9,740 research outputs found
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
MACOC: a medoid-based ACO clustering algorithm
The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
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