Drones Detection Using Smart Sensors

Drones are modern and sophisticated technology that have been used in numerous fields. Nowadays, many countries use them in exploration, reconnaissance operations, and espionage in military operations. Drones also have many uses that are not limited to only daily life. For example, drones are used for home delivery, safety monitoring, and others. However, the use of drones is a double-edged sword. Drones can be used for positive purposes to improve the quality of human lives, but they can also be used for criminal purposes and other detrimental purposes. In fact, many countries have been attacked by terrorists using smart drones. Hence, drone detection is an active area of research and it receives the attention of many scholars. Advanced drones are, many times, difficult to detect, and hence they, sometimes, can be life threatening. Currently, most detection methods are based on video, sound, radar, temperature, radio frequency (RF), or Wi-Fi techniques. However, each detection method has several flaws that make them imperfect choices for drone detection in sensitive areas. Our aim is to overcome the challenges that most existing drone detection techniques face. In this thesis, we propose two modeling techniques and compare them to produce an efficient system for drone detection. Specifically, we compare the two proposed models by investigating the risk assessments and the probability of success for each model

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-2

RF Fingerprinting Unmanned Aerial Vehicles

As unmanned aerial vehicles (UAVs) continue to become more readily available, their use in civil, military, and commercial applications is growing significantly. From aerial surveillance to search-and-rescue to package delivery the use cases of UAVs are accelerating. This accelerating popularity gives rise to numerous attack possibilities for example impersonation attacks in drone-based delivery, in a UAV swarm, etc. In order to ensure drone security, in this project we propose an authentication system based on RF fingerprinting. Specifically, we extract and use the device-specific hardware impairments embedded in the transmitted RF signal to separate the identity of each UAV. To achieve this goal, AlexNet with the data augmentation technique was employed

Multimedia Context Awareness for Smart Mobile Environments

openNowadays the development of the IoT framework and the resulting huge number of smart connected devices opens the door to exploit the presence of multiple smart nodes to accomplish a variety of tasks. Multimedia context awareness, together with the concept of ambient intelligence, is tightly related to the IoT framework, and it can be applied to a large number of smart scenarios. In this thesis, the aim is to study and analyze the role of context awareness in different applications related to smart mobile environments, such as future smart spaces and connected cities. Indeed, this research work focuses on different aspects of ambient intelligence, such as audio-awareness and wireless-awareness. In particular, this thesis tackles two main research topics: the first one, related to the framework of audio-awareness, concerns a multiple observations approach for smart speaker recognition in mobile environments; the second one, tied to the concept of wireless-awareness, regards Unmanned Aerial Vehicle (UAV) detection based on WiFi statistical fingerprint analysis.openXXXI CICLO - SC. E TECN. ING. ELETTR. E DELLE TEL. - Ambienti cognitivi interattiviGaribotto, Chiar

Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS signals is feasible because they exhibit different combined features such as strength, variance, and distribution due to their differences in multipath effects. We used the supervised support vector machine (S-SVM) [17] as the learning model, which is effective for binary classification. This design is validated via extensive experiments using commodity drones in resident areas with other Wi-Fi enabled mobile devices

Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services