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Denial of service attacks (DoS) are a common threat to many online services. These attacks aim to overcome the availability of an online service with massive traffic from multiple sources. By spoofing legitimate users, an attacker floods a target system with a high quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks problem and attempts to prevent it are explored. The attacks categories and existing countermeasures based on preventing, detecting, and responding are reviewed. Henceforward, a neural network learning algorithms and statistical analysis are utilized into the designing of our proposed packet filtering system.