Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions
With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2194 |
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| author | Pilla Vaishno Mohan Shriniket Dixit Amogh Gyaneshwar Utkarsh Chadha Kathiravan Srinivasan Jung Taek Seo |
| author_facet | Pilla Vaishno Mohan Shriniket Dixit Amogh Gyaneshwar Utkarsh Chadha Kathiravan Srinivasan Jung Taek Seo |
| author_sort | Pilla Vaishno Mohan |
| collection | DOAJ |
| description | With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research. |
| first_indexed | 2024-03-09T12:41:40Z |
| format | Article |
| id | doaj.art-321ff23f5ccf40b6b30a5af75582785b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:41:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-321ff23f5ccf40b6b30a5af75582785b2023-11-30T22:17:32ZengMDPI AGSensors1424-82202022-03-01226219410.3390/s22062194Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future DirectionsPilla Vaishno Mohan0Shriniket Dixit1Amogh Gyaneshwar2Utkarsh Chadha3Kathiravan Srinivasan4Jung Taek Seo5School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaDepartment of Computer Engineering, Gachon University, Seongnam 13120, KoreaWith information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.https://www.mdpi.com/1424-8220/22/6/2194defensive deceptionmachine-learningdeep learningcomputational intelligencehoneypotsmoving target defense |
| spellingShingle | Pilla Vaishno Mohan Shriniket Dixit Amogh Gyaneshwar Utkarsh Chadha Kathiravan Srinivasan Jung Taek Seo Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions Sensors defensive deception machine-learning deep learning computational intelligence honeypots moving target defense |
| title | Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions |
| title_full | Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions |
| title_fullStr | Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions |
| title_full_unstemmed | Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions |
| title_short | Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions |
| title_sort | leveraging computational intelligence techniques for defensive deception a review recent advances open problems and future directions |
| topic | defensive deception machine-learning deep learning computational intelligence honeypots moving target defense |
| url | https://www.mdpi.com/1424-8220/22/6/2194 |
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