A New AI-Based Semantic Cyber Intelligence Agent
The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/7/231 |
| _version_ | 1827732917551366144 |
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| author | Fahim Sufi |
| author_facet | Fahim Sufi |
| author_sort | Fahim Sufi |
| collection | DOAJ |
| description | The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both social media and web sources. The proposed method underwent evaluation from 13 October 2022 to 6 April 2023, utilizing a dataset comprising 37,386 tweets generated by 30,706 users across 54 languages. To address non-English content, a total of 8199 HTTP requests were made to facilitate translation. Additionally, the system processed 238,220 cyber threat data from the web. Within a remarkably brief duration of 6 s, the system autonomously generated a comprehensive cyber intelligence report encompassing 7 critical dimensions of cyber intelligence for countries such as Russia, Ukraine, China, Iran, India, and Australia. |
| first_indexed | 2024-03-11T01:03:45Z |
| format | Article |
| id | doaj.art-78379c1ab1cf491da5a9c9826db36b55 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T01:03:45Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-78379c1ab1cf491da5a9c9826db36b552023-11-18T19:26:47ZengMDPI AGFuture Internet1999-59032023-06-0115723110.3390/fi15070231A New AI-Based Semantic Cyber Intelligence AgentFahim Sufi0School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, AustraliaThe surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both social media and web sources. The proposed method underwent evaluation from 13 October 2022 to 6 April 2023, utilizing a dataset comprising 37,386 tweets generated by 30,706 users across 54 languages. To address non-English content, a total of 8199 HTTP requests were made to facilitate translation. Additionally, the system processed 238,220 cyber threat data from the web. Within a remarkably brief duration of 6 s, the system autonomously generated a comprehensive cyber intelligence report encompassing 7 critical dimensions of cyber intelligence for countries such as Russia, Ukraine, China, Iran, India, and Australia.https://www.mdpi.com/1999-5903/15/7/231cyber intelligencecyber threat analysiscyber warsituational analysissemantic agentsmulti-agent communication |
| spellingShingle | Fahim Sufi A New AI-Based Semantic Cyber Intelligence Agent Future Internet cyber intelligence cyber threat analysis cyber war situational analysis semantic agents multi-agent communication |
| title | A New AI-Based Semantic Cyber Intelligence Agent |
| title_full | A New AI-Based Semantic Cyber Intelligence Agent |
| title_fullStr | A New AI-Based Semantic Cyber Intelligence Agent |
| title_full_unstemmed | A New AI-Based Semantic Cyber Intelligence Agent |
| title_short | A New AI-Based Semantic Cyber Intelligence Agent |
| title_sort | new ai based semantic cyber intelligence agent |
| topic | cyber intelligence cyber threat analysis cyber war situational analysis semantic agents multi-agent communication |
| url | https://www.mdpi.com/1999-5903/15/7/231 |
| work_keys_str_mv | AT fahimsufi anewaibasedsemanticcyberintelligenceagent AT fahimsufi newaibasedsemanticcyberintelligenceagent |