Dipankar Dasgupta

Genetic and Evolutionary Computation - GECCO 2004

Charged particle swarm optimization (CPSO) is well suited to the dynamic search problem since inter-particle repulsion maintains population diversity and good tracking can be achieved with a simple algorithm. This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an adapted neutral swarm are compared for a number of different dynamic environments which include extreme ‘needle-inthe-haystack’ cases. The results suggest that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.

Artificial Immune Systems and Their Applications

This is a pioneering work on the emerging field of artificial immune systems-highly distributed systems based on the principles of the natural system. Like artificial neural networks, artificial immune systems can learn new information and recall previously learned information. This book provides an overview of artificial immune systems, explaining its applications in areas such as immunological memory, anomaly detection algorithms, and modeling the effects of prior infection on vaccine efficacy.

Advances in artificial immune systems

During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity

Evolutionary Algorithms in Engineering Applications

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Negative Selection Algorithm for Aircraft Fault Detection

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

An Overview of Artificial Immune Systems and Their Applications

The natural immune system is a subject of great research interest because of its powerful information processing capabilities. From an information processing perspective, the immune system is a highly parallel system. It proviEns an excellent Model of adaptive processes operating at the local level and of useful behavior emerging at the global level. Moreover, it uses learning, memory, and associative retrieval to solve recognition and classification tasks. This chapter illustrates different immunological mechanisms and their relation to information processing, and proviEns an overview of the rapidly emerging field called Artificial Immune Systems. These techniques have been successfully used in pattern recognition, fault Entection and diagnosis, computer security, and a variety of other applications.

Computational Intelligence in Cyber Security

This keynote speech will be devoted to the application of the state-of-the-art CI (computational intelligence)-based technologies - fuzzy systems, evolutionary computation, genetic programming, neural networks and artificial immune systems, and highlight how CI-based technologies play critical roles in various computer and information security problems

G-NAS: A grid-based approach for negative authentication

Surveys show that more than 80% authentication systems are password based and these systems are increasingly under direct and indirect attacks. In an effort to protect the Positive Authentication System (PAS), the negative authentication concept was introduced [9]. Here, the representation space of password profile is called self-region; any element outside this self-region is defined as the non-self-region. Then anti-password detectors (clusters) are generated covering most of the non-self-region while leaving some space uncovered to reduce detector generation time and obfuscation. In this work, we investigate a Grid-based NAS approach, called G-NAS, where anti-password detectors are generated deterministically. This approach allows faster detector generation compared to previous NAS approaches. We reported some experimental results of G-NAS using different real-world password datasets. Results demonstrate the efficiency of the proposed approach and exhibited significant improvements compared to NAS approaches. It appears to be more robust and scalable with respect to the size of password profiles and able to update of detector sets on-the-fly.

Estimating Security Coverage for Cloud Services

Secure cloud environment is essential for providing uninterrupted services to customers (individual user, company and government), since customers are relying on cloud for their computing and network service needs. As providers play the central role in cloud security, they need to establish rigorous security measures as a part of their service offerings. In order to limit liabilities for damages caused by the cloud, some form of insurance seems appropriate. For cloud security insurance, however, the question of differential security coverage is relevant as the cost of deploying special protection, detection and response tools varies and requires the coverage estimation. In this paper, we describe a framework to estimate security coverage for different type of service offerings. We have developed software prototype of this framework, called MEGHNAD and tested for various cloud service security requirements. This prototype can serve as a specialized Cloud Doctor in prescribing the right combination of security tools for different cloud services and according to the level of security assurance required.

A fuzzy decision support system for multifactor authentication

Multifactor authentication (MFA) is a growing trend for the accurate identification of the legitimate users through different modalities such as biometrics, nonbiometric, and cognitive behavior metric. In this paper, we have developed an adaptive MFA that considers the effects of different user devices, media, environments, and the frequency of authentication to detect the legitimate user. For this purpose, initially, we have evaluated the trustworthiness values of all the authentication modalities in different user devices and media using a nonlinear programming problem with probabilistic constraints. Finally, an evolutionary strategy, using fuzzy “IF–THEN” rule and genetic algorithm has been developed for the adaptive selection of authentication modalities. We have done a numerical simulation to prove the effectiveness and efficiency of the proposed method. Moreover, we have developed a prototype client–server-based application and have done a detailed user study to justify its better usability than the existing counterparts.