Intelligent Mobile Agent for Intrusion Detection System (IMAIDS) R´ eginald Lips lips [email protected]

Nabil El-Kadhi el-kad [email protected]

LERIA European Institute of Technology 24, rue Pasteur - 94270 Le Kremlin Bicˆetre, FRANCE

Abstract IMAIDS is carried out by five 4th year stu-

Keywords: security, IDS, intelligent mobile agent.

they use two methods for detection. The first based on signatures [4], makes it possible to react with known attacks in a reliable way but is inefficient with the unindexed attacks. The second [5], is based on host behaviour analysis in the network. This method is more aggressive against the unknown attacks but less efficient because of false positives [6]. These IDS are based on a monolithic architecture [7] which induces centralized data processing and substancially reduces performance and fault-tolerance, considerable disadvantages. Our project wants to fill these gaps by integrating a mobile agent platform and an emerging intrusion detection. The mobile agent platform will provide for an automatic deployment of the probes on the network, in order to organize the collection of information in a dynamic and effective way. This distributed architecture will give a new dimension to intrusion detection by decentralizing treatment and thus, make it more resistant [7].



dents, R´eginald Lips, Nicolas Carlier, Damien Molot, Jean-Marie Peschoux and Alexandre Luciani, from Epitech (European Institute of Technology; http: // www. epitech. net/ ), within the framework of their end of studies project. The purpose of this document is to present our project, resulting from work completed by the LERIA laboratory (Laboratory at Epitech for Research of Industrial Applications; http: // leria. epitech. net/ ). It has been co-written by R´eginald Lips, project leader of IMAIDS, Nabil El-Kadhi, director of the LERIA, and Davide Del Vecchio, senior security consultant for Telecom Italia. Initially, we will point out the general context of this project, with its objective and the topics which will be aborded. Then, we will describe more in detail the implementation schedule, and the most important tool features with the theoretical bases.


General context Objective of the project

IMAIDS is a research laboratory project applied to industry. The main goal of this project is to develop a finished intelligent tool prototype for intrusion detection. Intrusion Detection Systems (IDS [1]) were introduced by James Anderson [2] in a publication named Computer Security Threat Monitoring and Surveillance in 1980. Since research in this field developed, we have found many finished products (SNORT [1], Prelude [3]). However, IDS currently on the market have some gaps. Initially,

Basic principles

IMAIDS approaches two broad topics of computer science, networks [8] and artificial intelligence [9], to lead to an increasingly required objective, security. The systems for computer security must adapt to the growing needs of company networks. To meet these needs, IMAIDS is based on a mobile agent platform [14] for intrusion detection. This specific platform is composed of a set of agents able to move on the network, communicate and find reason for an attack recognition logic. As we mentioned previously, IDS are based on rules, known as statics (signatures), to detect an intrusion [10]. Our project

will develop this technique by introducing the concept of partial signatures [11]. These new dynamic rules will bring a similarity recognition to the system and will make it possible to discover polymorphic attacks or attacks based on the same vulnerabilities. IMAIDS will use a multi-agent platform for the deployment in order to acquire all the advantages of a distributed architecture. It must adapt to heterogeneous networks and be compatible with several operating systems. Moreover, this platform must respect the standardization of the MAS (Multi-Agent Systems) described by the FIPA (Foundation for Intelligent Physical Agents). Therefore, JAVA language will be chosen for its compatibility with many systems and especially for the use of already existing multi-agent platforms which respect these constraints. Finally, IMAIDS will constitute an intelligent system, based on the two preceding concepts. It will integrate four important concepts. The first is a conversion engine able to convert static rules (signatures) into dynamic rules (partial signatures) for an intelligent detection. The second is a decisional engine to raise alarms, based on the information collected by each agent. The third is an organisational engine able to deploy and manage the agents on the network. Fourth, collector agents collect information (network flow, system events).


Mobile agent platform: technical description

existing information system. IMAIDS is able to deploy the agents on sensitive hosts/networks, in a precise way. The detection of the attacks, in IMAIDS, is supported by a new system of signature called partial signatures in order to detect known and unknown attacks [12]. The detection of the attacks is reinforced by behavior analysis. IMAIDS will integrate a system of high level safety, cryptography, and rights management for the agents in order to guarantee system integrity.


Technical architecture

IMAIDS is presented as a multi-agent architecture. The treatment itself cannot be summarized by the execution of a single program on a signle host, but the execution of all mobile agents, which are able to move on a wide network. An agent can move to another host, be duplicated, communicate with other agents, or sleep. When the stability of a traditional program depends on a single host, this kind of platform can completely decentralize treatment on several hosts. The JAVA platform is used for the project completion. Indeed, existing frameworks such as JADE [14] or Agglets [13] are available. Our choice is currently the Jade platform for its secure communication between agents and the avalaible features. Agent organization in the IMAIDS architecture is summarized in Figure 1.

IMAIDS is based on a multi-agent architecture which allows a great integration flexibility in an


Agent description

IMAIDS is based on four types of mobile agents. First, collector agents patrol the network and collect information such as network flow or system events. For instance, IMAIDS is a network based IDS, so, probes catch only network flow. Each agent will be able to support the integration of several filters (protocol, IP, port and so on), in order to recover useful information and distribute the collection. Second, the correlator agent creates contexts of connections. The contexts of connections represent the relations between various information coming from multiple distinct collector agents. This correlator agent uses a set of rules to classify crucial events (also called critical events)

and will hurry this specific information to the appropriate analyzer agent. A very important aspect here is that attack detection has to remain active even if there is a possible lack of information (this point will be exposed below). The idea is to deduce that an attack occurs when the received events check a set of crucial properties. Third, the analyzer agent represents the detection engine. The partial rules of behavioral analysis, they will determine if the analyzed field is victim of an attack or a suspicious behavior. An analyzer agent can be specialized for specific recognition. Fourth, the agent manager is the organisational engine. It is in charge of the agent integration process which can be summarized by authentification, cloning, killing and deployment. It can be also used for routing

Figure 1: Agent organization agent communication (Where is Agent1? - On host Of course, this kind of agent should not be a centralized point in the system. That’s why an election concept to replace or add a new manager when the first is not functionning correctly is being developed. The election is a distributed algorithm to define a master manager in the agent platform.


Security management

The Jade platform [14] is a reliable system which proposes many options for secure communication between agents. For example, SSL protocol [15] can be used to encrypt messages. A secure agent authentification is also available. Each agent holds a x.509 certificate [16] which identifies it in a singular way. The agent which receives a message, can check the conformity and the sender. Checksums will be used (SHA1 [17]) to complete the authentification and data coherence process. It is also important to take into account the quantity of information generated and exchanged by the IDS. That is why it will be necessary to choose between completely encoded exchanges, and encrypted authentications followed by a non-encrypted exchange of information. Indeed, the content of some inter-agent communications will have no interest for a unscrupulous user. Nevertheless in the case of communication between agents on networks which is not confidential, it would be interesting to have a complete

encoding of the exchanges via private/public keys (certificate x.509). The information volume circulating on the network would not be too large, and IDS integrity would not be compromised. We can also note that messages have variable priorities. For example, an agent X, before sending a high priority message to an agent Y, has to know the safety level of this agent. If agent Y does not have the same safety level, agent X cannot send its message. Consequently, agent X must require that agent Y increases its safety level to begin the data transmission. High priority transfers will be completed before low priority ones to guarantee the reaction time of IMAIDS. Finally, our architecture is decentralized. Each machine will host a part of the IDS. This is an interesting point of the project. In fact, centralized data processing is a very sensitive point. That is why a completely distributed platform is used. Sensitive processes are duplicated to ensure system integrity and reliability.



In IMAIDS architecture, agents can communicate in two ways: • First, called ”distant communication”. In this communication mode, agent X needs information from agent Y deployed on another host. It must open one channel to communicate with it. Once the communcitation chan-

nel is created (priority, encoding) they communicate by messages. • Second, called ”local communication”. In this communication mode, agent X which must receive information, will be deployed on the host where agent Y is. Thus, the exchanges are local and don’t create large network load. As soon as agent X has received the expected information, it can come back on the last host, remain on the same host (as Y) or move to a third host. IMAIDS uses several kinds of agents, some need high communication, with rich information, and others just need to share a little information. The communication cannot be based on an unique type of language, shared by all agents. That’s why different kinds of communication must be organized into a hierarchy. IMAIDS’s agents integrate, at the same time, a language and a knowledge base, also called an ontology [18]. Each communication path rely on an ontology. One ontology is dedicated to communication within each agent’s group (collecting, correlation, analyzing, managing). Another ontology is required for communication between collectors and correlators. A third is used between correlator and analyser. Finally, a global one is required for basic communication between all agents.

3 3.1

Attack recognition Partial signature concept

The static signatures represent a reliable means to detect an attack, however they are specific to one attack. The problem occurs when the hacker modifies his attack. The static signatures thus become ineffective. To compensate this liability, a new signature called a partial signature is developed. The principle is to split the existing static signatures into distinct significant elements. Logical rules will be added to these elements, which will make it possible to take into account the polymorphism of the known attacks. A static signature contains all the various elements which characterize an attack such as value of the header in network packets (IP, TCP, UDP and so on), character string in the content of a packet or any information which can be crucial to detect the attack. Here’s a simple example of breaking down which corresponds to the concept of the partial signatures:

• An element A, represents a value of IP header in the packet used for the attack. • An element B, represents the protocol used. • An element C, represents the specific character string of the attack. The corresponding partial rule could be represented in the form (A.B) + C. IMAIDS will interpret the partial signature as follows: • If it detects the elements A and B but not C, it considers that the attack occured. • If it detects the elements A and B and C, it considers that the attack occured. • If it detects the elements B then A but not C, it considers that the attack occured. • If it detects the elements B then A then C, it considers that the attack occured. • If it detects the elements A and C but not B, it considers that the attack did not occur.


Analysis process

The first step is to collect information as network flow or system events, thanks to collector agents. Collector agents are also able to preprocess observations. This information flow is filtered (by protocol, IP and so on) and then, prepared for the next step. The preprocessing is defined by a Data Type Document (DTD) establishing the list of the field composing a packet sniffing or syslog parsing. Here is an example of preprocessing for a HTTP request : 80 GET /index.html HTTP/1.1 Host: The second step is the correlation analysis. Our correlation engine (correlator agents) integrates a

kind of expert system which will generate correlated events and states to create a context. This is a cyclic process that takes into account two kinds of information. First, the recently observed events, resulting from the previous step (preprocessing) and then, the deduced or correlated events generated by the previous cycle. The idea here is to be able to anticipate some events and to propose crucial events. To do so, this analysis is based on correlation rules that take into account little information such as event time out, relationship between events, prior-

ity of event and synchronization properties among others. Figure 2. Analyser agents are the final step of our solution. They receive alerts from correlator agents, analyse this informations and decide to alert the administrator. They could be specialized by analysis such as classical signature detection or anomaly detection. Finally, if an analyser agent decides to raise an alert, it could assign a set of counter-measures. The idea is to propose a set of guidelines and solutions to the system administrators.

Figure 2: Correlation Analysis

Conclusion IMAIDS integrates two emerging technologies, a multi-agent platform, and attack recognition based on partial signatures to make an effective and intelligent intrusion detection system. IMAIDS will keep watch over a set of computers, reducing net-

work resources and without interfering with other hosts. Finally, a conversion engine connected to wellknown security servers, could bring the system an autonomous learning process to automatically add new dynamic rules in the platform.

Our project is currently in development. A first version for tests will be released on July 2007. A finished prototype is planned for January 2008. Additionnal information is available on: • imaids/ • php?action view=submenu&option=tree&id tree=257

Acknowledgement Special thanks to Emery Pierson, Epitech english department director, for his language contribution.

References [1] Kerry J. Cox, Christopher Gerg. Managing Security with Snort & IDS Tools. O’Reilly, 2004. [2] James P. Anderson. Computer Security Threat Monitoring and Surveillance. Final report of study, 1980. [3] The Prelude Hybrid IDS project. http://www. [4]

What You Need to Know About Intrusion Detection Systems. http: // What You Need to Know About Intrusion Detection Systems.html.

[5] The Evolution of Intrusion Detection Systems. 1514. [6] William Yurcik. Controlling Intrusion Detection Systems by Generating False Positives. sift/papers/-lcn02.pdf. [7] Mark Crosbie, Gene Spafford. Defending a Computer System using Autonomous Agents. http://ftp.cerias. mcrosbie-spaf-NISC.pdf. Purdue University, March 11, 1994. [8] Andrew S. Tanenbaum Computer Networks. Prentice Hall, August 9, 2002. [9] Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, January 15, 1995. [10] Stephen Northcutt, Judy Novak. Network Intrusion Detection: An Analyst’s Handbook. New Riders Publishing, September 22, 2000. [11] Jerry M. Mendel. Uncertain Rule-Based Fuzzy Logic Systems. Prentice Hall, December 2000. [12] Nabil El-Kadhi, Romain Dequit, Fayal Daira. Using Correlation Detection for IMA-IDS Architecture. main.php?action view=submenu&option= tree&id tree=89, July 2004. [13] Danny Lange, Oshima Mitsuru, Mitsuru Oshima. Programming and Deploying Java Mobile Agents With Aglets. Addison-Wesley Professional, 1998. [14] Fabio Luigi Bellifemine, Giovanni Caire, Dominic Greenwood. Developing Multi-agent Systems With Jade. John Wiley & Sons, 2007. [15] Eric Rescorla SSL and TLS: Designing and Building Secure Systems. Addison-Wesley Professional, October 13, 2000. [16] R. Housley, W. Ford, W. Polk, D. Solo Internet X.509 Public Key Infrastructure Certificate and CRL Profile. RFC 2459, January 1999. [17] D. Eastlake, P. Jones US Secure Hash Algorithm 1 (SHA1). RFC 3174, September 2001. [18] Raj Sharman, Rajiv Kishore, Ram Ramesh. Ontologies. Springer, 2007.

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