Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

Abstract

Cyber Threat Intelligence (CTI) can be used by organisations to assist their security teams in safeguarding their networks against cyber-attacks. This can be achieved by including threat data feeds into their networks or systems. However, despite being an effective Cyber Security (CS) tool, many organisations do not sufficiently utilise CTI. This is due to a number of reasons such as not fully understanding how to manage a daily flood of data filled with extraneous information across their security systems. This adds an additional layer of complexity to the tasks performed by their security teams who might not have the appropriate tools or sufficient skills to determine what information to prioritise and what information to disregard. Therefore, to help address the stated issue, this paper aims firstly to provide an in-depth understanding of what CTI is and how it can benefit organisations, and secondly to deliver a brief analysis of the application of Artificial Intelligence and Machine Learning in generating actionable CTI. The key contribution of this paper is that it assists organisations in better understanding their approach to CTI, which in turn will enable them to make informed decisions in relation to CTI.

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

“…It is often difficult to obtain consistent results and the required quality of the image with each test image due to several interferences and limitations of this technique. It is often observed that pre-defined algorithms or scripts may work fine for a certain set of images while it may not be able to process other sets of images due to inconsistencies that may be due to various factors [82,83]. To address these issues, various AI and machine learning tools can be adopted.…”

“…Machine learning will enable the processing of large datasets, and train prediction models which can implement intelligence as per the training models. This would give higher accuracy of the obtained results [82][83][84]. As per the extensive review carried out for this study, numerous technologies are currently being used involving artificial intelligence and machine learning; however, not much work is being carried out to integrate technologies for enhanced management of floods.…”

“…To improve the existing systems, the authorities can map the flooded region either using aerial imagery through satellites or using UAVs allocated to the affected regions. The UAV route can be optimised to provide maximum area coverage of the area in minimum time and cost [83] (Figure 6). The UAV swarm will collect the images of the region and provide the gathered data to the control centre [85][86][87][88].…”

“…The UAV route can be optimised to provide maximum area coverage of the area in minimum time and cost [83] (Figure 6). The UAV swarm will collect the images of the region and provide the gathered data to the control centre [85][86][87][88].…”

A threat intelligence framework for protecting smart satellite-based healthcare networks

“…This raises the need for implementing a more effective and proactive security approach such as threat intelligence (TI) [13,14]. TI refers to any evidence or event-based knowledge about potential attacks that highlight the risk landscape [15]. It can be described as a big picture of the attacker's intention and capability to target a specific asset that enables the organization (e.g.…”