Ahmad Y. Javaid

Mehzabien Iqbal, Ahmad Y. Javaid

Next generation ISAC networks operating in the mmWave and THz bands must provide physical layer secrecy against potential eavesdroppers (mobile and static) while coordinating distributed hybrid edge nodes under stringent power and QoS constraints. However, these requirements are rarely addressed in a unified manner in existing ISAC physical layer security designs. This paper proposes iBEAMS, a hierarchical Stackelberg--GNE--Bayesian framework for secure and energy efficient ISAC with distributed hybrid nodes. The proposed architecture integrates: (i) a Stackelberg leader at the ISAC base station that jointly optimizes total transmit power, power splitting among confidential data, artificial noise, and sensing, and broadcasts incentive prices to shape follower utilities; (ii) a Generalized Nash Equilibrium Game in which hybrid nodes select transmit powers and transmission versus jamming roles under coupled interference constraints and base-station-imposed leakage penalties; and (iii) a Bayesian cooperative refinement layer that forms geometry-aware jamming coalitions aligned with the posterior distribution of the eavesdropper's Angle of Arrival. Simulations over carrier frequencies from 28 GHz to 3 THz demonstrate hierarchical convergence of both base station and hybrid node decisions with stable cooperative friendly jamming. iBEAMS attains approximately 4.4--4.7 bps/Hz average secrecy rate, achieves about $2\times$ higher Secrecy Energy Efficiency (SEE), and delivers 30--70% higher SEE than a Stackelberg-decision-based baseline, while maintaining zero outage at 28 GHz. Moreover, the posterior-aligned jamming remains sharply directive and resilient under mobile eavesdroppers and increasing adversary density, indicating that iBEAMS can simultaneously act against static and mobile adversaries while coordinating hybrid edge nodes under limited power and QoS constraints.

Keyur Kulkarni, Ahmad Y Javaid

Since last decade, smartphones have become an integral part of everyone's life. Having the ability to handle many useful and attractive applications, smartphones sport flawless functionality and small sizes leading to their exponential growth. Additionally, due to the huge user base and a wide range of functionalities, these mobile platforms have become a popular source of information to the public through several Apps provided by the DHS Citizen Application Directory. Such wide audience to this platform is also making it a huge target for cyber- attacks. While Android, the most popular open source mobile platform, has its base set of permissions to protect the device and resources, it does not provide a security framework to defend against any attack. This paper surveys threat, vulnerability and security analysis tools, which are open source in nature, for the Android platform and systemizes the knowledge of Android security mechanisms. Additionally, a comparison of three popular tools is presented.

Tauheed Khan Mohd, Nicole Nguyen, Ahmad Y Javaid

Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human. Therefore, there is a need to develop interactive systems that could replicate a more realistic and easier human-machine interaction. On the other hand, developers and researchers need to be aware of state-of-the-art methodologies being used to achieve this goal. We present this survey to provide researchers with state-of-the-art data fusion technologies implemented using multiple inputs to accomplish a task in the robotic application domain. Moreover, the input data modalities are broadly classified into uni-modal and multi-modal systems and their application in myriad industries, including the health care industry, which contributes to the medical industry's future development. It will help the professionals to examine patients using different modalities. The multi-modal systems are differentiated by a combination of inputs used as a single input, e.g., gestures, voice, sensor, and haptic feedback. All these inputs may or may not be fused, which provides another classification of multi-modal systems. The survey concludes with a summary of technologies in use for multi-modal systems.

Tauheed Khan Mohd, Jackson Carvalho, Ahmad Y Javaid

Wearable electronic equipment is constantly evolving and is increasing the integration of humans with technology. Available in various forms, these flexible and bendable devices sense and can measure the physiological and muscular changes in the human body and may use those signals to machine control. The MYO gesture band, one such device, captures Electromyography data (EMG) using myoelectric signals and translates them to be used as input signals through some predefined gestures. Use of this device in a multi-modal environment will not only increase the possible types of work that can be accomplished with the help of such device, but it will also help in improving the accuracy of the tasks performed. This paper addresses the fusion of input modalities such as speech and myoelectric signals captured through a microphone and MYO band, respectively, to control a robotic arm. Experimental results obtained as well as their accuracies for performance analysis are also presented.

Praveen Damacharla, Achuth Rao M. V., Jordan Ringenberg et al.

Visual steel surface defect detection is an essential step in steel sheet manufacturing. Several machine learning-based automated visual inspection (AVI) methods have been studied in recent years. However, most steel manufacturing industries still use manual visual inspection due to training time and inaccuracies involved with AVI methods. Automatic steel defect detection methods could be useful in less expensive and faster quality control and feedback. But preparing the annotated training data for segmentation and classification could be a costly process. In this work, we propose to use the Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect detection. We use a U-Net architecture as the base and explore two kinds of encoders: ResNet and DenseNet. We compare these nets' performance using random initialization and the pre-trained networks trained using the ImageNet data set. The experiments are performed using Severstal data. The results demonstrate that the transfer learning performs 5% (absolute) better than that of the random initialization in defect classification. We found that the transfer learning performs 26% (relative) better than that of the random initialization in defect segmentation. We also found the gain of transfer learning increases as the training data decreases, and the convergence rate with transfer learning is better than that of the random initialization.

Praveen Damacharla, Ahmad Y. Javaid, Jennie J. Gallimore et al.

A significant amount of work is invested in human-machine teaming (HMT) across multiple fields. Accurately and effectively measuring system performance of an HMT is crucial for moving the design of these systems forward. Metrics are the enabling tools to devise a benchmark in any system and serve as an evaluation platform for assessing the performance, along with the verification and validation, of a system. Currently, there is no agreed-upon set of benchmark metrics for developing HMT systems. Therefore, identification and classification of common metrics are imperative to create a benchmark in the HMT field. The key focus of this review is to conduct a detailed survey aimed at identification of metrics employed in different segments of HMT and to determine the common metrics that can be used in the future to benchmark HMTs. We have organized this review as follows: identification of metrics used in HMTs until now, and classification based on functionality and measuring techniques. Additionally, we have also attempted to analyze all the identified metrics in detail while classifying them as theoretical, applied, real-time, non-real-time, measurable, and observable metrics. We conclude this review with a detailed analysis of the identified common metrics along with their usage to benchmark HMTs.

Aakif Mairaj, Asif I. Baba, Ahmad Y. Javaid

Over the past two decades, Unmanned Aerial Vehicles (UAVs), more commonly known as drones, have gained a lot of attention, and are rapidly becoming ubiquitous because of their diverse applications such as surveillance, disaster management, pollution monitoring, film-making, and military reconnaissance. However, incidents such as fatal system failures, malicious attacks, and disastrous misuses have raised concerns in the recent past. Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. Therefore, it is necessary to address these issues to ensure proper functioning of these UAVs while keeping their uniqueness in mind. Furthermore, adequate security and functionality require the consideration of many parameters that may include an accurate cognizance of the working mechanism of vehicles, geographical and weather conditions, and UAVNet communication. This is achievable by creating a simulator that includes these aspects. A performance evaluation through relevant drone simulator becomes indispensable procedure to test features, configurations, and designs to demonstrate superiority to comparative schemes and suitability. Thus, it becomes of paramount importance to establish the credibility of simulation results by investigating the merits and limitations of each simulator prior to selection. Based on this motivation, we present a comprehensive survey of current drone simulators. In addition, open research issues and research challenges are discussed and presented.

Ashutosh Bandekar, Ahmad Y. Javaid

Recent years have seen exponential development in wireless sensor devices and their rebirth as IoT, as well as increased popularity in wireless home devices such as bulbs, fans, and microwave. As they can be used in various fields such as medical devices, environmental studies, fire department or military application, etc., security of these low powered devices will always be a concern for all the user and security experts. Users nowadays want to control all these "smart" wireless home devices through their smartphones using an internet connection. Attacks such as distributed attacks on these devices will render the whole system vulnerable as these attacks can record and extract confidential information as well as increase resource (energy) consumption of the entire network. In this paper, we propose a cyber-attack detection algorithm and present an impact analysis of easy-to-launch cyber-attacks on a low-power mote (Z1 Zolertia) as a model IoT device. We also present detailed results of power consumption analysis with and without attack along with when the mitigation algorithm for intrusion detection is implemented.