5,182 research outputs found
Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD
Poseidon: a 2-tier Anomaly-based Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon
is payload-based, and presents a two-tier architecture: the first stage
consists of a Self-Organizing Map, while the second one is a modified PAYL
system. Our benchmarks on the 1999 DARPA data set show a higher detection rate
and lower number of false positives than PAYL and PHAD
WiFi Miner: An online apriori and sensor based wireless network Intrusion Detection System
This thesis proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms.
The proposed system, WiFi Miner, solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm also proposed by this thesis. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, thereby improving efficiency and run times significantly. A positive anomaly score is assigned to each packet (record) for each infrequent pattern found while a negative anomaly score is assigned for each frequent pattern found. So, a record with final positive anomaly score is considered as anomaly based on the presence of more infrequent patterns than frequent patterns 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
A Correlation Framework for Continuous User Authentication Using Data Mining
Merged with duplicate records: 10026.1/572, 10026.1/334 and 10026.1/724 on 01.02.2017 by CS (TIS)The increasing security breaches revealed in recent surveys and security threats reported in the media reaffirms the lack of current security measures in IT systems. While most reported work in this area has focussed on enhancing the initial login stage in order to counteract against unauthorised access, there is still a problem detecting when an intruder has compromised the front line controls. This could pose a senous threat since any subsequent indicator of an intrusion in progress could be quite subtle and may remain hidden to the casual observer. Having passed the frontline controls and having the appropriate access privileges, the intruder may be in the position to do virtually anything without further challenge. This has caused interest'in the concept of continuous authentication, which inevitably involves the analysis of vast amounts of data. The primary objective of the research is to develop and evaluate a suitable correlation engine in order to automate the processes involved in authenticating and monitoring users in a networked system environment. The aim is to further develop the Anoinaly Detection module previously illustrated in a PhD thesis [I] as part of the conceptual architecture of an Intrusion Monitoring System (IMS) framework
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