As part of the security within distributed systems, various services and resources need protection from unauthorized use. Remote authentication is the most commonly used method to determine the identity of a remote client. This paper investigates a systematic approach for authenticating clients by three factors, namely password, smart card, and biometrics. A generic and secure framework is proposed to upgrade two-factor authentication to three-factor authentication. The conversion not only significantly improves the information assurance at low cost but also protects client privacy in distributed systems. In addition, our framework retains several practice-friendly properties of the underlying two-factor authentication, which we believe is of independent interest.

Distributed Denial of Service attacks is one of the most challenging areas to deal with in Security. Not only do security managers have to deal with flood and vulnerability attacks. They also have to consider whether they are from legitimate or malicious attackers. In our previous work we developed a framework called bodyguard, which is to help security software developers from the current serialized paradigm, to a multi-core paradigm. In this paper, we update our research work by moving our bodyguard paradigm, into our new Ubiquitous Multi-Core Framework. From this shift, we show a marked improvement from our previous result of 20% to 110% speedup performance with an average cost of 1.5 ms. We also conducted a second series of experiments, which we trained up Neural Network, and tested it against actual DDoS attack traffic. From these experiments, we were able to achieve an average of 93.36%, of this attack traffic.

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.