Distributed Denial-of-Service (DDoS) attack, using random spoofed source addresses, is popular because it can protect the attacker's anonymity. It is very difficult to defent against this attack because it is very hard to differentiate bad traffic from the normal. In this paper, based on the source addresses distribution statistical feature, an effective defense scheme, which can differentiate vicious traffic from normal traffic, is presented. Based on a novel Extended Counting Bloom Filter (ECBF) data structure, this paper proposes an algorithm to identify normal addresses accurately. Once a normal address is sought out, packets from it will be forwarded with high priority, thus, normal traffic is protected. The simulation results show that this scheme can identify legitimate addresses accurately, protect legitimate traffic effectively, and give better protection to valuable long flows. Because the time complexity of the method is 0(1), and it needs several MB memory space, it can be implemented in edge routers or network secure devices like firewalls to defend against random spoofed source address DDoS attacks.%由于能够有效隐藏攻击者,随机伪造源地址分布式拒绝服务攻击被广泛采用.抵御这种攻击的难点在于无法有效区分合法流量和攻击流量.基于此类攻击发生时攻击包源地址的统计特征,提出了能够有效区分合法流量和攻击流量,并保护合法流量的方法.首先设计了一种用于统计源地址数据包数的高效数据结构Extended Counting Bloom Filter(ECBF),基于此,提出了随机伪造源地址分布式拒绝服务攻击发生时合法地址识别算法.通过优先转发来自合法地址的数据包,实现对合法流量的有效保护.采用真实互联网流量进行模拟,实验结果表明,所提方法能精确识别合法地址,有效地保护合法流量,尤其能够较好地保护有价值的交易会话.所提方法的时间复杂性为O(1),并且只需数兆字节的内存开销,可嵌入边界路由器或网络安全设备,如防火墙中,实现随机伪造源地址分布式拒绝服务攻击的在线过滤.
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