Antifragility = Elasticity + Resilience + Machine Learning Models and Algorithms for Open System Fidelity

Florio, Vincenzo De

doi:10.1016/j.procs.2014.05.499

Cited by 28 publications

(22 citation statements)

References 30 publications

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“…In the case of these pseudo-antifragile systems, it is difficult to argue that they really benefit from failure. One possible exception is illustrated by an Antifragile 2014 paper [19], which claims their system is antifragile because the more their system is subjected to threats and challenging conditions, the more insight it will acquired on how to respond to new and more threatening situations (either for itself or at the genotypic level). It could be argued that if the system never experienced minor threats (which may be quick to overcome), it would not do as well at combating severe threats that were not anticipated at the time of design.…”

Section: The Meaning Of Antifragilitymentioning

confidence: 99%

Antifragile Communications

Lichtman

Vondal

Clancy

et al. 2018

IEEE Systems Journal

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Jamming is an ongoing threat that plagues wireless communications in contested areas. Unfortunately, jamming complexity and sophistication will continue to increase over time. The traditional approach to addressing the jamming threat is to harden radios, such that they sacrifice communications performance for more advanced jamming protection. To provide an escape from this trend, we investigate the previously unexplored area of jammer exploitation.This dissertation develops the concept of antifragile communications, defined as the capability for a communications system to improve in performance due to a system stressor or harsh condition. Antifragility refers to systems that increase in capability, resilience, or robustness as a result of disorder (e.g., chaos, uncertainty, stress). An antifragile system is fundamentally different from one that is resilient (i.e., able to recover from failure) and robust (i.e., able to resist failure). We apply the concept of antifragility to wireless communications through several novel strategies that all involve exploiting a communications jammer. These strategies can provide an increase in throughput, efficiency, connectivity, or covertness, as a result of the jamming attack itself. Through analysis and simulation, we show that an antifragile gain is possible under a wide array of electronic warfare scenarios. Throughout this dissertation we provide guidelines for realizing these antifragile waveforms. Other major contributions of this dissertation include the development of a communications jamming taxonomy, feasibility study of reactive jamming in a SATCOM-type scenario, and a reinforcement learning-based reactive jamming mitigation strategy, for times when an antifragile approach is not practical.Most of the jammer exploitation strategies described in this dissertation fall under the category of jammer piggybacking, meaning the communications system turns the jammer into an unwitting relay. We study this jammer piggybacking approach under a variety of reactive jamming behaviors, with emphasis on the sense-and-transmit type. One piggybacking approach involves transmitting using a specialized frequency-shift keying (FSK) waveform, tailored to exploit a jammer that channelizes a block of spectrum and selectively jams active subchannels. To aid in analysis, we introduce a generalized model for reactive jamming, applicable to both repeater-based and sensing-based jamming behaviors. Despite being limited to electronic warfare scenarios, we hope that this work can pave the way for further research into antifragile communications.

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Section: The Meaning Of Antifragilitymentioning

confidence: 99%

Antifragile Communications

Lichtman

Vondal

Clancy

et al. 2018

IEEE Systems Journal

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“…A trend of reducing human intervention, in case of recov- ering from faults, errors and other damages, is seen for these systems. The literature presented in [59][60][61][62][63] are a few cases advocating the need to induce knowledge and intelligence in modern systems. These intelligent systems, when deployed with minimum knowledge, must learn from their environment in the wake of challenges and obstacles, making mistakes and growing stronger as time passes by.…”

Section: Antifragile Cyber Securitymentioning

confidence: 99%

Modelling cyber security for software-defined networks those grow strong when exposed to threats

Ahmed

Raza

Hussain

et al. 2015

J Reliable Intell Environ

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With each passing day, the information and communication technologies are evolving with more and more information shared across the globe using the internet superhighway. The threats to information, while connected to the cyber world are getting more targeted, voluminous, and sophisticated requiring new antifragile and resilient network security mechanisms. Whether the information is being processed in the application, in transit within the network or residing in the storage, it is equally susceptible to attack at every level of abstraction and cannot be handled in isolation as the case has been with conventional security mechanisms. The advent of Software-Defined Networks (SDN) has given a new outlook to information protection, where the network can aid in the design of a system that is secure and dependable in case of cyber threats. The nature of SDN, mainly its programmability and centrality of network information and control has led us to think of security in an antifragile per- Pak-UK Institute of Innovative Technologies for Disaster Management, University of Gujrat, Gujrat, Pakistan spective. Our networks can now thrive and grow stronger when they are exposed to volatility by overwhelming cyber threats. However, SDN infrastructure itself is susceptible to severe threats that may mutilate the provision of its usability as security provider. Both these perspectives of "Security with SDN" and "Security for SDN" have invited research and innovations, yet both these approaches remain disintegrated, failing to support each other. The contribution of this paper is threefold, with first reviewing the current state of the art work for both perspectives of SDN security. Second, it advocates the necessity and introduces a novel approach of antifragile cyber security within SDN paradigm and finally it proposes a unified model for integrating both approaches of "Security with SDN" and "Security for SDN" to achieve the overall objective of protecting our information from cyber threats in this globally connected internetwork.

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“…Those responses may depend, e.g., on the quality and performance of the involved organs. Said quality may be modeled as a dynamic system and expressed in terms of fidelity and its fluctuations (called "driftings" in [25,13]). Moreover, as responses call for energy and energy being often a limited and valuable commodity, responses enacted by some nodes are likely to exact energy from other nodes 5 .…”

Section: Preliminary Conclusionmentioning

confidence: 99%

On environments as systemic exoskeletons: crosscutting optimizers and antifragility enablers

Florio

2015

J Reliable Intell Environ

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Classic approaches to general systems theory often adopt an individual perspective and a limited number of systemic classes. As a result, those classes include a wide number and variety of systems that result equivalent to each other. This paper presents a different approach: first, systems belonging to a same class are further differentiated according to five major general characteristics. This introduces a "horizontal dimension" to system classification. A second component of our approach considers systems as nested compositional hierarchies of other sub-systems. The resulting "vertical dimension" further specializes the systemic classes and makes it easier to assess similarities and differences regarding properties such as resilience, performance, and quality of experience. Our approach is exemplified by considering a telemonitoring system designed in the framework of Flemish project "Little Sister". We show how our approach makes it possible to design intelligent environments able to closely follow a system's horizontal and vertical organization and to artificially augment its features by serving as crosscutting optimizers and as enablers of antifragile behaviors.

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Antifragility = Elasticity + Resilience + Machine Learning Models and Algorithms for Open System Fidelity

Cited by 28 publications

References 30 publications

Antifragile Communications

Antifragile Communications

Modelling cyber security for software-defined networks those grow strong when exposed to threats

On environments as systemic exoskeletons: crosscutting optimizers and antifragility enablers

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