4,998 research outputs found
Application of bagging, boosting and stacking to intrusion detection
This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and reduce the false positive rate. We use four different data mining algorithms, naïve bayes, J48 (decision tree), JRip (rule induction) and iBK( nearest neighbour), as base classifiers for those ensemble methods. Our experiment shows that the prototype which implements four base classifiers and three ensemble algorithms achieves an accuracy of more than 99% in detecting known intrusions, but failed to detect novel intrusions with the accuracy rates of around just 60%. The use of bagging, boosting and stacking is unable to significantly improve the accuracy. Stacking is the only method that was able to reduce the false positive rate by a significantly high amount (46.84%); unfortunately, this method has the longest execution time and so is insufficient to implement in the intrusion detection fiel
Next Challenges in Bringing Artificial Immune Systems to Production in Network Security
The human immune system protects the human body against various pathogens
like e.g. biological viruses and bacteria. Artificial immune systems reuse the
architecture, organization, and workflows of the human immune system for
various problems in computer science. In the network security, the artificial
immune system is used to secure a network and its nodes against intrusions like
viruses, worms, and trojans. However, these approaches are far away from
production where they are academic proof-of-concept implementations or use only
a small part to protect against a certain intrusion. This article discusses the
required steps to bring artificial immune systems into production in the
network security domain. It furthermore figures out the challenges and provides
the description and results of the prototype of an artificial immune system,
which is SANA called.Comment: 7 pages, 1 figur
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
Intrusion Detection Mechanism Using Fuzzy Rule Interpolation
Fuzzy Rule Interpolation (FRI) methods can serve deducible (interpolated)
conclusions even in case if some situations are not explicitly defined in a
fuzzy rule based knowledge representation. This property can be beneficial in
partial heuristically solved applications; there the efficiency of expert
knowledge representation is mixed with the precision of machine learning
methods. The goal of this paper is to introduce the benefits of FRI in the
Intrusion Detection Systems (IDS) application area, in the design and
implementation of the detection mechanism for Distributed Denial of Service
(DDOS) attacks. In the example of the paper as a test-bed environment an open
source DDOS dataset and the General Public License (GNU) FRI Toolbox was
applied. The performance of the FRI-IDS example application is compared to
other common classification algorithms used for detecting DDOS attacks on the
same open source test-bed environment. According to the results, the overall
detection rate of the FRI-IDS is in pair with other methods. On the example
dataset it outperforms the detection rate of the support vector machine
algorithm, whereas other algorithms (neural network, random forest and decision
tree) recorded lightly higher detection rate. Consequently, the FRI inference
system could be a suitable approach to be implemented as a detection mechanism
for IDS; it effectively decreases the false positive rate value. Moreover,
because of its fuzzy rule base knowledge representation nature, it can easily
adapt expert knowledge, and also be-suitable for predicting the level of degree
for threat possibility
Survey on Incremental Approaches for Network Anomaly Detection
As the communication industry has connected distant corners of the globe
using advances in network technology, intruders or attackers have also
increased attacks on networking infrastructure commensurately. System
administrators can attempt to prevent such attacks using intrusion detection
tools and systems. There are many commercially available signature-based
Intrusion Detection Systems (IDSs). However, most IDSs lack the capability to
detect novel or previously unknown attacks. A special type of IDSs, called
Anomaly Detection Systems, develop models based on normal system or network
behavior, with the goal of detecting both known and unknown attacks. Anomaly
detection systems face many problems including high rate of false alarm,
ability to work in online mode, and scalability. This paper presents a
selective survey of incremental approaches for detecting anomaly in normal
system or network traffic. The technological trends, open problems, and
challenges over anomaly detection using incremental approach are also
discussed.Comment: 14 pages, 1 figure, 11 tables referred journal publicatio
CONDOR: A Hybrid IDS to Offer Improved Intrusion Detection
Intrusion Detection Systems are an accepted and very
useful option to monitor, and detect malicious activities.
However, Intrusion Detection Systems have inherent limitations which lead to false positives and false negatives; we propose that combining signature and anomaly based IDSs should be examined. This paper contrasts signature and anomaly-based IDSs, and critiques some proposals about hybrid IDSs with signature and heuristic capabilities, before considering some of their contributions in order to include them as main features of a new hybrid IDS named CONDOR (COmbined Network intrusion Detection ORientate), which is designed to offer superior pattern analysis and anomaly detection by reducing false positive rates and administrator intervention
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm
Network intrusion detection systems (NIDSs) have a role of identifying
malicious activities by monitoring the behavior of networks. Due to the
currently high volume of networks trafic in addition to the increased number of
attacks and their dynamic properties, NIDSs have the challenge of improving
their classification performance. Bio-Inspired Optimization Algorithms (BIOs)
are used to automatically extract the the discrimination rules of normal or
abnormal behavior to improve the classification accuracy and the detection
ability of NIDS. A quantum vaccined immune clonal algorithm with the estimation
of distribution algorithm (QVICA-with EDA) is proposed in this paper to build a
new NIDS. The proposed algorithm is used as classification algorithm of the new
NIDS where it is trained and tested using the KDD data set. Also, the new NIDS
is compared with another detection system based on particle swarm optimization
(PSO). Results shows the ability of the proposed algorithm of achieving high
intrusions classification accuracy where the highest obtained accuracy is 94.8
%
Analyzing and Improving Performance of a Class of Anomaly-based Intrusion Detectors
Anomaly-based intrusion detection (AID) techniques are useful for detecting
novel intrusions into computing resources. One of the most successful AID
detectors proposed to date is stide, which is based on analysis of system call
sequences. In this paper, we present a detailed formal framework to analyze,
understand and improve the performance of stide and similar AID techniques.
Several important properties of stide-like detectors are established through
formal proofs, and validated by carefully conducted experiments using test
datasets. Finally, the framework is utilized to design two applications to
improve the cost and performance of stide-like detectors which are based on
sequence analysis. The first application reduces the cost of developing AID
detectors by identifying the critical sections in the training dataset, and the
second application identifies the intrusion context in the intrusive dataset,
that helps to fine-tune the detectors. Such fine-tuning in turn helps to
improve detection rate and reduce false alarm rate, thereby increasing the
effectiveness and efficiency of the intrusion detectors.Comment: Submit to journal for publicatio
Intrusions Detection System Based on Ubiquitous Network Nodes
Ubiquitous computing allows to make data and services within the reach of
users anytime and anywhere. This makes ubiquitous networks vulnerable to
attacks coming from either inside or outside the network. To ensure and enhance
networks security, several solutions have been implemented. These solutions are
inefficient and or incomplete. Solving these challenges in security with new
requirement of Ubicomp, could provide a potential future for such systems
towards better mobility and higher confidence level of end user services. We
investigate the possibility to detect network intrusions, based on security
nodes abilities. Specifically, we show how authentication can help build user
profiles in each network node. Authentication is based on permissions and
restrictions to access to information and services on ubiquitous network. As a
result, our idea realizes a protection of nodes and assures security of
network.Comment: 6 pages, 3 figures, The Fourth International Conference on Advanced
Communications and Computation. 201
Machine Learning Techniques for Intrusion Detection
An Intrusion Detection System (IDS) is a software that monitors a single or a
network of computers for malicious activities (attacks) that are aimed at
stealing or censoring information or corrupting network protocols. Most
techniques used in today's IDS are not able to deal with the dynamic and
complex nature of cyber attacks on computer networks. Hence, efficient adaptive
methods like various techniques of machine learning can result in higher
detection rates, lower false alarm rates and reasonable computation and
communication costs. In this paper, we study several such schemes and compare
their performance. We divide the schemes into methods based on classical
artificial intelligence (AI) and methods based on computational intelligence
(CI). We explain how various characteristics of CI techniques can be used to
build efficient IDS.Comment: 11 page
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