Network support for IP traceback
IEEE/ACM Transactions on Networking (TON)
An algebraic approach to IP traceback
ACM Transactions on Information and System Security (TISSEC)
IEEE/ACM Transactions on Networking (TON)
Tracing Network Attacks to Their Sources
IEEE Internet Computing
On realistic network topologies for simulation
MoMeTools '03 Proceedings of the ACM SIGCOMM workshop on Models, methods and tools for reproducible network research
An adaptive genetic-based signature learning system for intrusion detection
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The denial-of-service (DoS) attacks with the source IP address spoofing techniques has become a major threat to the Internet. An intrusion detection system is often used to detect DoS attacks and to coordinate with the firewall to block them. However, DoS attack packets consume and may exhaust all the resources, causing degrading network performance or, even worse, network breakdown. A proactive approach to DoS attacks is allocating the original attack host(s) issuing the attacks and stopping the malicious traffic, instead of wasting resources on the attack traffic. In this paper, an ant-based traceback approach is proposed to identify the DoS attack origin. Instead of creating a new type or function or processing a high volume of fine-grained data used by previous research, the proposed traceback approach uses flow level information to identify the origin of a DoS attack. Two characteristics of ant algorithm, quick convergence and heuristic, are adopted in the proposed approach on finding the DoS attack path. Quick convergence efficiently finds out the origin of a DoS attack; heuristic gives the solution even though partial flow information is provided by the network. The proposed method is evaluated through simulation on various network environments and two simulated real networks, NSFNET and DFN. The simulation results show that the proposed method can successfully and efficiently find the DoS attack path in various simulated network environments, with full and partial flow information provided by the networks.