A Machine-Synesthetic Approach To DDoS Network Attack Detection

In the authors' opinion, anomaly detection systems, or ADS, seem to be the most perspective direction in the subject of attack detection, because these systems can detect, among others, the unknown (zero-day) attacks. To detect anomalies, the authors propose to use machine synesthesia. In this case, machine synesthesia is understood as an interface that allows using image classification algorithms in the problem of detecting network anomalies, making it possible to use non-specialized image detection methods that have recently been widely and actively developed. The proposed approach is that the network traffic data is "projected" into the image. It can be seen from the experimental results that the proposed method for detecting anomalies shows high results in the detection of attacks. On a large sample, the value of the complex efficiency indicator reaches 97%.Comment: 12 pages, 2 figures, 5 tables. Accepted to the Intelligent Systems Conference (IntelliSys) 201

Controlled offspring generation in evolutionary programming

The paper presents an extension to the standard evolutionary programming (SEP) technique, which incorporates the concept of fitness based offspring generation. Each parent in the population tries to solve the problem of surviving into future generations by the controlled generation of offspring. Offspring generated by parents with good environmental support have a high chance of survival as compared to the offspring generated by other parents. It is argued that generating more offspring from a low fit parent yields a high chance for the survival of its progeny. As a consequence, a modification is required to the SEP to generate more offspring than the number of parents. Experimental results for the selected problems are compared with the results obtained by the SEP techniqu

Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library

Connectionist approach for global optimization

A connectionist approach for global optimization is proposed. The standard function set is tested. Results obtained, in the case of large scale problems, indicate excellent scalability of the proposed approac

A deductive clustering approach

Clustering is concerned with grouping a collection of input objects. Conventional clustering algorithms cluster unlabelled objects. We argue that there are useful applications that involve clustering of labelled objects. We propose an approach for clustering of labelled objects. The proposed approach makes use of the domain knowledge represented in the form of a directed acyclic graph for clustering. We also propose a set of proper axioms in logic as a basis for the proposed algorithm. We study some of the properties of the approach such as order-independence and describe in detail an application of the proposed algorithm in the context of document retrieval (62 refs.

A comparison between conceptual clustering and conventional clustering

Clustering is a process of partitioning a given set of patterns into meaningful groups. The clustering process can be viewed as consisting of the following three phases: (i) feature selection phase, (ii) classification phase, and (iii) description generation phase. Conventional clustering algorithms implicitly use knowledge about the clustering environment to a large extent in the feature selection phase. This reduces the need for the environmental knowledge in the remaining two phases, permitting the usage of simple numerical measure of similarity in the classification phase. Conceptual clustering algorithms proposed by Michalski and Stepp [IEEE Trans. PAMI, PAMI-5, 396–410 (1983)] and Stepp and Michalski [Artif. Intell., pp. 43–69 (1986)] make use of the knowledge about the clustering environment in the form of a set of predefined concepts to compute the conceptual cohesiveness during the classification phase. Michalski and Stepp [IEEE Trans. PAMI, PAMI-5, 396–410 (1983)] have argued that the results obtained with the conceptual clustering algorithms are superior to conventional methods of numerical classification. However, this claim was not supported by the experimental results obtained by Dale [IEEE Trans. PAMI, PAMI-7, 241–244 (1985)]. In this paper a theoretical framework, based on an intuitively appealing set of axioms, is developed to characterize the equivalence between the conceptual clustering and conventional clustering. In other words, it is shown that any classification obtained using conceptual clustering can also be obtained using conventional clustering and vice versa

Simulated annealing for selecting optimal initial seeds in the K-means algorithm

Explores the applicability of simulated annealing, a probabilistic search method, for finding optimal partition of the data. A new formulation of the clustering problem is investigated. In order to obtain an optimal partition, a search is undertaken to locate optimal initial seeds, such that the K-means algorithm converges to optimal partition. Search space involved in this process is continuous, so discretization is done and simulated annealing is employed for locating optimal initial seeds. Experimental results substantiate the proposed method. Results obtained with the selected data sets are presented