Monitoring post-release behavioural activity of captive-bred urial (Ovis vignei punjabiensis) at Togh Managara Safari Park Khyber Pakhtunkhwa, Pakistan

The Punjab urial (Ovis vignei punjabiensis) is a globally threatened wild sheep species. In Pakistan the species is legally protected and bred in captivity to release into natural habitats to reinforce wild populations. Vigilance and feeding are critical to survival, though the degree to which captive-bred animals exhibit these behaviours post-release has rarely been investigated. We monitored the daily behaviours of five adult urial after release and classifying behaviours from 0600 hrs to 1800 hrs, replicating each one-hour period twice and recording four observations of each animal per hour. At the herd level, vigilance and feeding behaviours were exhibited equally. Males and females invested equal time in feeding, males were more vigilant (p = 0.001) and more aggressive (p = 0.010), and females were more restful (p = 0.019) or engaged in grooming (p = 0.044) or locomotion (p = 0.002). Females spent more time feeding than being vigilant (p = 0.002). Males maintained high levels of vigilance throughout the day. Patterns of resting 1300 hrs to 1500 hrs and feeding in early morning and late afternoon were common for both sexes. Behaviours classified as ‘other’ were exhibited equally between sexes. Our results reveal positive indications of captive-bred urial balancing threat detection and energy acquisition post-release and exhibiting natural behaviours and activity patterns. We encourage assessment of survivorship to evaluate long-term effectiveness of captive breeding and release as a candidate restoration programme.

APPLICATION OF THE THREAT INTELLIGENCE PLATFORM TO INCREASE THE SECURITY OF GOVERNMENT INFORMATION RESOURCES

With the development of information technology, the need to solve the problem of information security has increased, as it has become the most important strategic resource. At the same time, the vulnerability of the modern information society to unreliable information, untimely receipt of information, industrial espionage, computer crime, etc. is increasing. In this case, the speed of threat detection, in the context of obtaining systemic information about attackers and possible techniques and tools for cyberattacks in order to describe them and respond to them quickly is one of the urgent tasks. In particular, there is a challenge in the application of new systems for collecting information about cyberevents, responding to them, storing and exchanging this information, as well as on its basis methods and means of finding attackers using integrated systems or platforms. To solve this type of problem, the promising direction of Threat Intelligence as a new mechanism for acquiring knowledge about cyberattacks is studied. Threat Intelligence in cybersecurity tasks is defined. The analysis of cyberattack indicators and tools for obtaining them is carried out. The standards of description of compromise indicators and platforms of their processing are compared. The technique of Threat Intelligence in tasks of operative detection and blocking of cyberthreats to the state information resources is developed. This technique makes it possible to improve the productivity of cybersecurity analysts and increase the security of resources and information systems.

A Deep Learning Approach for Malware and Software Piracy Threat Detection

Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.