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A novel approach to analyze statistically the network traffic raw data is proposed. The huge amount of raw data of actual network traffic from the Intrusion Detection System is analyzed to determine if a traffic is a normal or harmful one. Using the active ports in each host in a network as sensors, the system continuously monitors the incoming packets, and generates its average behaviors at different time scales including its variances. The average region of behaviors at certain time scale is then being used as the baseline of normal traffic. Deploying the exhaustive search based decission system, the system detects the incoming threats to the whole network under supervision.
Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catas
Internet has played a vital role in this modern world, the possibilities and opportunities offered are limitless. Despite all the hype, Internet services are liable to intrusion attack that could tamper the confidentiality and integrity of important
With massive data being generated daily and the ever-increasing interconnectivity of the worlds Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national secu
The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and dis
We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on