Performance Comparison Of Weak And Strong Learners In Detecting GPS Spoofing Attacks On Unmanned Aerial Vehicles (uavs)

Unmanned Aerial Vehicle systems (UAVs) are widely used in civil and military applications. These systems rely on trustworthy connections with various nodes in their network to conduct their safe operations and return-to-home. These entities consist of other aircrafts, ground control facilities, air traffic control facilities, and satellite navigation systems. Global positioning systems (GPS) play a significant role in UAV\u27s communication with different nodes, navigation, and positioning tasks. However, due to the unencrypted nature of the GPS signals, these vehicles are prone to several cyberattacks, including GPS meaconing, GPS spoofing, and jamming. Therefore, this thesis aims at conducting a detailed comparison of two widely used machine learning techniques, namely weak and strong learners, to investigate their performance in detecting GPS spoofing attacks that target UAVs. Real data are used to generate training datasets and test the effectiveness of machine learning techniques. Various features are derived from this data. To evaluate the performance of the models, seven different evaluation metrics, including accuracy, probabilities of detection and misdetection, probability of false alarm, processing time, prediction time per sample, and memory size, are implemented. The results show that both types of machine learning algorithms provide high detection and low false alarm probabilities. In addition, despite being structurally weaker than strong learners, weak learner classifiers also, achieve a good detection rate. However, the strong learners slightly outperform the weak learner classifiers in terms of multiple evaluation metrics, including accuracy, probabilities of misdetection and false alarm, while weak learner classifiers outperform in terms of time performance metrics

Trojan Spoofing: A Threat to Critical Infrastructure

This article explores the phenomenon of location spoofing – where the spoofer is able to ‘teleport’ systems in and out of defined locations, either for the purpose of infiltration into no-go zones or for the ‘teleportation’ out of real, defined zones in the physical world. The research relied on a qualitative methodology, utilising academic research findings, media reports, hacker demonstrations, and secondary data from these sources, to situate the spoofing threat in the context of international security. This conceptual, argumentative essay finds that signal spoofing, the methods of which can be followed via online scripts, allows users the ability to overcome geographically-defined territorial restrictions. This, as this article finds, allows violent actors to weaponise systems such as unmanned ariel systems (UAS), potentially leading to the escalation of political tensions in extreme but unfortunately ever-frequent episodes. The article concludes that, while Trojan Spoofing (in particular) poses a real and an existential threat to international security, it is only a sum-of-all parts in considering other threats to critical functions in society. If geofences are to be used as a single point of security to protect assets against hostile actors, managers need to be aware of the vulnerability of intrusion and the resulting geopolitical consequences.publishedVersio

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp

Machine Learning for Intrusion Detection into Unmanned Aerial System 6G Networks

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user\u27s needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of the CPS and IoT technologies provided many connected devices, generating an enormous amount of data. Consequently, the innovation of 6G technology is an urgent issue in the coming years. The 6G network architecture is an integration of the satellite network, aerial networks, terrestrial networks, and marine networks. These integrated network layers will provide new enabling technologies, for example, air interfaces and transmission technology. Therefore, integrating heterogeneous network layers guarantees an expansion strategy in the capacity that leads to low latency, ultra-high throughput, and high data rates. In the 6G network, Unmanned Aerial Vehicles (UAVs) are expected to densely occupy aerial spaces as UAV flying base stations (UAV-FBS) that comprise the aerial network layer to offer ubiquitous connectivity and enhance the terrestrial network in remote areas where it is challenging to deploy traditional infrastructure, for example, mountain, ocean deserts, and forest. Although the aerial network layer offers benefits to facilitate governmental and commercial missions, adversaries exploit network vulnerabilities to block intercommunication among nodes by jamming attacks and violating integrity through executing spoofing attacks. This work offers a practical IDS onboard UAV intrusion detection system to detect unintentional interference, intentional interference jamming, and spoofing attacks. Integrating time series data with machine learning models is the main part of the suggested IDF to detect anomalies accurately. This integration will improve the accuracy and effectiveness of the model. The 6G network is expected to handle a high volume of data where non-malicious interference and congestion in the channel are similar to a jamming attack. Therefore, an efficient anomaly detection technique must distinguish behaviors in the drone\u27s wireless network as normal or abnormal behavior. Our suggested model comprises two layers. The first layer has the algorithm to detect the anomaly during transmission. Then it will send the initial decision to the second layer in the model, including two separated algorithms, confirming the initial decision separately (nonintentional interference such as congestion in the channel, intentional interference jamming attack, and classify the type of jamming attack, and the second algorithm confirms spoofing attack. A jamming attack is a stealthy attack that aims to exhaust battery level or block communication to make wireless UAV networks unavailable. Therefore, the UAV forcibly relies on GPS signals. In this case, the adversary triggers a spoofing attack by manipulating the Global Navigation Satellite System (GNSS) signal and sending a fake signal to make UAVs estimate incorrect positions and deviate from their planning path to malicious zones. Hackers can start their malicious action either from malicious UAV nodes or the terrestrial malicious node; therefore, this work will enhance security and pave the way to start thinking about leveraging the benefit of the 6G network to design robust detection techniques for detecting multiple attacks that happen separately or simultaneously

Current Trends in Small Unmanned Aircraft Systems: Implications for U.S. Special Operations Forces

This paper assesses current trends in small unmanned aircraft systems (sUAS) technology and its applications to the Special Operations Forces (SOF) community. Of critical concern to SOF is that commercial-off-the-shelf (COTS) sUAS technologies are relatively inexpensive, improving at a dramatic rate, and widely available throughout the world. Insurgents, terrorists, violent extremist organizations (VEOs) and other nefarious actors have used COTS sUAS to conduct offensive attacks as well as to develop battlefield situation awareness; these technological improvements combined with their widespread availability will require enhanced and rapidly adaptive counter-sUAS measures in the future. To understand the most current trends in the unmanned aircraft systems (UAS) technology and their applicability to SOF, this paper analyzes the definition and classification of sUAS, their major applications, and characteristics. In the military context, UAS are principally used for intelligence, surveillance, and reconnaissance (ISR), border security, counterinsurgency, attack and strike, target identification and designation, communications relay, electronic attack, remote sensing, and aerial mapping. As technology improves, smaller versions of sUAS will be used by both friendly operators and maligned actors (insurgents, terrorists, VEOs, nation states) as force multipliers for military operations. As armed forces around the world continue to invest in research and development of sUAS technologies, there will be tremendous potential to revolutionize warfare, particularly in context of special operations. Consequently, the use of sUAS technology by SOF is likely to escalate over the next decade, as is the likelihood of sUAS countermeasures due to the availability of the technology within nefarious organizations

Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives

The development of the Internet of Drones (IoD) becomes vital because of a proliferation of drone-based civilian or military applications. The IoD based technological revolution upgrades the current Internet environment into a more pervasive and ubiquitous world. IoD is capable of enhancing the state-of-the-art for drones while leveraging services from the existing cellular networks. Irrespective to a vast domain and range of applications, IoD is vulnerable to malicious attacks over open-air radio space. Due to increasing threats and attacks, there has been a lot of attention on deploying security measures for IoD networks. In this paper, critical threats and vulnerabilities of IoD are presented. Moreover, taxonomy is created to classify attacks based on the threats and vulnerabilities associated with the networking of drone and their incorporation in the existing cellular setups. In addition, this article summarizes the challenges and research directions to be followed for the security of IoD.Comment: 13 pages, 3 Figures, 1 Table, The 3rd International Symposium on Mobile Internet Security (MobiSec'18), Auguest 29-September 1, 2018, Cebu, Philippines, Article No. 37, pp. 1-1