# A New AI-Based Semantic Cyber Intelligence Agent
## 1 Introduction  
By the year 2020, the global cost incurred as a result of cyber-attacks had surpassed $1 trillion USD [1]. Projections indicate that by 2025, cybercrimes will have an annual cost of up to $10.5 trillion USD [2]. Notably, larger economies, including China, Brazil, the United States, India, Mexico, France, Australia, and the United Arab Emirates, face billions of dollars in consumer losses due to cybercrime [3]. For instance, in 2017, Chinese consumers experienced a financial loss of $66.3 billion USD attributed to cybercrime [3]. Moreover, apart from financial ramifications, cyber-attacks have profound social and psychological implications for individuals [4]. A recent example is the cyber-attack on Optus, a prominent Australian telecommunications company, which resulted in widespread stress and anger among the affected individuals [5, 6]. As a consequence of this cyber-attack, sensitive information, such as names, dates of birth, email addresses, driver's license details, Medicare cards, passport numbers, and more, may have been exposed, impacting 2.1 million people [6, 7]. More recently, cybercriminals managed to obtain confidential patient data from Australia's leading health insurance provider, which included medical diagnoses and procedures [8]. Thus, cybercrimes stands as a paramount challenge for contemporary countries, states, corporations, and individuals. To mitigate the effects of cybercrime, two crucial prerequisites are the availability of cyberspace-related data and advanced analytical algorithms capable of detecting and preventing threats. Recent studies [1, 9] have emphasized the importance of cyber data. Such data can be acquired through actual network traffic analysis [10; 11; 12; 13], simulation methods [14], surveys [15], open-source antivirus intelligence [16], and even social media data [17; 18; 19; 20; 21; 22; 23]. Among these various sources of cyber intelligence, open-source data and social media data from platforms such as Twitter have been identified as the most effective means of obtaining cyber data, as supported by recent research findings [16; 17; 18; 19; 20; 21; 22; 23]. This paper introduces a novel multi-agent system designed to achieve the generation of multi-dimensional cyber intelligence through semantic communication among its constituent agents. The proposed agent effectively utilizes both social media data and web-based data to deliver a rigorous and comprehensive intelligence output to cyber analysts and strategists. Notably, the generation of cyber intelligence capitalizes on cutting-edge advancements in artificial intelligence (AI) and machine learning (ML) algorithms. Figure 1 illustrates the schematic representation of the system wherein a user submits a request for cyber intelligence acquisition to the aggregation agent. The aggregation agent facilitates communication with two distinct agents, namely, the social media agent and the web media agent. The social media agent leverages various algorithms, including sentiment analysis, translation, term frequency analysis, topic modelling, and anomaly detection, to deliver cyber intelligence based on social media platforms, such as Twitter. These algorithms are implemented through both application programming interfaces (APIs) and non-API methods. The execution of API-based natural language processing (NLP) algorithms, such as sentiment analysis and translations, is carried out by a separate agent called the cognitive service agent. In addition to acquiring cyber intelligence from social media sources, the web media agent also provides cyber threat intelligence by encompassing the spectrum of threats specific to selected countries, which is sourced from antivirus vendor websites, such as Kaspersky. Finally, the user is furnished with a comprehensive cyber intelligence report that amalgamates knowledge obtained through AI-driven acquisition from both social media and web media sources.