In the last few years, Internet of Things (IoT) devices have evolved at an accelerated rate. Smart technology is leaning towards new human-device interaction interfaces that lack screens or buttons, thus reducing the cost of the devices while offering a more natural user experience. Voice is one of the most extended IoT user interfaces nowadays and has been popularized with the advent of smart assistants. However, with the adoption of voice interfaces, new challenges arise in terms of security. According to our research, the complexity of speaker recognition has led to non-reliable voice authentication on IoT devices. Recent approaches to solve this problem include multi-factor authentication (MFA) schemes, but existing solutions, such as one-time passwords (OTP), virtual/physical buttons, and others, have a significant impact on the user experience that is not suitable for all applications and can negate the hands-free benefits of voice interfaces. In addition, the privacy concerns of voice biometrics hinder the development of personalized voice applications that require user accounting and access control policies. Motivated by this security concern, in this paper, we present a two-factor authentication (2-FA) scheme for voice-activated devices that uses a smartphone as a key to authorize voice commands when the user’s presence is detected. The proposed security model, which makes use of Wi-Fi location for presence detection, was implemented on an Amazon Echo device and an Android smartphone, the results proved that the denominated Smartphone-key model can protect against the most common attack vectors on voice-controlled devices without affecting the user experience.

Texture features and stability have generated significant interest in biometric recognition. The inner knuckle print is distinctive and difficult to fake, making it extensively used in individual identification, criminal investigation, and various other domains. In recent years, the rapid progress of deep learning technology has created new prospects for internal knuckle recognition. This paper proposes a robust inner-knuckle-print recognition system (RIKP-RS) depending on two deep learning (DL) models. This paper focuses on the key components of the inner surface of the hand namely the little finger, ring finger, middle finger, index finger, and thumb finger that are used for human identification. Using the new segmentation method, rely on the Hands Landmark Module (MediaPipe Module) to detect components that have important biometric features. By Considering the inner knuckle print (IKP) as a texture, this study adopts two effective models: The DenseNet201 model and the InceptionV3 model to extract distinctive features from every modality. Uses all the key points of inner knuckle prints (IKP) of ten fingers for concatenated fusion recognition of all the features extracted by these models. Ultimately, these features are classified by different similarity metrics that are employed to compute the matching procedure for each model individually. A dataset of 11,076K hands with left and right palms was used to evaluate the proposed system. The system achieved the best performance on this dataset with a rank-1 score of 98.45% on the denseNet201 model, a rank-1 score of 99.81% on the inceptionV3 model for all left IKP, a rank-1 score of 96.68% on the denseNet201 model, and rank-1 score of 98.32% on the inceptionV3 model for all right IKP. These results cover the inceptionV3 model for all concatenated fusion recognition. In terms of performance, the RIKP-RS outperforms the most advanced inner knuckles pattern (IKP) recognition systems.

In the last few years, Unmanned Aerial Vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over the Internet {(Internet of drones (IoD))}. Two approaches will be used in performing the safety analysis: (1) a qualitative safety analysis approach, and (2) a quantitative safety analysis approach. The first approach uses the international safety standards, namely ISO 12100 and ISO 13849 to assess the safety of drone's missions by focusing on qualitative assessment techniques. The methodology starts with hazard identification, risk assessment, risk mitigation, and finally, draws the safety recommendations associated with a drone delivery use case. The second approach presents a method for the quantitative safety assessment using Bayesian Networks (BN) for probabilistic modeling. BN utilizes the information provided by the first approach to model the safety risks related to UAVs' flights. An illustrative UAV crash scenario is presented as a case study, followed by scenario analysis, to demonstrate the applicability of the proposed approach. These two analyses, qualitative and quantitative, enable { all involved stakeholders} to detect, explore and address the risks of UAV flights, which will help the industry to better manage the safety concerns of UAVs.

The MAVLink is a lightweight communication protocol between Unmanned Aerial Vehicles (UAVs) and ground control stations (GCSs). It defines a set of bi-directional messages exchanged between a UAV (aka drone) and a ground station. The messages carry out information about the UAV's states and control commands sent from the ground station. However, the MAVLink protocol is not secure and has several vulnerabilities to different attacks that result in critical threats and safety concerns. Very few studies provided solutions to this problem. In this paper, we discuss the security vulnerabilities of the MAVLink protocol and propose MAVSec, a security-integrated mechanism for MAVLink that leverages the use of encryption algorithms to ensure the protection of exchanged MAVLink messages between UAVs and GCSs. To validate MAVSec, we implemented it in Ardupilot and evaluated the performance of different encryption algorithms (i.e. AES-CBC, AES-CTR, RC4, and ChaCha20) in terms of memory usage and CPU consumption. The experimental results show that ChaCha20 has a better performance and is more efficient than other encryption algorithms. Integrating ChaCha20 into MAVLink can guarantee its messages confidentiality, without affecting its performance, while occupying less memory and CPU consumption, thus, preserving memory and saving the battery for the resource-constrained drone.

Cybersecurity in biometric systems is an urgent requirement due to their increasing use in identity verification, especially in smartphones, surveillance systems, and electronic transactions. These systems depend on distinctive and immutable biological features, such as fingerprints, Knuckles and facial features, making them potential targets for cyberattacks. In this context, the need to develop advanced security mechanisms, including encryption, forgery detection, and multi-factor authentication, has emerged to guarantee the confidentiality of biometric data and protect it from identity theft or manipulation. This trend emphasizes the need to integrate cybersecurity and biometric technologies to secure and ensure the reliability of systems in modern digital environments. Therefore, this research paper proposes an enhanced cybersecurity for a finger knuckle print recognition system (ECS-FKPRS) using an enhanced Rubik’s cube technique within the encryption algorithm. This ECS-FKPRS begins by extracting features of key components of the dorsal finger knuckle prints, namely the base-knuckle, the major-knuckle, the minor-knuckle, and the fingernails, using two deep learning models: ResNet-50 and MobileNet-V2. To complement this, the ECS-FKPRS uses a new segmentation method based on the Hands Landmark Module (MediaPipe Module) to detect components. After that, the resulting feature vectors are extracted for each model, which will be used in conjunction with the Rubik’s cube technique to generate an efficient and strong secret key. Then, the resulting knuckle features are encrypted with the Rabbit lightweight encryption algorithm. Furthermore, the Rubik’s Cube technique proposes the integration of logistic scrambling, permutations, and SHA-256 hashing with the encrypted biometric data, which constitutes an enhanced key generation method to ensure confidentiality and integrity against statistical attacks. The system achieved excellent results with security metrics showing outstanding results: NPCR (99.61% and 99.57%), and UACI (30.22% and 27.82%), and resistance to statistical attacks on the 11,076K Hands and the ’Poly U HD’ datasets. The ECS-FKPRS results prove that deep learning models using biometric data with an encryption algorithm are efficient. Moreover, it has high durability and security.