Detecting denial of service attacks with Bayesian classifiers and the random neural network

Denial of Service (DoS) is a prevalent threat in today’s networks. While such an attack is not difficult to launch, defending a network resource against it is disproportionately difficult, and despite the extensive research in recent years, DoS attacks continue to harm. The first goal of any protection scheme against DoS is the detection of its existence, ideally long before the destructive traffic build-up. In this paper we propose a generic approach which uses multiple Bayesian classifiers, and we present and compare four different implementations of it, combining likelihood estimation and the Random Neural Network (RNN). The RNNs are biologically inspired structures which represent the true functioning of a biophysical neural network, where the signals travel as spikes rather than analog signals. We use such an RNN structure to fuse real-time networking statistical data and distinguish between normal and attack traffic during a DoS attack. We present experimental results obtained for different traffic data in a large networking testbed

Classification Denial Of Service (Dos) Attack Using Artificial Neural Network Learning Vector Quantization (Lvq)

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%

Classification Denial of Service (DOS) Attack using Artificial Neural Network Learning Vector Quantization (LVQ)

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%

Surveying port scans and their detection methodologies

Scanning of ports on a computer occurs frequently on the Internet. An attacker performs port scans of IP addresses to find vulnerable hosts to compromise. However, it is also useful for system administrators and other network defenders to detect port scans as possible preliminaries to more serious attacks. It is a very difficult task to recognize instances of malicious port scanning. In general, a port scan may be an instance of a scan by attackers or an instance of a scan by network defenders. In this survey, we present research and development trends in this area. Our presentation includes a discussion of common port scan attacks. We provide a comparison of port scan methods based on type, mode of detection, mechanism used for detection, and other characteristics. This survey also reports on the available datasets and evaluation criteria for port scan detection approaches