An adaptive, distributed learning system based on the immune system

2002 ◽  
Author(s):  
J.E. Hunt ◽  
D.E. Cooke
Keyword(s):  
Immune System ◽  
Distributed Learning ◽  
Learning System

scholarly journals Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jian Jiang ◽  
Fen Zhang
Keyword(s):  
Machine Learning ◽  
Immune System ◽  
Intelligent Systems ◽  
Global Economy ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Realistic Representation ◽  
Executable File

As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metamorphism mechanisms used by malware developers and how to effectively address them. This work presents an innovative Artificial Evolutionary Fuzzy LSTM Immune System which, by using a heuristic machine learning method that combines evolutionary intelligence, Long-Short-Term Memory (LSTM), and fuzzy knowledge, proves to be able to adequately protect modern information system from Portable Executable Malware. The main innovation in the technical implementation of the proposed approach is the fact that the machine learning system can only be trained from raw bytes of an executable file to determine if the file is malicious. The performance of the proposed system was tested on a sophisticated dataset of high complexity, which emerged after extensive research on PE malware that offered us a realistic representation of their operating states. The high accuracy of the developed model significantly supports the validity of the proposed method. The final evaluation was carried out with in-depth comparisons to corresponding machine learning algorithms and it has revealed the superiority of the proposed immune system.

An evaluation of a distributed learning system

2001 ◽  
Vol 43 (2) ◽  
pp. 105-116 ◽  
Author(s):  
Peter M. Lawther ◽  
Derek H.T. Walker
Keyword(s):  
Distributed Learning ◽  
Learning System

scholarly journals Understanding Adaptive Immune System as Reinforcement Learning

2020 ◽  
Author(s):  
Takuya Kato ◽  
Tetsuya J. Kobayashi
Keyword(s):  
Reinforcement Learning ◽  
Immune System ◽  
Clonal Selection ◽  
Learning Rule ◽  
Adaptive Immune System ◽  
Learning System ◽  
Immune Network ◽  
Cellular Interactions ◽  
Th Cells ◽  
Adaptive Immune

The adaptive immune system of vertebrates can detect, respond to, and memorize diverse pathogens from past experience. While the selection of T helper (Th) clones is the simple and established mechanism to recognize and memorize new pathogens, the question that still remains unexplored is how the Th cells can acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals. In this work, we address this problem by associating the adaptive immune network organized by the Th cells with reinforcement learning (RL). By employing recent advancements of network-based RL, we show that the Th immune network can acquire the association between antigen patterns of and the effective responses to pathogens. Moreover, the clonal selection as well as other inter-cellular interactions are derived as a learning rule of this network. We also demonstrate that the stationary clone-size distribution after learning shares characteristic features with those observed experimentally. Our theoretical framework may contribute to revising and renewing our understanding of adaptive immunity as a learning system.

scholarly journals Adaptive discrimination of antigen risk by predictive coding in immune system

2021 ◽  
Author(s):  
Kana Yoshido ◽  
Naoki Honda
Keyword(s):  
Machine Learning ◽  
Immune Response ◽  
T Cells ◽  
Immune System ◽  
Prediction Error ◽  
Memory T Cells ◽  
Predictive Coding ◽  
Underlying Mechanism ◽  
Learning System ◽  
Past Experiences

The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as predictive coding. Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; helper and regulatory T cells predict the antigen amount and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens.

scholarly journals A Flexible Vulnerability Reduction System Using Machine Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 3784-3791
Author(s):  
Dr. Shaji. N. Raj
Keyword(s):  
Machine Learning ◽  
Immune System ◽  
Computer Security ◽  
Cyber Security ◽  
Web Application ◽  
Research Problem ◽  
Cyber Attacks ◽  
Learning System ◽  
Human Immune System ◽  
Human Immune

The human system have an ability to adapt dynamically and protect against biological viruses is amazing. Computer security faces an ever-increasing threat and a system which can prevent any viruses coming in, is an open research problem. We propose a new model, called (RI Secure Web), whic h can be resilient and immune to web application vulnerability for injection and URL manipulation for injection methods using an agent based machine learning system The ability of human immune system to survive and maintain body from different damages and its self curing capabi lity inspires the development of a resilient and adaptive cyber security system. Such system functions proactive and defends itself against viruses as human immune system does. In this paper, an architectural view of a system for reducing application level vulnerabilities to protect cyber attacks, particularly injection method is proposed.

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