Social media behaviour plays a vital role in maintaining cyber resilience in place, and research suggests that twisted behaviours can increase the vulnerability of cyber-attacks. They suggest that an individual’s permanent or temporary cognitive impairment can lead to temperament, impulsiveness, and activities that increase the cyber risk. Online spaces have residual risk that is associated with unlimited opportunities for the user and lead to mental conditions that emerge in many ways, which are of particular interest to cybercriminals.
The human brain distraction functions in rhythms between bursts of curious attractions. During the distraction time, the brain pauses the regular synaptic receivers and engages the mechanism to retain and reorganize active memory. The cortical noises partially influence these sequences of reorganizing the dynamic memory, contributing to the curious distractions, and dissociating the attractors. In information security, deliberately designed reconnaissance such as Social Engineering Exploits impels the brain into the curious distractions and gaining access to the reward mechanism. Hence, the influenced brain weaponizes the cyber attack by opening multiple channels to the targets, aiming to establish a breach. The effect of curious distraction is previously studied in exceptional cases from brain temperament and fatigue, PTSD, Alzheimer, ADHD, autism, and even neurodegenerative diseases. These studies all presented that a pathologically disabled human brain presents extra capacity for reward-seeking in a specific way; for example, a sense of pleasure can be an evolved deception, serving to motivate a person to pursue rewards.
In recent years, cyber gatekeepers have shown an increased interest in correlating social cyber engineering risk with mental abilities, such as dissociative learning and counterfeit pleasure-seeking, which can produce an intensive motivation to focus on the target. Hence, risk studies could use this concept to design practical tests to target human perception’s weakest areas. It has been a specific discussion among the cyber safety research community to correlate the components of curious distraction with mental conditions as a significant cyber risk. This research focuses on a type-test, which triggers the anticipation function via visual distraction as a substantial contributor to the curious distraction’s hyperactivity on an impaired hedonic and compulsive reward-seeking system. A neural network models visual distraction via eye-tracking to map the entire tracking parameters. The map is then analyzed using a machine-learning algorithm to predict any conditions that may lead to possible correlations with the cybersecurity risk measures. This research suggests a novel method to measure behaviour that could lead to a potential cyber attack.
A novel algorithm is being researched at Silkatech, based on a combination of Generic-Leaky-Integrate-and-Fire (GLIF) neural network and wake-sleep algorithm. The main components of this research, is decrypting human contribution to the risk of a cyber attack by looking into biometrics and decoding the hidden cyber risk components. The correlating components will then be analyzed through a series of real-time tests by tracking the eye-gaze map for all temporal dynamic regions, and the GLIF model learns to dynamically re-map for these new regions. Next, the learning-based algorithm is developed, following the wake-sleep machine’s principles to predict high-risk situations. The gaze data set is generated using a wearable eye tracking device and a pre-condition mental health dictionary borrowed from the referenced research. Finally, the correlating data is analyzed and generates a new cyber risk assessment data set.
