Use Of Artificial Immune Systems Research Articles

Analysis of the Use of Artificial Immune Systems

Artificial immune systems are systems of artificial intelligence at the basis of work, which are based on the principles of the functioning of the biological immune system. These systems are of great interest from researchers developing models and algorithms in the field of machine learning for solving complex computational and engineering problems. In the presented work, a review of modern methods for the implementation of artificial immune systems is carried out, taking into account the results obtained recently.

A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can largely damage the availability of the cloud services and can be effectively initiated by utilizing different tools, prompting financial harm or influencing the reputation. Consequently, there is a requirement for a more grounded and general approach to block these attacks. This paper proposes the use of artificial immune systems to alleviate DDoS attacks in cloud computing by identifying the most potential features of the attack. This methodology is capable of detecting threats and responding according to the behavior of the biological resistance mechanism in human beings. It is carried out by emulating the various immune reactions and the construction of the intrusion detection system. For the assessment, experiments with public domain datasets (KDD cup 99) were implemented. Based on broad theoretical and performance analysis, the proposed system is capable to identify the anomalous entries with high detection accuracy and low false alarm rate.

Experimenting Dynamic Clonal Selection (DCS) for Parallel Multiple Interest Topics of User Profile Adaptation in Content Based Filtering

User profile for information filtering is an inspiring issue through distinctive profiling features plus profiling desires. The profile of the user should be accomplished of constant learning and be unable to remember past interest. Forgetting on the past interest is necessary for maintaining present demonstration of the user’s profiling. Profile that does not forget the past interest will eventually become drenched with not relevant features of user’s interest. The dynamic nature of the user profiling offers the use of Artificial Immune Systems (AIS). The clonal selection theory in natural immune system has gain the insight to researchers to create an algorithm that evolve the repertoire by means of selection, cloning and mutation. This paper discusses the experimentation of dynamic clonal selection (DCS) algorithm in adapting user profile for content-based filtering. The experiment focused on the scenario of multiple interest of topics in user profiling for content-based filtering. The result shows that the algorithm of DCS is applicable with classification over multiple topics of interest in which changing interest of topics are tracked in data over time with a form of maintaining and enhancing range of the repertoire.

Adaptive artificial immune networks for mitigating DoS flooding attacks

Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD’99, CAIDA’07 and CAIDA’08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

Constrained design optimization of active magnetic bearings through an artificial immune system

Purpose The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs). Design/methodology/approach This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue. Findings A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG. Originality/value In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

DETECTION OF INFORMATION SECURITY INCIDENTS BY THE MODIFIED ALGORITHM OF ARTIFICIAL IMMUNE SYSTEM WITH CLONAL SELECTION

In the article proposed the use of artificial immune systems as a heuristic detection method of information security incidents for algorithmic maintenance of intrusion detection systems. Describes the most known computational models of immune systems. As the basis of artificial immune system clonal selection theory is chosen. The algorithm of clonal selection is modified by use of the pseudorandom numbers generator on the basis of Blum- Blum- Shub’s algorithm. For affinity calculation in work the metrics coordination percent is used. Empirical results of an assessment of efficiency of the modified algorithm are received at approbation on a set of test data according to the procedure of research and their analysis. Conclusions about efficiency of application of the artificial immune system modified algorithm with clonal selection are formulated at the solution of a problem of deliberate changes detection on a controlled data set.

A New Approach to Artificial Immune System for Intrusion Detection of the Mobile Ad Hoc Networks

The use of artificial immune systems (AIS) in intrusion detection is an attractive concept for several reasons. Then it is judicious to expect that approaches of biological inspirations in this area, and specifically the abstraction of immune defense mechanism with its high detection capabilities and its strong defense against intrusion, will probably be able to meet this challenge. Researchers have implemented different immune models to design intrusion detection systems (IDS) in order to secure Mobile Ad Hoc Networks (MANET), but the most popular one is the self and non-self model. This model was used in the vast majority of biological inspiration in the field of MANET security. It has demonstrated attractive success, as well as it showed some weakness especially in terms of scalability and coverage. This paper try to incorporate some additional concepts proposed by the new danger theory in order to overcome some of the problems related to the adoption of the self and non-selfmodel. The proposed algorithm integrates and combines the basic concepts of intrusion detection system based on the role of T cells described by the negative selection algorithm, with those inspired by the role of dendritic cells to process the alarm signals and to judge thereafter whether there is presence of a dangerous element or not.

Classification of Prostate Cancer with the Use of Artificial Immune System and ANN

Before analyzing cells in Laboratory in prostate cancer detection, a classification system can give valuable information about the cancer. The purpose of this paper is to assess the value of Artificial Immune System (AIS) and Artificial Neural Networks (ANN) for classification of prostate cancer cases. Paraffine-embedded prostate cancer tissue specimens of 50 prostate cancer subjects were used in this study. Age range was 35-72 years and all subjects were males. 10 subjects had family history of cancer and 40 patients were non family. An Artificial Immune System (AIS) which is based on clonal selection theory was used to classify these 50 subjects as healthy and patient. With the correct arrangement in system parameters, AIS has reached a classification accuracy of 93.33%. This ratio in 50 data means that in test phase, only one data was misclassified as healthy whereas indeed that data was belonging to a patient. The classification procedure was also done with another method which is a well-known effective classification method for biomedical data: Artificial Neural Networks. The result for this application was 100% with ANN method. While it seems that there is a big difference in the performances of AIS and ANN in the classification accuracy, this difference was only because of 1 data. Thus, it can be said that, AIS is also a good performing classification algorithm as well as ANN for this application.

Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications

This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work.

AISIID: An artificial immune system for interesting information discovery on the web

There exist numerous systems for mining the web in search of relevant information but few exist for the discovery of interesting information. The discovery of interesting information is an advance on basic text mining in that it aims to identify text that is novel, unexpected or surprising to a user, whilst still being relevant. This article investigates the use of artificial immune systems (AIS) applied to discovery of interesting information. AIS are thought to confer the adaptability and learning required for this task. Artificial immune system for interesting information discovery (AISIID) is described in some detail, then an evaluative study is undertaken involving the subjective evaluation of the results by users. AISIID is found to discover pages rated more interesting by users than a comparative system.

Immune system approaches to intrusion detection – a review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. First, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Second, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

Immune System Approaches to Intrusion Detection - A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.