IDS Based on Bio-inspired Models
نویسندگان
چکیده
Unsupervised projection approaches can support Intrusion Detection Systems for computer network security. The involved technologies assist a network manager in detecting anomalies and potential threats by an intuitive display of the progression of network traffic. Projection methods operate as smart compression tools and map raw, high-dimensional traffic data into 2-D or 3-D spaces for subsequent graphical display. The paper compares three projection methods, namely, Cooperative Maximum Likelihood Hebbian Learning, Auto-Associative Back-Propagation networks and Principal Component Analysis. Empirical tests on anomalous situations related to the Simple Network Management Protocol (SNMP) confirm the validity of the projection-based approach. One of these anomalous situations (the SNMP community search) is faced by these projection models for the first time. This work also highlights the importance of the time-information dependence in the identification of anomalous situations in the case of the applied methods.
منابع مشابه
The Importance of Time in the Identification of Anomalous Situations by Means of MOVICAB-IDS
Intrusion Detection Systems (IDSs) are a part of the computer security infrastructure of most organizations. They are designed to detect suspect patterns by monitoring and analysing computer network events. Different areas of artificial intelligence, statistical and signature verification techniques have been applied in the field of IDSs. Additionally, visualization tools have been applied for ...
متن کاملDesign of an Intrusion Detection System for Unknown-attacks based on Bio-inspired Algorithms
Signature-based Intrusion Detection System (IDS) can detect only known attacks that have signatures. As new unknown-attacks are appearing continuously, the detection of unknown-attacks has become the essential part of IDS. This paper presents a novel design of IDS by combining two existing bio-inspired machine learning algorithms; Artificial Immune System (AIS) and Ant Clustering Algorithm (ACA...
متن کاملA thesis submitted in fulfilment of requirements for the degree of MASTER OF ENGINEERING
With computer network’s fast penetration into our life, various types of malicious attacks and service abuses increase dramatically. Network security has become one of the big challenges in the modern networks. Intrusion Detection (ID) is one of the active branches in network security research field. Many technologies, such as neural networks, fuzzy logic and genetic algorithms have been applie...
متن کاملHybrid Bio-Inspired Clustering Algorithm for Energy Efficient Wireless Sensor Networks
In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. I...
متن کاملImmune System Based Intrusion Detection System
The threats and intrusions in IT systems can basically be compared to human diseases with the difference that the human body has an effective way to deal with them, what still need to be designed for IT systems. The human immune system (HIS) can detect and defend against yet unseen intruders, is distributed, adaptive and multilayered to name only a few of its features. Our immune system incorpo...
متن کاملDistributed Agent Based Model for Intrusion Detection System Based on Artificial Immune System
With mounting global network connectivity, the issue of intrusion has achieved importance, promoting active research on efficient Intrusion Detection Systems (IDS). Artificial Immune System (AIS) is a new bio-inspired model which is applied for solving various problems in the field of information security. Because of its unique features, (self-learning, self-adaptation and selfimprovement), AIS...
متن کامل