Artificial Intelligence

Combating Trust Erosion: Discerning Fake News and Propaganda on Social Media in the Era of AI


This paper introduces a model to combat fake news and propaganda spread on social media, derived from a systematic literature review of 28 articles. It outlines the model based on seven key themes: scepticism, AI detection, fact checking, media literacy, ethical technology use, digital manipulation, and community verification. This comprehensive model aims to bolster individuals’ and communities’ abilities to critically assess information, emphasising its application in research, policy, and education. By advocating a multi-layered strategy, the model seeks to foster a discerning global community equipped to navigate the complexities of discerning fake news and propaganda.

Beyond Deepfakes: Synthetic Moving Images and the Future of History


This paper investigates the role of generative Artificial Intelligence (AI) tools in the production of synthetic moving images—specifically, how these images could be used in online disinformation campaigns and could profoundly affect historical footage archives. AI-manipulated content, especially moving images, will have an impact far beyond the current information warfare (IW) environment and will bleed into the unconsidered terrain of visual historical archives with unknown consequences. The paper will also consider IW scenarios in which new types of long-term disinformation campaigns may emerge and will conclude with potential verification and containment strategies.

A Systemic View of Surprise Attacks: Why It Matters


This paper examines the concept of a Surprise Attack from a systemic perspective. It looks at the approaches that can be assumed about the system when using intelligence methods to explore the environment of the system at hand. Assuming all system models are a creation of humans who often try to ascertain is that they are real, it is important that the implied complexity in terms of the system’s elements and the relationships between them are considered. The amount of reliability in the concepts that make up the system model as well as the desired outputs are also important. The boundary of the system showing the element in the system and those that are outside are also critical. A caveat is introduced to show that reality is chaos and probably no boundary around physical and conceptual system entities will truly capture everything that is relevant, so will ultimately fail in terms of surprises. The argument continues with the examination of the Known/Unknown elements within an intelligence problem, further confounding the elimination of surprise in the system. A final point adding to the production of the intelligence product is Cognitive Rigidity. Here, the use of imagination rather than just deduction of established facts and cognitive assumptions could better prepare an organization for a surprise attack.

False Information as a Threat to Modern Society: A Systematic Review of False Information, Its Impact on Society, and Current Remedies


False information and by extension misinformation, disinformation and fake news are an ever-growing concern to modern democratic societies, which value the freedom of information alongside the right of the individual to express his or her opinions freely. This paper focuses on misinformation, with the aim to provide a collation of current research on the topic and a discussion of future research directions

Mini-Drone Swarms: Their Issues and Potential in Conflict Situations


Drones are currently used for a wide range of operations, such as border surveillance, general surveillance, reconnaissance, transport, aerial photography, traffic control, earth observation, communications, broadcasting, and armed attacks.

Human Rights and Artificial Intelligence: A Universal Challenge


As artificially intelligent systems benefit citizens around the globe, there remain many ethical questions about the intrusion of AI into every aspect of our private and professional lives. This paper raises awareness of the unprecedented challenge that governments and private industry face in managing these complex systems that include regulators, markets, and special interests. 

Fitting the Artificial Intelligence Approach to Problems in DoD


Emerging and disruptive technologies, due to advances made over the last couple of decades, have become the centrepiece of Department of Defense (DoD) concerns about national security. The technologies are unique because they can both benefit and hinder the DoD from its mission. Artificial Intelligence (AI) is revered in DoD circles as one of the most important of these technologies because of its potential as an absolute ‘gamechanger’ in cybersecurity operations. In this study, the focus is on the DoD fitting the Artificial Intelligence approach to its problems in a time of limited and diminishing resources.

Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems


Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data platform service. Adversarial examples are malicious inputs to ML-models that provide erroneous model outputs while appearing to be unmodified. This kind of attack can fool the classifier and can prevent ML-models from generalizing well and from learning high-level representation; instead, the ML-model learns superficial dataset regularity. This study focuses on investigating, detecting, and preventing adversarial attacks towards a cloud data platform in the cyber-physical context.

Developing a Cyber Operations Computational Ontology


Cyber operations lack models, methodologies, and mechanisms to describe relevant data and knowledge. This problem is directly reflected when cyber operations are conducted, and their effects assessed, and it can produce dissonance and disturbance in corresponding decision-making processes and communication between different military actors.

Set Your Drones to Stun: Using Cyber-Secure Quadcopters to Disrupt Active Shooters


This paper will examine pairing the autonomous precision-flight capabilities of Micro- Unmanned Aerial Vehicles (UAVs) with the growing capability of Artificial Intelligence (specifically AI based on neuromorphic computing systems) to field cyber-secure, active-shooter response systems to counter the active-shooter threat to civilian ‘soft targets’, such as schools or train stations. This paper proposes a pilot to demonstrate the feasibility of disrupting terrorist attacks with a micro-UAV, armed with less than lethal weapons, for instance, a stun gun, where such a ‘stun-drone’ is part of an emergency-response system that is trustworthy and correctly engages only active shooters.

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.















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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.


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