Franciszek Seredynski

Cellular automata computations and secret key cryptography

In this paper, cellular automata (CAs) are used to design a symmetric key cryptography system based on Vernam cipher. CAs are applied to generate a pseudo-random numbers sequence (PNS) which is used during the encryption process. The quality of PNSs highly depends on the set of applied CA rules. Rules of radius r = 1 and 2 for nonuniform one-dimensional CAs have been considered. A new set of rules has been discovered using an evolutionary technique called cellular programming. This set provides very high quality encryption, and the system is very resistant to attempts of breaking the cryptography key.

Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support

The paper presents cellular automata (CA)-based multiprocessor scheduling system, in which an extraction of knowledge about scheduling process occurs and this knowledge is used while solving new instances of the scheduling problem. There are three modes of the scheduler: learning, normal operating, and reusing. In the learning mode, a genetic algorithm is used to discover CA rules suitable for solving instances of a scheduling problem. In the normal operating mode, discovered rules are able to find automatically, without a calculation of a cost function, an optimal or suboptimal solution of the scheduling problem for any initial allocation of program tasks in a multiprocessor system. In the third mode, previously discovered rules are reused with support of an artificial immune system (AIS) to solve new instances of the problem. We present a number of experimental results showing the performance of the CA-based scheduler.

Sequential and parallel cellular automata-based scheduling algorithms

We present an approach to designing cellular automata-based multiprocessor scheduling algorithms in which extracting knowledge about the scheduling process occurs. We consider the simplest case when a multiprocessor system is limited to two-processors. To design cellular automata corresponding to a given program graph, we propose a generic definition of program graph neighborhood, transparent to the various kinds, sizes, and shapes of program graphs. The cellular automata-based scheduler works in two modes: learning mode and operation mode. Discovered rules are typically suitable for sequential cellular automata working as a scheduler, while the most interesting and promising feature of cellular automata are their massive parallelism. To overcome difficulties in evolving parallel cellular automata rules, we propose using coevolutionary genetic algorithm. Discovered this way, rules enable us to design effective parallel schedulers. We present a number of experimental results for both sequential and parallel scheduling algorithms discovered in the context of a cellular automata-based scheduling system.

Competitive Coevolutionary Multi-Agent Systems

A new paradigm for a parallel and distributed evolutionary computation is proposed in this paper. The main idea of the proposed approach is based on considering a given system as a multi-agent system with game-theoretic models of interaction between players. For this purpose a model of noncooperativeN-person games with limited interaction is considered. Each player in the game has a payoff function and a set of actions. While players compete to maximize their payoffs, we are interested in the global behavior of the team of players, measured by the average payoff received by the team. To evolve a global behavior in the system, we propose three distributed schemes with evaluation of only local fitness functions. The first scheme uses ?-learning automata and is compared with two coevolutionary schemes, which we call loosely coupled genetic algorithms and loosely coupled classifier systems, respectively. We present simulation results which indicate that the global behavior in the systems emerges and is achieved in particular by only a local cooperation between players acting without global information about the system. The models of multi-agent systems are applied to develop parallel and distributed algorithms of dynamic mapping and scheduling tasks in parallel computers.

Distributed scheduling using simple learning machines

A new approach to develop parallel and distributed algorithms of scheduling tasks in parallel computers is proposed. A game theoretical model with the use of genetic-algorithms based learning machines called classifier systems as players in a game, serves as a theoretical framework of the approach. Experimental study of such a system shows its self-organizing features and the ability of collective behaviour. Following this approach a parallel and distributed scheduler is described. A simple version of the proposed scheduler has been implemented. Results of the experimental study of the scheduler demonstrate its high performance.

Anomaly detection in TCP/IP networks using immune systems paradigm

The paper presents an architecture of an anomaly detection system based on the paradigm of artificial immune systems (AISs). Incoming network traffic data are considered by the system as signatures of potential attackers by mapping them into antigens of AISs either using some parameters of network traffic or headers of selected TCP/IP protocols. A number of methods of generation of antibodies (anomaly detectors) were implemented. The way of anomaly detection depends on the method of antibodies generation. The paper presents results of an experimental study performed with use of real data and shows how the performance of the anomaly detection system depends on traffic data coding and methods of generation of detectors.

Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study

Abstract A competitive coevolutionary approach using loosely coupled genetic algorithms is proposed for a distributed optimization of Rosenbrocks function. The computational scheme is a coevolutionary system of agents with only local interaction among them, without any central synchronization. We use a recently developed coordination language, called Manifold, to implement our distributed optimization algorithm. We show that this implementation outperforms a sequential optimization algorithm based on standard genetic algorithms.

Function Optimization with Coevolutionary Algorithms

The problem of parallel and distributed function optimization with coevolutionary algorithms is considered. Two coevolutionary algorithms are used for this purpose and compared with sequential genetic algorithm (GA). The first coevolutionary algorithm called a loosely coupled genetic algorithm (LCGA) represents a competitive coevolutionary approach to problem solving and is compared with another coevolutionary algoritm called cooperative coevolutionary genetic algorithm (CCGA). The algorithms are applied for parallel and distributed optimization of a number of test functions known in the area of evolutionary computation. We show that both coevolutionary algorithms outperform a sequential GA. While both LCGA and CCGA algorithms offer high quality solutions, they may compete to outperform each other in some specific test optimization problems.

Loosely Coupled Distributed Genetic Algorithms

Iterated, noncooperative N-person games with limited interaction are considered. Each player in the game has defined its local payoff function and a set of strategies. While each player acts to maximize its payoff, we are interested in a global behavior of the team of players measured by the average payoff received by the team. To study behavior of the system we propose a new parallel and distributed genetic algorithm based on evaluation of local fitness functions while the global criterion is optimized. We present results of simulation study which support our ideas.

Recurrent neural networks towards detection of SQL attacks

In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to recurrent neural network (RNN) trained by back-propagation through time (BPTT) algorithm. Teaching data are shifted by one token forward in time with relation to input. The purpose of the testing phase is to predict the next token in the sequence. All experiments were conducted on Jordan and Elman networks using data gathered from PHP Nuke portal. Experimental results show that the Jordan network outperforms the Elman network predicting correctly queries of the length up to ten.