Abstract
Over the last couple of months a large number of distributed denial of service (DDoS) attacks have occurred across the world, especially targeting those who provide Web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called intelligent decision prototype (IDP), is proposed. IDP can be used on both probabilistic packet marking (PPM) and deterministic packet marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as data mining, forensic analysis, intrusion detection systems (IDS) and DDoS defense systems.
Highlights
Businesses over the last decade have invested heavily in web technologies to provide better services to their clients and customers
The packet comes into the router, and is analysed by Pre-Marked Decision (PMD) for attributes that make up a Distributed Denial of Service (DDoS) attack
Using the MIT data set, we set out to test to if PMD could detect the DDoS attack packets that we inserted into the data
Summary
Businesses over the last decade have invested heavily in web technologies to provide better services to their clients and customers. With such heavy investment, any form of disruptions to these services can cost a business not just loss of profit and the high cost of repairs to fix the problems. In a ‘general’ DDoS attack, the attacker usually disguises or ‘spoofs’ the IP address section of a packet header in order to hide their identity from their victim. This makes it extremely difficult to track the source of the attack. If PMD decides that the packet shows signs that it is not legitimate, it sends it for packet marking
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