Fingerprinting Radio Frequency (RF) emitters typically involves finding unique emitter characteristics that are featured in their transmitted signals. These fingerprints are nuanced but sufficiently detailed, motivating the pursuit of methods that can successfully extract them. The most granular downstream task is known as Specific Emitter Identification (SEI), which requires a well informed RF fingerprinting (RFF) approach for it to be successful. RFF and SEI have a long history, with numerous application areas in defence and civilian contexts such as signal intelligence, electronic surveillance, physical-layer authentication of wireless communication devices, to name a few. RFF methods also support many other downstream tasks such as Emitter Data Association (EDA) and RF Emitter Clustering (RFEC) and are applicable to a range of transmission types. In recent years, data-driven approaches have become popular in the RFF domain due to their ability to automatically learn intricate fingerprints from raw data. These methods generally deliver superior performance when compared to traditional techniques. The more traditional approaches are often labour-intensive, inflexible and only applicable to a particular emitter type or transmission scheme. Therefore, we consider data-driven Machine Learning (ML)-enabled RFF. In particular, we propose a generic framework for ML-enabled RFF which is inclusive of several popular downstream tasks such as SEI, EDA and RFEC. Each task is formulated as a RF fingerprint-dependent task. A variety of use cases using real RF datasets are presented here to demonstrate the framework for a range of tasks and application areas, such as spaceborne surveillance, signal intelligence and countering drones.

The recent rise in increasingly sophisticated cyber-attacks raises the need for robust and resilient autonomous cyber-defence (ACD) agents. Given the variety of cyber-attack tactics, techniques and procedures (TTPs) employed, learning approaches that can return generalisable policies are desirable. Meanwhile, the assurance of ACD agents remains an open challenge. We address both challenges via an empirical game-theoretic analysis of deep reinforcement learning (DRL) approaches for ACD using the principled double oracle (DO) algorithm. This algorithm relies on adversaries iteratively learning (approximate) best responses against each others' policies; a computationally expensive endeavour for autonomous cyber operations agents. In this work we introduce and evaluate a theoretically-sound, potential-based reward shaping approach to expedite this process. In addition, given the increasing number of open-source ACD-DRL approaches, we extend the DO formulation to allow for multiple response oracles (MRO), providing a framework for a holistic evaluation of ACD approaches.

It looks like a cross between a toy airplane and a drone, but this British solar-powered aircraft could be the future of aerial surveillance. PHASA-35, built by British company BAE Systems, is a 150kg solar-electric aircraft that can quietly cruise through the stratosphere for months at a time. Named after its 35-metre wingspan, the unmanned aerial vehicle (UAV) travels at a maximum height of 70,000 feet, at a leisurely speed of 55mph. Designed as a cheaper and lighter alternative to satellites, it can be used for Earth observation and surveillance, border control, communications and disaster relief. Now, BAE Systems reveals that PHASA-35 has just completed a second round of test flights into the stratosphere – the second layer of Earth's atmosphere.

Intelligent autonomous agents hold much potential for the domain of cyber security. However, due to many state-of-the-art approaches relying on uninterpretable black-box models, there is growing demand for methods that offer stakeholders clear and actionable insights into their latent beliefs and motivations. To address this, we evaluate Theory of Mind (ToM) approaches for Autonomous Cyber Operations. Upon learning a robust prior, ToM models can predict an agent's goals, behaviours, and contextual beliefs given only a handful of past behaviour observations. In this paper, we introduce a novel Graph Neural Network (GNN)-based ToM architecture tailored for cyber-defence, Graph-In, Graph-Out (GIGO)-ToM, which can accurately predict both the targets and attack trajectories of adversarial cyber agents over arbitrary computer network topologies. To evaluate the latter, we propose a novel extension of the Wasserstein distance for measuring the similarity of graph-based probability distributions. Whereas the standard Wasserstein distance lacks a fixed reference scale, we introduce a graph-theoretic normalization factor that enables a standardized comparison between networks of different sizes. We furnish this metric, which we term the Network Transport Distance (NTD), with a weighting function that emphasizes predictions according to custom node features, allowing network operators to explore arbitrary strategic considerations. Benchmarked against a Graph-In, Dense-Out (GIDO)-ToM architecture in an abstract cyber-defence environment, our empirical evaluations show that GIGO-ToM can accurately predict the goals and behaviours of various unseen cyber-attacking agents across a range of network topologies, as well as learn embeddings that can effectively characterize their policies.

A drone called the T-600 - named after the Terminator - successfully launched a torpedo from the sky. BAE Systems demonstrated the feat during a NATO training exercise, which saw a human controller fly the quadcopter strapped with the torpedo from a dock and over the ocean, where it let the weapon drop. The electric-powered, car-sized T-600 has a payload capacity of 441 pounds, tops speeds of 87 miles per hour and has a range of up to 50 miles. The demonstration aimed to showcase the anti-submarine warfare capabilities of the T-600 and its potential for automated logistics, resupply, casualty and evacuation. The T-600 is the AI-powered machine that takes over the world in the iconic Terminator series and features a combat endoskeleton made of titanium alloy, sometimes covered in synthetic latex.

The Air Force Research Laboratory (AFRL) awarded BAE Systems a $7.8 million contract to develop tightly integrated machine learning software as part of the Multi-Sensor Exploitation for Tactical Autonomy (META) program. This technology will enable advanced situational awareness and automatic target recognition (ATR). Under the terms of the award, BAE Systems' FAST Labs research and development organization will provide Environmentally Adaptive Geospatial Learning and Exploitation, an innovative suite of machine learning and fusion algorithms. The system integrates multiple elements of the company's extensive autonomy portfolio to provide high confidence detection, tracking, identification, and intent understanding for critical mobile targets in contested environments, including targets under camouflage, concealment, and deception. "With the addition of environmentally adaptive processing, this solution bridges a critical gap in machine learning," said Mark Kolba, program manager for BAE Systems' FAST Labs.

A British-built solar powered drone with a 115ft wingspan that can stay in the air for over a year will be an alternative to low Earth orbit satellites, its developers claim. PHASA-35 is a cutting edge drone being developed by BAE systems at their facility in Warton, Lancashire that can fly about at 70,000ft above the surface for 20 months. It harnesses power from the Sun to stay airborne, charging a bank of small batteries during the day to keep it flying overnight, allowing for longer operations. The 150kg drone is able to carry a payload of up to 15kg including cameras, sensors and communications equipment to allow troops to talk to each other or provide internet access to rural locations during a natural disaster or emergency. BAE systems say it will be available by the middle of the decade and provide a'persistent and affordable alternative to satellite technology.'

Autonomous flying'bug drones' that can spy on enemies more than a mile away and operate in strong winds of up to 50mph are being tested by the British Army. Developed by the British defence firms BAE Systems and UAVTEK, 'The Bug' is a fist-sized robot weighing just 6.7 ounces (191g) -- roughly the same as a smart phone. The drone has a 40 minute battery life and a'stealthy low visual profile' that makes it hard for the enemy to spot. The army is said to have taken delivery of 30 units. In the recent Army Warfighting Experiment, the Bug proved to be the only small drone tested that was capable of withstanding difficult weather conditions.

WASHINGTON - New software developed by BAE Systems could help the Department of Defense build confidence in decisions and intelligence produced by machine learning algorithms, the company claims. BAE Systems said it recently delivered its new MindfuL software program to the Defense Advanced Research Projects Agency in a July 14 announcement. Developed in collaboration with the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory, the software is designed to increase transparency in machine learning systems--artificial intelligence algorithms that learn and change over time as they are fed ever more data--by auditing them to provide insights about how it reached its decisions. "The technology that underpins machine learning and artificial intelligence applications is rapidly advancing, and now it's time to ensure these systems can be integrated, utilized, and ultimately trusted in the field," said Chris Eisenbies, product line director of the cmpany's Autonomy, Control, and Estimation group. "The MindfuL system stores relevant data in order to compare the current environment to past experiences and deliver findings that are easy to understand."

BAE Systems recently delivered software to the Defense Advanced Research Projects Agency (DARPA) as part of a contract under the agency--s Competency-Aware Machine Learning (CAML) program. The delivery of the MindfuL----software is the first milestone in the program to improve the transparency of machine learning systems. Transitioning artificial intelligence-based systems from decision-making tools into true partners requires users to trust in their machine counterpart. While machine learning technology has matured, these systems are unable to communicate context and confidence in their decisions -- including task strategies, the completeness of their training relative to a given task, factors that may influence their actions, or the likelihood to succeed under specific conditions. To meet these challenges, BAE Systems provided its MindfuL solution, a system which will independently --audit-- a machine learning-based system and provide the end user with insights to build trust in the technology.