2,369 research outputs found
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
Improving intrusion detection systems using data mining techniques
Recent surveys and studies have shown that cyber-attacks have caused a
lot of damage to organisations, governments, and individuals around the world.
Although developments are constantly occurring in the computer security field,
cyber-attacks still cause damage as they are developed and evolved by
hackers. This research looked at some industrial challenges in the intrusion
detection area. The research identified two main challenges; the first one is that
signature-based intrusion detection systems such as SNORT lack the capability of
detecting attacks with new signatures without human intervention. The other
challenge is related to multi-stage attack detection, it has been found that
signature-based is not efficient in this area. The novelty in this research is
presented through developing methodologies tackling the mentioned challenges.
The first challenge was handled by developing a multi-layer classification
methodology. The first layer is based on decision tree, while the second layer is a
hybrid module that uses two data mining techniques; neural network, and fuzzy
logic. The second layer will try to detect new attacks in case the first one fails to
detect. This system detects attacks with new signatures, and then updates the
SNORT signature holder automatically, without any human intervention. The
obtained results have shown that a high detection rate has been obtained with
attacks having new signatures. However, it has been found that the false positive
rate needs to be lowered. The second challenge was approached by evaluating IP
information using fuzzy logic. This approach looks at the identity of participants
in the traffic, rather than the sequence and contents of the traffic. The results have
shown that this approach can help in predicting attacks at very early stages in
some scenarios. However, it has been found that combining this approach with a
different approach that looks at the sequence and contents of the traffic, such as
event- correlation, will achieve a better performance than each approach
individually
Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections
Intrusion detection and prevention of web service attacks for software as a service:Fuzzy association rules vs fuzzy associative patterns
Cloud computing inherits all the systems, networks as well asWeb Services’ security vulnerabilities, in particular
for software as a service (SaaS), where business applications or services are provided over the Cloud as Web Service (WS). Hence, WS-based applications must be protected against loss of integrity, confidentiality and availability when they are deployed over to the Cloud environment. Many existing IDP systems address only attacks mostly occurring at PaaS and IaaS. In this paper, we present our fuzzy association rule-based (FAR) and fuzzy associative pattern-based (FAP) intrusion detection and prevention (IDP) systems in defending against WS attacks at the SaaS level. Our experimental results have validated the capabilities of these two IDP systems in terms of detection of known attacks and prediction of newvariant attacks
with accuracy close to 100%. For each transaction transacted over the Cloud platform, detection, prevention or prediction is carried out in less than five seconds. For load and volume testing on the SaaS where the system is under stress (at a work load of 5000 concurrent users submitting normal, suspicious and malicious transactions over a time interval of 300 seconds), the FAR IDP system provides close to 95% service availability to normal transactions. Future work involves determining more
quality attributes besides service availability, such as latency, throughput and accountability for a more trustworthy SaaS
Autonomous decision on intrusion detection with trained BDI agents
In the context of computer security, the first step to respond to an intrusive incident is the detection of such activity in the monitored system. In recent years, research in intrusion detection has evolved to become a multi-discipline task that involves areas such as data mining, decision analysis, agent-based systems or cost–benefit analysis among others. We propose a multiagent IDS that considers decision analysis techniques in order to configure itself optimally according to the conditions faced. This IDS also provides a quantitative measure of the value of the response decision it can autonomously take. Results regarding the well-known 1999 KDD dataset are shown.Publicad
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