (DDoS) attacks have emerged as one of the most serious threats to the Internet whose aim is to completely deny the availability of different Internet based services to legitimate users. The attackers compromise a large number of Internet enabled devices and gain malicious control over them by exploiting their vulnerabilities. Simplicity of launching, traffic variety, IP spoofing, high volume traffic, involvement of numerous agent machines, and weak spots in Internet topology are important characteristics of DDoS attacks and makes its defense very challenging. This article provides a survey with the enhanced taxonomies of DDoS attacks and defense mechanisms. Additionally, we describe the timeline of DDoS attacks to date and attempt to discuss its impact according to various motivations. We highlighted the general issues, challenges, and current trends of DDoS attack technology. The aim of the article is to provide complete knowledge of DDoS attacks and defense mechanisms to the research community. This will, in turn, help to develop a powerful, effective, and efficient defense mechanism by filling the various research gaps addressed in already proposed defense mechanisms.     

浅谈运营商异常流量清洗手段的部署和运用

随着互联网的高速发展,其已经渗入到人们生活的方方面面,对经济和社会有着重大的影响。但近年来出现了大量的DDoS攻击事件,给互联网带来了很大冲击,严重影响了业务的可用性、用户的感知,以及给营运商造成了重大经济损失。在大流量的DDoS攻击面前,传统的安全防护设备显得那么无能为力,如何有效抵御DDoS攻击是每个运营商无法回避的难题。在深入分析DDoS攻击特征的前提下,探索性地将异常流量清洗设备引入到运营商的网络中,并成功防御了多次DDoS攻击事件,为解决难题指出了一条可行道路。     

sShield: small DDoS defense system using RIP-based traffic deflection in autonomous system

DDoS (distributed denial of service) attacks have gradually increased and have become more sophisticated. There have been several methods for defending against these attacks. However, because the types and scales of DDoS attacks have been diversified, it has become important to defend against DDoS attacks not only in main networks, but also in small scale networks such as AS (autonomous system). We have designed a DDoS defense system working inside AS without either changing the network structure or modifying the router. For this purpose, we have applied the Shield mechanism, which deals with the location problem in DDoS defense, and utilizes the routing updates protocol called RIP (routing information protocol), a representative protocol of IGP (interior gateway protocol). Moreover, we have also conducted experiments by using simulations to find the optimal number and locations of deployed systems.     

Improving the resilience of content distribution networks to large scale distributed denial of service attacks

Distributed Denial of Service (DDoS) attacks remain a daunting challenge for Internet service providers. Previous work on countering these attacks has focused primarily on attacks at a single server location and the associated network infrastructure. Increasingly, however, high-volume sites are served via content distribution networks (CDNs). In this paper, we propose two mechanisms to withstand and deter DDoS attacks on CDN-hosted Web sites and the CDN infrastructure. First, we present a novel CDN request routing algorithm which allows CDN proxies to effectively distinguish attacks from the requests from actual users. The proposed scheme, based on the keyed hash function, can significantly improve the resilience of CDNs to DDoS attacks. In particular, the resilience of a CDN, consisting of n proxies, becomes O(n²) with the proposed approach, when compared to a site hosted by a single server. We present performance numbers from a controlled test environment to show that the proposed approach is effective. Second, we introduce novel site allocation algorithms based on the well-established theory on binary codes. The proposed allocation algorithm guarantees an upper bound on the level of service outage of a CDN-hosted site even when a DoS attack on another site on the same CDN has been successful. Together, our schemes significantly improve the resilience of the Web sites hosted by CDNs, and complement other work on countering DoS.     

基于软件定义物联网的分布式拒绝服务攻击检测方法

由于物联网（IoT）设备众多、分布广泛且所处环境复杂,相较于传统网络更容易遭受分布式拒绝服务（DDoS）攻击,针对这一问题提出了一种在软件定义物联网（SD-IoT）架构下基于均分取值区间长度-K均值（ELVR-Kmeans）算法的DDoS攻击检测方法。首先,利用SD-IoT控制器的集中控制特性通过获取OpenFlow交换机的流表,分析SD-IoT环境下DDoS攻击流量的特性,提取出与DDoS攻击相关的七元组特征;然后,使用ELVR-Kmeans算法对所获取的流表进行分类,以检测是否有DDoS攻击发生;最后,搭建仿真实验环境,对该方法的检测率、准确率和错误率进行测试。实验结果表明,该方法能够较好地检测SD-IoT环境中的DDoS攻击,检测率和准确率分别达到96.43%和98.71%,错误率为1.29%。     

Intrusion Detection Systems of ICMPv6-based DDoS attacks

Neural Computing and Applications - Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are thorny and a grave problem of today’s Internet, resulting in economic damages...     

A hybrid machine learning approach for detecting unprecedented DDoS attacks

Service availability plays a vital role on computer networks, against which Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year. Machine learning (ML) is a promising approach widely used for DDoS detection, which obtains satisfactory results for pre-known attacks. However, they are almost incapable of detecting unknown malicious traffic. This paper proposes a novel method combining both supervised and unsupervised algorithms. First, a clustering algorithm separates the anomalous traffic from the normal data using several flow-based features. Then, using certain statistical measures, a classification algorithm is used to label the clusters. Employing a big data processing framework, we evaluate the proposed method by training on the CICIDS2017 dataset and testing on a different set of attacks provided in the more up-to-date CICDDoS2019. The results demonstrate that the Positive Likelihood Ratio (LR+) of our method is approximately 198% higher than the ML classification algorithms.

     

基于验证与自主学习的主动DDoS防御系统

主动DDoS防御系统(ADDS)主要防御基于网络的DDoS攻击。使用体系防御技术组织防火墙、路由器和中心主机共同防御,弥补了防火墙、路由器和内核级防御程序单独防御的不足;使用分区超时技术使系统具有快速防御DDoS的能力;使用流量分析技术控制路由器,优化网络流量分配;使用连接验证技术消除傀儡机的攻击。多种技术和多层次的防御使系统具有较强的防御DDoS攻击能力。