research-article

Securing the Internet of Things with Responsive Artificial Immune Systems

Author:

Julie GreensmithAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 113 - 120

https://doi.org/10.1145/2739480.2754816

Published: 11 July 2015 Publication History

Abstract

The Internet of Things is a network of `smart' objects, transforming everyday objects into entities which can measure, sense and understand their environment. The devices are uniquely identifiable, rely on near field connectivity, often in embedded devices. The Internet of Things is designed to be deployed without human intervention or interaction. One application is the `smart house', with components including household appliances, networked with the user able to control devices remotely. However, the security inherent in these systems is added as somewhat of an afterthought. One hypothetical scenario is where a malicious party could exploit this technology with potentially disastrous consequences, turning on a cooker remotely leading to digital arson. Reliance on standard methods is insufficient to provide the user with adequate levels of security, an area where AIS may be extremely useful. There are currently limitations with AIS applied in security, focussing on detection without providing automatic responses. This problem provides an opportunity to advance AIS in providing both an ideal scenario for testing their real-world application and to develop novel responsive AIS. A responsive version of the deterministic Dendritic Cell Algorithm will be proposed to demonstrate how responsive AIS will need to be developed to meet these future challenges through proposing the incorporation of a model of T-cell responses.

References

[1]

U. Aickelin and S. Cayzer. The danger theory and its application to artificial immune systems. In Proceedings of the 1st International Conference on ARtificial Immune Systems (ICARIS 2002), Canterbury, UK, pages 141--148, 2002.

[2]

R. Chen, C. Liu, and L. Xiao. A security situation sense model based on artificial immune system in the internet of things. Advanced Materials Research, 403--408:2457--2460, 2012.

[3]

I. R. Cohen. Real and artificial immune systems: computing the state of the body. Nat Rev Immunol, 07(07):569--574, 07 2007.

[4]

L. N. de Castro and F. J. Von Zuben. Immune and neural network models: theoretical and empirical comparisons. International Journal of Computational Intelligence and Applications, 1(3):239--257, 2001.

[5]

L. N. De Castro and F. J. Von Zuben. Learning and optimization using the clonal selection principle. Evolutionary Computation, IEEE Transactions on, 6(3):239--251, 2002.

Digital Library

[6]

I. Dent, T. Craig, U. Aickelin, and T. Rodden. Variability of behaviour in electricity load profile clustering; who does things at the same time each day? In P. Perner, editor, Advances in Data Mining, volume 8557 of Lecture Notes in Computer Science, pages 70--84, Heidelberg, 2014. Springer.

[7]

Y. Ding, Y. Jin, L. Ren, and K. Hao. An intelligent self-organisation scheme for the internet of things. IEEE Computational Intelligence Magazine, pages 41--53, 2013.

Digital Library

[8]

R. L. Fanelli. A hybrid model for immune inspired network intrusion detection. In Artificial Immune Systems, pages 107--118. Springer, 2008.

Digital Library

[9]

S. Forrest, S. A. Hofmeyr, and A. Somayaji. Computer immunology. Commun. ACM, 40(10):88--96, Oct. 1997.

Digital Library

[10]

J. Greensmith. The Dendritic Cell Algorithm. PhD thesis, University of Nottingham, 2007.

[11]

F. Gu, J. Greensmith, and U. Aickelin. Theoretical formulation and analysis of the deterministic dendritic cell algorithm. Biosystems, 111 (2):127--135, 2013.

[12]

S. A. Hofmeyr and S. Forrest. Architecture for an artificial immune system. Evolutionary computation, 8(4):443--473, 2000.

Digital Library

[13]

http://www.economist.com/news/special-report/21606420-perils-connected-devices%-home-hacked home.

[14]

http://www.hivehome.com.

[15]

G. Kortuem, F. Kawsar, D. Fitton, and V. Sundramoorthy. Smart objects as building blocks for the internet of things,. IEEE Internet Computing, 14(1):44--51, 2009.

Digital Library

[16]

P. Matzinger. Tolerance, danger, and the extended family. Annual review of immunology, 12(1):991--1045, 1994.

[17]

C. M. Medaglia and A. Serbanati. An overview of privacy and security issues in the internet of things. The Internet of Things, pages pp 389--395, 2010.

[18]

H. Ning and H. Liu. Cyber-physical-social based security architecture for future internet of things. Advances in Internet of Things, 2:1--7, 2012.

[19]

M. Roesch. Snort: Lightweight intrusion detection for networks. In LISA, volume 99, pages 229--238, 1999.

Digital Library

[20]

T. Stibor, J. Timmis, and C. Eckert. A comparative study of real-valued negative selection to statistical anomaly detection techniques. In Artificial Immune Systems, pages 262--275. Springer, 2005.

Digital Library

[21]

R. V. Sutavani, R. G. Bradley, J. M. Ramage, A. M. Jackson, L. G. Durrant, and I. Spendlove. Cd55 costimulation induces differentiation of a discrete t regulatory type 1 cell population with a stable phenotype. The Journal of Immunology, 191(12):5895--5903, 2013.

[22]

J. Timmis. Artificial immune systems--today and tomorrow. Natural Computing, 6(1):1--18, 2007.

Digital Library

[23]

R. Weber. Internet of things -- new security and privacy challenges. Computer Law and Security Review, 26(1):23--30, 2010.

Cited By

Upadhyay ANaaz SThakur VAnsari I(2023)Machine Learning Approach to the Internet of Things Threat DetectionData Science and Network Engineering10.1007/978-981-99-6755-1_31(407-418)Online publication date: 3-Nov-2023
https://doi.org/10.1007/978-981-99-6755-1_31
Abdullahi MBaashar YAlhussian HAlwadain AAziz NCapretz LAbdulkadir S(2022)Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature ReviewElectronics10.3390/electronics1102019811:2(198)Online publication date: 10-Jan-2022
https://doi.org/10.3390/electronics11020198
Fortino GGreco CGuzzo AIanni M(2022)Identification and prediction of attacks to industrial control systems using temporal point processesJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-04416-514:5(4771-4783)Online publication date: 23-Sep-2022
https://doi.org/10.1007/s12652-022-04416-5
Show More Cited By

Index Terms

Securing the Internet of Things with Responsive Artificial Immune Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

Authentication Protocol for Securing Internet of Things
ICEMIS '18: Proceedings of the Fourth International Conference on Engineering & MIS 2018

The Internet of things (IoT) is become an important research topic because it integrates various sensors and objects to communicate directly with one another object without human intervention. Today, Internet of Things has so many applications in our ...
Innate immune memory and its application to artificial immune systems
Abstract
The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either ...
Threat-Based Security Analysis for the Internet of Things
SIOT '14: Proceedings of the 2014 International Workshop on Secure Internet of Things

The Internet of Things (IoT) is an emerging paradigm focusing on the inter-connection of things or devices to each other and to the users. This technology is anticipated to become an integral milestone in the development of smart homes and smart cities. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
573
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Upadhyay ANaaz SThakur VAnsari I(2023)Machine Learning Approach to the Internet of Things Threat DetectionData Science and Network Engineering10.1007/978-981-99-6755-1_31(407-418)Online publication date: 3-Nov-2023
https://doi.org/10.1007/978-981-99-6755-1_31
Abdullahi MBaashar YAlhussian HAlwadain AAziz NCapretz LAbdulkadir S(2022)Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature ReviewElectronics10.3390/electronics1102019811:2(198)Online publication date: 10-Jan-2022
https://doi.org/10.3390/electronics11020198
Fortino GGreco CGuzzo AIanni M(2022)Identification and prediction of attacks to industrial control systems using temporal point processesJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-04416-514:5(4771-4783)Online publication date: 23-Sep-2022
https://doi.org/10.1007/s12652-022-04416-5
Alrubayyi HGoteng GJaber MKelly J(2021)Challenges of Malware Detection in the IoT and a Review of Artificial Immune System ApproachesJournal of Sensor and Actuator Networks10.3390/jsan1004006110:4(61)Online publication date: 26-Oct-2021
https://doi.org/10.3390/jsan10040061
Brown JAnwar M(2021)Blacksite: human-in-the-loop artificial immune system for intrusion detection in internet of thingsHuman-Intelligent Systems Integration10.1007/s42454-020-00017-9Online publication date: 2-Jan-2021
https://doi.org/10.1007/s42454-020-00017-9
Zhou WLiang Y(2021)An immune optimization based deterministic dendritic cell algorithmApplied Intelligence10.1007/s10489-020-02098-0Online publication date: 22-May-2021
https://doi.org/10.1007/s10489-020-02098-0
Banihashemi BMaamar ZFaci NUgljanin EChbeir RManolopoulos YDamiani EBenslimane DBellatreche LMorzy T(2020)Thing Mutation as a Countermeasure to Safeguard IoTProceedings of the 12th International Conference on Management of Digital EcoSystems10.1145/3415958.3433033(96-103)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1145/3415958.3433033
Kamaldeep Dutta MGranjal J(2020)Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense MechanismsIEEE Access10.1109/ACCESS.2020.30056438(127272-127312)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3005643
Verma APrakash SSrivastava VKumar AMukhopadhyay S(2019)Sensing, Controlling, and IoT Infrastructure in Smart Building: A ReviewIEEE Sensors Journal10.1109/JSEN.2019.292240919:20(9036-9046)Online publication date: 15-Oct-2019
https://doi.org/10.1109/JSEN.2019.2922409
Talal MZaidan AZaidan BAlbahri AAlamoodi AAlbahri OAlsalem MLim CTan KShir WMohammed K(2019)Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body SensorsJournal of Medical Systems10.1007/s10916-019-1158-z43:3(1-34)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10916-019-1158-z
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten