Novel Application of Open-Source Cyber Intelligence

Abstract

The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people’s lives on social and psychological levels. Conventional practice involves cyber experts sourcing data from various outlets and applying personal discernment and rational inference to manually formulate cyber intelligence specific to a country. This traditional approach introduces personal bias towards the country-level cyber reports. However, this paper reports a novel approach where country-level cyber intelligence is automatically generated with artificial intelligence (AI), employing cyber-related social media posts and open-source cyber-attack statistics. Our innovative cyber threat intelligence solution examined 37,386 tweets from 30,706 users in 54 languages using sentiment analysis, translation, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), N-gram, and Porter stemming. Moreover, the presented study utilized 238,220 open-intelligence cyber-attack statistics from eight different web links, to create a historical cyber-attack dataset. Subsequently, AI-based algorithms, like convolutional neural network (CNN), and exponential smoothing were used for AI-driven insights. With the confluence of the voluminous Twitter-derived data and the array of open-intelligence cyber-attack statistics, orchestrated by the AI-driven algorithms, the presented approach generated seven-dimensional cyber intelligence for Australia and China in complete automation. Finally, the topic analysis on the cyber-related social media messages revealed seven main themes for both Australia and China. This methodology possesses the inherent capability to effortlessly engender cyber intelligence for any country, employing an autonomous modality within the realm of pervasive computational platforms.

Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges

“…By applying LDA, the study discerns underlying thematic patterns in the tweets, offering a comprehensive and granular perspective on the cyber-related discussions and conversations within distinct geographic contexts. It should be mentioned that generic preprocessing steps like transforming all the posts into lower case texts, removing stop words, removing hypertext markup tags, and tokenizing were performed similarly to recent studies in [17,22,35].…”

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

“…The dataset offers both percentage-wise attack statistics and world-ranking-wise attack statistics, offering researchers a multifaceted view of the cyber-threat landscape. Though some of our recent projects have already utilized a smaller subset of this dataset, demonstrating its versatility and applicability across various domains [13][14][15][16], the full dataset had yet to be publicly released (as shown in Table 1). The public release and description of this dataset offers numerous benefits.…”

“…For predicting time-series data there are some popular methods, like exponential smoothing [39], autoregressive integrated moving average (ARIMA) [40], and neural networks [40,41]. In our previous research, an exponential smoothing algorithm was used to predict cyber-attacks from time-series data [13][14][15] using Microsoft Power BI implementation. However, in this study neural-network-based forecasting was implemented with Python code within a Microsoft Power BI environment.…”