Improving network intrusion detection by means of domain-aware genetic programming

Proceeding of: International Conference on Availability, Reliability, and Security, 2010. ARES '10, 15-18 February 2010, Krakow, PolandOne of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown in previous research, we guide the search by means of a fitness function based on recent advances on IDS evaluation. For the experimental work we use a well-known dataset (i.e. KDD- 99) that has become a standard to compare research although its drawbacks. Results clearly show that an intelligent use of GP achieves systems that are comparable (and even better in realistic conditions) to top state-of-the-art proposals in terms of effectiveness, improving them in efficiency and simplicity.This work was partially supported by CDTI, Ministerio de Industria, Turismo y Comercio of Spain in collaboration with Telefónica I+D, Project SEGUR@ CENIT-2007 2004Publicad

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Evaluation of Classification Algorithms for Intrusion Detection in MANETs

Mobile Ad-hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs.This work has been partially supported by the Marie Curie IEF, project "PPIDR: Privacy-Preserving Intrusion Detection and Response in Wireless Communications", grant number 252323, and also by the Comunidad de Madrid and Carlos III University of Madrid, Project EVADIR CCG10-UC3M /TIC-5570.Publicad

Dynamic deployment of context-aware access control policies for constrained security devices

Securing the access to a server, guaranteeing a certain level of protection over an encrypted communication channel, executing particular counter measures when attacks are detected are examples of security requirements. Such requirements are identi ed based on organizational purposes and expectations in terms of resource access and availability and also on system vulnerabilities and threats. All these requirements belong to the so-called security policy. Deploying the policy means enforcing, i.e., con guring, those security components and mechanisms so that the system behavior be nally the one speci ed by the policy. The deployment issue becomes more di cult as the growing organizational requirements and expectations generally leave behind the integration of new security functionalities in the information system: the information system will not always embed the necessary security functionalities for the proper deployment of contextual security requirements. To overcome this issue, our solution is based on a central entity approach which takes in charge unmanaged contextual requirements and dynamically redeploys the policy when context changes are detected by this central entity. We also present an improvement over the OrBAC (Organization-Based Access Control) model. Up to now, a controller based on a contextual OrBAC policy is passive, in the sense that it assumes policy evaluation triggered by access requests. Therefore, it does not allow reasoning about policy state evolution when actions occur. The modi cations introduced by our work overcome this limitation and provide a proactive version of the model by integrating concepts from action speci cation languages

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes

A functional framework to evade network IDS

Proceeding of: 44th Hawaii International Conference on System Science, Kauai, HI, January 4-7, 2011Signature based Network Intrusion Detection Systems (NIDS) apply a set of rules to identify hostile traffic in network segments. Currently they are so effective detecting known attacks that hackers seek new techniques to go unnoticed. Some of these techniques consist of exploiting network protocols ambiguities. Nowadays NIDS are prepared against most of these evasive techniques, as they are recognized and sorted out. The emergence of new evasive forms may cause NIDS to fail. In this paper we present an innovative functional framework to evade NIDS. Primary, NIDS are modeled accurately by means of Genetic Programming (GP). Then, we show that looking for evasions on models is simpler than directly trying to understand the behavior of NIDS. We present a proof of concept showing how to evade a self-built NIDS regarding two publicly available datasets. Our framework can be used to audit NIDS.This work was partially supported by CDTI, Ministerio de Industria, Turismo y Comercio of Spain in collaboration with Telefonica I+D, Project SEGUR@ CENIT-2007 2004.Publicad