This paper presents a novel authentication method based on a distributed version of Kerberos for UAVs. One of the major problems of UAVs in recent years has been cyber-attacks which allow attackers to control the UAV or access its information. The growing use of UAVs has encouraged us to investigate the methods of their protection especially authentication of their users. In the past, the Kerberos system was rarely used for authentication in UAV systems. In our proposed method, based on a distributed version of Kerberos, we can authenticate multiple ground stations, users, and controllers for one or more UAVs. This method considers most of the security aspects to protect UAV systems mainly in the authentication phase and improves the security of UAVs and ground control stations and their communications considerably.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods.

This research studies the effect of a countdown timer and a count-up timer on the keystroke pattern of the student and finds out whether changing the timer type changes the keystroke pattern. It also points out which timer affects more students in a timer environment during exams. We used two hypothesis testing statistical Algorithms, namely, the Two-Sample T-Test and One-way ANOVA Test, for analysis to identify the effect of different times our whether significant differences were found in the keystroke pattern or not when different timers were used. The supporting results have been found with determines that timer change can change the keystroke pattern of the student and from the study of hypothesis testing, different students result from different types of stress when they are under different timer environments.

The impact of digital gadgets is enormous in the current Internet world because of the easy accessibility, flexibility and time-saving benefits for the consumers. The number of computer users is increasing every year. Meanwhile, the time spent and the computers also increased. Computer users browse the internet for various information gathering and stay on the internet for a long time without control. Nowadays working people from home also spend time with the smart devices, computers, and laptops, for a longer duration to complete professional work, personal work etc. the proposed study focused on deriving the impact factors of Smartphones by analyzing the keystroke dynamics Based on the usage pattern of keystrokes the system evaluates the stress level detection using machine learning techniques. In the proposed study keyboard users are intended for testing purposes. Volunteers of 200 members are collectively involved in generating the test dataset. They are allowed to sit for a certain frame of time to use the laptop in the meanwhile the keystroke of the Mouse and keyboard are recorded. The system reads the dataset and trains the model using the Dynamic Cat-Boost algorithm (DCB), which acts as the classification model. The evaluation metrics are framed by calculating Euclidean distance (ED), Manhattan Distance (MahD), Mahalanobis distance (MD) etc. Quantitative measures of DCB are framed through Accuracy, precision and F1Score.

User authentication based on muscle tension manifested during password typing seems to be an interesting additional layer of security. It represents another way of verifying a person’s identity, for example in the context of continuous verification. In order to explore the possibilities of such authentication method, it was necessary to create a capturing software that records and stores data from EMG (electromyography) sensors, enabling a subsequent analysis of the recorded data to verify the relevance of the method. The work presented here is devoted to the design, implementation and evaluation of such a solution. The solution consists of a protocol and a software application for collecting multimodal data when typing on a keyboard. Myo armbands on both forearms are used to capture EMG and inertial data while additional modalities are collected from a keyboard and a camera. The user experience evaluation of the solution is presented, too.

Pauses in typing are generally considered to indicate cognitive processing and so are of interest in educational contexts. While much prior work has looked at typing behavior of Computer Science students, this paper presents results of a study specifically on the pausing behavior of students in Introductory Computer Programming. We investigate the frequency of pauses of different lengths, what last actions students take before pausing, and whether there is a correlation between pause length and performance in the course. We find evidence that frequency of pauses of all lengths is negatively correlated with performance, and that, while some keystrokes initiate pauses consistently across pause lengths, other keystrokes more commonly initiate short or long pauses. Clustering analysis discovers two groups of students, one that takes relatively fewer mid-to-long pauses and performs better on exams than the other.

Smart Phones being a revolution in this Modern era which is considered a boon as well as a curse, it is a known fact that most kids of the current generation are addictive to smartphones. The National Institute of Health (NIH) has carried out different studies such as exposure of smartphones to children under 12 years old, health risk associated with their usage, social implications, etc. One such study reveals that children who spend more than two hours a day, on smartphones have been seen performing poorly when it comes to language and cognitive skills. In addition, children who spend more than seven hours per day were diagnosed to have a thinner brain cortex. Hence, it is of great importance to control the amount of exposure of children to smartphones, as well as access to irregulated content. Significant research work has gone in this regard with a plethora of inputs features, feature extraction techniques, and machine learning models. This paper is a survey of the State-of-the-art techniques in detecting the age of the user using machine learning models on touch, keystroke dynamics, and sensor data.

The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

Next Word Prediction involves guessing the next word which is most likely to come after the current word. The system suggests a few words. A user can choose a word according to their choice from a list of suggested word by system. It increases typing speed and reduces keystrokes of the user. It is also useful for disabled people to enter text slowly and for those who are not good with spellings. Previous studies focused on prediction of the next word in different languages. Some of them are Bangla, Assamese, Ukraine, Kurdish, English, and Hindi. According to Census 2011, 43.63% of the Indian population uses Hindi, the national language of India. In this work, deep learning techniques are proposed to predict the next word in Hindi language. The paper uses Long Short Term Memory and Bidirectional Long Short Term Memory as the base neural network architecture. The model proposed in this work outperformed the existing approaches and achieved the best accuracy among neural network based approaches on IITB English-Hindi parallel corpus.

Trip planning, which targets at planning a trip consisting of several ordered Points of Interest (POIs) under user-provided constraints, has long been treated as an important application for location-based services. The goal of trip planning is to maximize the chance that the users will follow the planned trip while it is difficult to directly quantify and optimize the chance. Conventional methods either leverage statistical analysis to rank POIs to form a trip or generate trips following pre-defined objectives based on constraint programming to bypass such a problem. However, these methods may fail to reflect the complex latent patterns hidden in the human mobility data. On the other hand, though there are a few deep learning-based trip recommendation methods, these methods still cannot handle the time budget constraint so far. To this end, we propose a TIme-aware Neural Trip Planning (TINT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate trips under given constraints. Then, we propose a specially-designed reinforcement learning (RL) paradigm to directly optimize the objective to obtain an optimal trip generator. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Subsequently, to ensure the feedback from the discriminator is always instructive, we integrate an adversarial learning strategy into the RL paradigm to update the trip generator and the discriminator alternately. Moreover, we devise a novel pre-training schema to speed up the convergence for an efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our framework, which shows that TINT could remarkably outperform the state-of-the-art baselines within short response time.

Resiliency of cyber-physical systems (CPSs) against malicious attacks has been a topic of active research in the past decade due to widely recognized importance. Resilient CPS is capable of tolerating some attacks, operating at a reduced capacity with core functions maintained, and failing gracefully to avoid any catastrophic consequences. Existing work includes an architecture for hierarchical control systems, which is a subset of CPS with wide applicability, that is tailored for resiliency. Namely, the architecture consists of local, network and supervision layers and features such as simplex structure, resource isolation by hypervisors, redundant sensors/actuators, and software defined network capabilities. Existing work also includes methods of ensuring a level of resiliency at each one of the layers, respectively. However, for a holistic system level resiliency, individual methods at each layers must be coordinated in their deployment because all three layers interact for the operation of CPS. For this purpose, a resiliency coordinator for CPS is proposed in this work. The resiliency coordinator is the interconnection of central resiliency coordinator in the supervision layer, network resiliency coordinator in the network layer, and finally, local resiliency coordinators in multiple physical systems that compose the physical layer. We show, by examples, the operation of the resiliency coordinator and illustrate that RC accomplishes a level of attack resiliency greater than the sum of resiliency at each one of the layers separately.

Childcare, a critical infrastructure, played an important role to create community resiliency during the COVID-19 pandemic. By finding pathways to remain open, or rapidly return to operations, the adaptive capacity of childcare providers to offer care in the face of unprecedented challenges functioned to promote societal level mitigation of the COVID-19 pandemic impacts, to assist families in their personal financial recoveries, and to provide consistent, caring, and meaningful educational experiences for society's youngest members. This paper assesses the operational adaptations of childcare centers as a key resource and critical infrastructure during the COVID-19 pandemic in the Greater Rochester, NY metropolitan region. Our findings evaluate the policy, provider mitigation, and response actions documenting the challenges they faced and the solutions they innovated. Implications for this research extend to climate-induced disruptions, including fires, water shortages, electric grid cyberattacks, and other disruptions where extended stay-at-home orders or service critical interventions are implemented.

Power system robustness against high-impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorms is evaluated on the IEEE reliability test system (RTS) by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered discrete characteristics of the system.

Recently, Cloud Computing became one of today’s great innovations for provisioning Information Technology (IT) resources. Moreover, a new model has been introduced named Fog Computing, which addresses Cloud Computing paradigm issues regarding time delay and high cost. However, security challenges are still a big concern about the vulnerabilities to both Cloud and Fog Computing systems. Man- in- the- Middle (MITM) is considered one of the most destructive attacks in a Fog Computing context. Moreover, it’s very complex to detect MiTM attacks as it is performed passively at the Software-Defined Networking (SDN) level, also the Fog Computing paradigm is ideally suitable for MITM attacks. In this paper, a MITM mitigation scheme will be proposed consisting of an SDN network (Fog Leaders) which controls a layer of Fog Nodes. Furthermore, Multi-Path TCP (MPTCP) has been used between all edge devices and Fog Nodes to improve resource utilization and security. The proposed solution performance evaluation has been carried out in a simulation environment using Mininet, Ryu SDN controller and Multipath TCP (MPTCP) Linux kernel. The experimental results showed that the proposed solution improves security, network resiliency and resource utilization without any significant overheads compared to the traditional TCP implementation.

An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results.

With the inception of the Spectre attack in 2018, microarchitecture mitigation strategies propose secure cache hi-erarchies that do not leak the speculative state. Among many mitigation strategies, MuonTrap, proposes an efficient, secure cache hierarchy that provides speculative attack resiliency with minimum performance slowdown. Hardware prefetchers play a significant role in improving application performance by fetching and bringing data and instructions into caches before time. To prevent hardware prefetchers from leaking information about the speculative blocks brought into the cache, MuonTrap trains and triggers hardware prefetchers on the committed instruction streams, eliminating speculative state leakage. We find that on-commit prefetching can lead to significant performance slowdown as high as 20.46 % (primarily because of prefetch timeliness issues), making hardware prefetchers less effective. We propose Speculative yet Secure Prefetching (SpecPref), enhancements on top of the MuonTrap hierarchy that allows prefetching both on-commit and speculatively. We focus on improving the performance slowdown with the state-of-the-art hardware prefetchers without compromising the security guarantee provided by the MuonTrap implementation and provide an average performance slowdown of 1.17%.

Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC).

Safety-critical systems require resiliency against both cyberattacks and environmental faults. Researches have shown that microkernels can isolate components and limit the capabilities of would-be attackers by confining the attack in the component that it is initiated in. This limits the propagation of faults to sensitive components in the system. Nonetheless, the isolation mechanism in microkernels is not fully investigated for its resiliency against hardware faults. This paper investigates whether microkernels provide protection against hardware faults and, if so, to what extent quantitatively. This work is part of an effort in establishing an overlap between security and reliability with the goal of maximizing both while minimizing their impact on performance. In this work, transient faults are emulated on the seL4 microkernel and Linux kernel using debugger-induced bit flips across random timestamps in benchmark applications. Results show differences in the frequency and final outcome of fault to error manifestation in the seL4 environment compared to the Linux environment, including a reduction in silent data corruptions.

Acoustic communication is a key enabler for underwater Internet of Things networks between autonomous underwater platforms. Underwater Internet of Things networks face a harsh communications environment and limited energy resources which makes them susceptible to interference, whether intentional (i.e., jamming) or unintentional. Resilient, power efficient waveforms and modulation schemes are needed for underwater acoustic communications in order to avoid outages and excessive power drain. We explore the impact of modulation scheme on the resiliency of underwater acoustic communications in the presence of channel impairments, interference, and jamming. In particular, we consider BFSK and OFDM schemes for underwater acoustic communications and assess the utility of Polar coding for strengthening resiliency.

True Random Number Generator (TRNG) is an important hardware security primitive for system security. TRNGs are capable of providing random bits for initialization vectors in encryption engines, for padding and nonces in authentication protocols and for seeds to pseudo random number generators (PRNG). A TRNG needs to meet the same statistical quality standards as a physical unclonable function (PUF) with regard to randomness and uniqueness, and therefore one can envision a unified architecture for both functions. In this paper, we investigate a FPGA implementation of a TRNG using the Shift-register Reconvergent-Fanout (SiRF) PUF. The SiRF PUF measures path delays as a source of entropy within a engineered logic gate netlist. The delays are measured at high precision using a time-to-digital converter, and then processed into a random bitstring using a series of linear-time mathematical operations. The SiRF PUF algorithm that is used for key generation is reused for the TRNG, with simplifications that improve the bit generation rate of the algorithm. This enables the TRNG to leverage both fixed PUF-based entropy and random noise sources, and makes the TRNG resilient to temperature-voltage attacks. TRNG bitstrings generated from a programmable logic implementation of the SiRF PUF-TRNG on a set of FPGAs are evaluated using statistical testing tools.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

Physical Unclonable Functions (PUFs) are the secured hardware primitives to authenticate Integrated Circuits (ICs) from various unauthorized attacks. The secured key generation mechanism through PUFs is based on random Process Variations (PVs) inherited by the CMOS transistors. In this paper, we proposed a chaotic-based challenge generation mechanism to feed the arbiter PUFs. The chaotic property is introduced to increase the non-linearity in the arbitration mechanism thereby the uncertainty of the keys is attained. The chaotic sequences are easy to generate, difficult to intercept, and have the additional advantage of being in a large number Challenge-Response Pair (CRP) generation. The proposed design has a significant advantage in key generation with improved uniqueness and diffuseness of 47.33%, and 50.02% respectively. Moreover, the enhancement in the reliability of 96.14% and 95.13% range from −40C to 125C with 10% fluctuations in supply voltage states that it has prominent security assistance to the Internet of Things (IoT) enabled devices against malicious attacks.

The robustness of the encryption systems in all of their types depends on the key generation. Thus, an encryption system can be said robust if the generated key(s) are very complex and random which prevent attackers or other analytical tools to break the encryption system. This paper proposed an enhanced key generation based on iris image as biometric, to be implemented dynamically in both of authentication process and data encryption. The captured iris image during the authentication process will be stored in a cloud server to be used in the next login to decrypt data. While in the current login, the previously stored iris image in the cloud server would be used to decrypt data in the current session. The results showed that the generated key meets the required randomness for several NIST tests that is reasonable for one use. The strength of the proposed approach produced unrepeated keys for encryption and each key will be used once. The weakness of the produced key may be enhanced to become more random.