5,341 research outputs found
A robust domain partitioning intrusion detection method
The capacity for data mining algorithms to learn rules from data is influenced by, inter-alia, the random nature of training and test data as well as by the diversity of domain partitioning models. Isolating normal from malicious data traffic across networks is one regular task that is naturally affected by that randomness and diversity. We propose a robust algorithm Sample-Measure-Assess (SMA) that detects intrusion based on rules learnt from multiple samples. We adapt data obtained from a set of simulations, capturing data attributes identifiable by number of bytes, destination and source of packets, protocol and nature of data flows (normal and abnormal) as well IP addresses. A fixed sample of 82,332 observations on 27 variables was drawn from a superset of 2.54 million observations on 49 variables and multiple samples were then repeatedly extracted from the former and used to train and test multiple versions of classifiers, via the algorithm. With two class labels–binary and multi-class, the dataset presents a classic example of masked and spurious groupings, making an ideal case for concept learning. The algorithm learns a model for the underlying distributions of the samples and it provides mechanics for model assessment. The settings account for our method’s novelty–i.e., ability to learn concept rules from highly masked to highly spurious cases while observing model robustness. A comparative analysis of Random Forests and individually grown trees show that we can circumvent the former’s dependence on multicollinearity of the trees and their individual strength in the forest by proceeding from dimensional reduction to classification using individual trees. Given data of similar structure, the algorithm can order the models in terms of optimality which, means our work can contribute towards understanding the concept of normal and malicious flows across tools. The algorithm yields results that are less sensitive to violated distributional assumptions and, hence, it yields robust parameters and provides a generalisation that can be monitored and adapted to specific low levels of variability. We discuss its potential for deployment with other classifiers and potential for extension into other applications, simply by adapting the objectives to specific conditions
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems
We present a new distributed fuzzy partitioning method to reduce the
complexity of multi-way fuzzy decision trees in Big Data classification
problems. The proposed algorithm builds a fixed number of fuzzy sets for all
variables and adjusts their shape and position to the real distribution of
training data. A two-step process is applied : 1) transformation of the
original distribution into a standard uniform distribution by means of the
probability integral transform. Since the original distribution is generally
unknown, the cumulative distribution function is approximated by computing the
q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy
partition in the transformed attribute space using a fixed number of equally
distributed triangular membership functions. Despite the aforementioned
transformation, the definition of every fuzzy set in the original space can be
recovered by applying the inverse cumulative distribution function (also known
as quantile function). The experimental results reveal that the proposed
methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT)
induction algorithm to maintain classification accuracy with up to 6 million
fewer leaves.Comment: Appeared in 2018 IEEE International Congress on Big Data (BigData
Congress). arXiv admin note: text overlap with arXiv:1902.0935
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
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