Anti-poaching operations increasingly make use of a wide variety of technology for intelligence and communications. These technologies introduce cybersecurity risk, and they need to be secured to provide greater protection to the information and people involved in anti-poaching operations, ultimately protecting vulnerable animals better. A hypothetical network of anti-poaching technologies was simulated in Graphical Network Simulator 3 (GNS3), consisting of various field devices identified in the literature, and a main control room with relevant hardware devices. A virtual Kali Linux machine was connected to the network and played the role of a digital attacker or intruder. Several cyber-attacks were carried out, to show the risks inherent to such an interoperable and socio-technical network. These attacks included Man in the Middle (MitM) and Denial of Service (DoS) attacks. These attacks were then mitigated via system configurations. Further risks and threat considerations were identified in the literature. Using the STRIDE, DREAD and Attack Tree threat models, the risks to an anti-poaching network were classified and calculated. The most prevalent threats and the attacks performed in the simulation were all calculated to have a high risk level, posing a great threat to an unsecured network. The STRIDE classes of Denial of Service and Elevation of Privilege posed the most risk to the system, both having a calculated average risk score of 9 out of 10. Mitigations to general network threats and those identified in the simulation are mentioned. Additionally, authentication for such a system was investigated, as improper authentication practices were deemed a risk and provides a foothold for further risks in the network. Recommendations made, include the proper configuration of network devices, especially the router and switch, and the use of anti-virus, firewalls, and intrusion detection systems, as well as having an external audit performed annually. Multi-factor authentication, with a password/fingerprint combination, is recommended.

The development of anti-poaching networks and systems has created a new environment for animals in game reserves all over the world. Advanced technologies such as heat sensors, drones, and trip wires help prevent poachers from entering the property and therefore, creating a safer environment for animals to roam freely. Radio frequency identification (RFID) systems are used to track the location of animals. These networks are, however, susceptible to being hacked if not properly protected with cybersecurity tools, resulting in cyber-criminals gaining access into the network. Many attacks or threats can be executed on the RFID network due to some exposed vulnerabilities of elements within the anti-poaching network. The purpose of this paper is to explore the empirical methods of common attacks, used by cyber-criminals, to attack the anti-poaching network, and whether these methods used are effective in identifying weaknesses within the network. This will be executed by creating an experimental structure of the anti-poaching system with a specialised focus on the RFID elements, using quantitative research methods to produce findings. GNS3, an open-source software application that has specifically been chosen to conduct this research, is used to build the network simulation in order to analyse the weaknesses of the network. Cybersecurity protocols are implemented to protect the network and aim to protect the animals. The attacks performed, such as Flood and Scapy attacks, have shown that the anti-poaching network is vulnerable to penetration from cyber-criminals. A hypothesis test was conducted to determine whether the attacks had a significant effect on the network, by using the average ping time from specific nodes to Google. It was found that the average ping time increased by 2.0020 units, therefore stating that the elements of the network were successfully attacked. The fault tolerance test shows that the availability of the anti-poaching network is roughly 90 percent which concludes that the network is configured to deliver quality performance and handle failures, should there be any intervention. This will allow game reserves to implement and have information on a better and safer RFID system for the animals.

Advanced Persistent Threats (APTs) have increased in parallel to growing cloud infrastructure and cloud Software-as-a-Service (SaaS) needs, exposing new vulnerabilities within the cloud environment. Moreover, APT groups are becoming more sophisticated and organised which needs to be addressed by the research community to enable faster response and more importantly, prevent threats within the domain. The MITRE ATT&CK Cloud framework offers one of the leading structured inventories within this context. Our research is to expose patterns and signatures of a select group of APT’s on the MITRE Cloud Framework by using Formal Concept Analysis (FCA) to construct a “lattice graph” and an ontology. The goal is to develop a better conceptualisation of the MITRE ATT&CK Cloud Matrix framework for cyber security experts to be able to proactively act upon adversary techniques. The MITRE ATT&CK framework was retrieved, cleaned, and pre-processed to construct the lattice and ontology using data cleaning methods, FCA tools such as Concept Explorer, and the Web Ontology Language (OWL), with additional symbolic reasoning and inference generation. This resulted in knowledge representations/graphs, which are highly efficient representations of this knowledge field. The underlying linkages between techniques and targets specific to those APTs are further exposed and enriched and presented visually and integrated into the ontology. The ontology gives formalisation to associations and implications between techniques, tactics, and APTs – enabling cyber security practitioners to forecast potential targets and techniques based on their scenario, but also to attribute certain technique patterns and signatures to individual APTs. Cyber security practitioners can query from this knowledge graph and formulate strategic proactive measures. From these findings, the applications and constraints of the APTs’ cyber-attack techniques and their associated patterns were determined. The findings provide a guideline for future additional research in the field of AI knowledge representation in cybersecurity, as well as highlighting certain limitations in this field of research.

sdfssd