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The safety-critical nature of vehicle steering is one of the main motivations for exploring the space of possible cyber-physical attacks against the steering systems of modern vehicles. This paper investigates the adversarial capabilities for destabilizing the interaction dynamics between human drivers and vehicle haptic shared control (HSC) steering systems. In contrast to the conventional robotics literature, where the main objective is to render the human-automation interaction dynamics stable by ensuring passivity, this paper takes the exact opposite route. In particular, to investigate the damaging capabilities of a successful cyber-physical attack, this paper demonstrates that an attacker who targets the HSC steering system can destabilize the interaction dynamics between the human driver and the vehicle HSC steering system through synthesis of time-varying impedance profiles. Specifically, it is shown that the adversary can utilize a properly designed non-passive and time-varying adversarial impedance target dynamics, which are fed with a linear combination of the human driver and the steering column torques. Using these target dynamics, it is possible for the adversary to generate in real-time a reference angular command for the driver input device and the directional control steering assembly of the vehicle. Furthermore, it is shown that the adversary can make the steering wheel and the vehicle steering column angular positions to follow the reference command generated by the time-varying impedance target dynamics using proper adaptive control strategies. Numerical simulations demonstrate the effectiveness of such time-varying impedance attacks, which result in a non-passive and inherently unstable interaction between the driver and the HSC steering system., Comment: 8 pages, 13 figures, accepted in The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), Detroit, MI, Oct. 2023

This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation task is then "distilled" (MobileNet encoder). Performance is benchmarked against three state-of-the-art encoders on a publicly available data-set emphasizing head-pose variations. Experimental evaluations show the Seg-Distilled-ID network shows notable robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on ResNet-101, 96.1% on VGG-19 and 96.3% on InceptionV3. This is achieved using approximately one-tenth of the top encoder's inference parameters. These results demonstrate distilling semantic-segmentation features can efficiently address face-recognition pose-invariance.

Personality development of an individual is a continuous process that involves multiple methods and techniques. Study on a person’s personality is conducted by examining inherited and adopted personality traits during their life span. Higher education institutes play a vital role in flourishing student’s personality and significant revolution is noticed by the researchers in this regard. Individual’s personality is being affected by use of social media including Facebook, Twitter, LinkedIn and many more. Facebook (FB) is widely used as an online social network to communicate with other individuals. It is observed that students are also users of FB for learning, interacting, recreating and browsing purposes. Our research falls in the same dimension to study student’s personality traits through their FB use and eventually analyze the change in their personality. For the same, four higher education institutes of Pakistan located in Lahore are targeted. Data is collected for five years from under graduate students from 2015-2019. It has two main purpose; firstly, to present analytical results and secondly, to examine the changes in person’s personality traits (PT) during degree program at the university. Data is collected from FB profiles through an application and Big Five Personality Trait model is used to conduct personality assessment. Five PT are studied namely; extraversion, agreeableness, openness, conscientiousness, and neuroticism. Results interpret the direct relationship of the personality trait with the use of FB and it has shown increased impact on student’s personality over the progress of the degree program. Students of all four universities came up with almost identical results that depict the similar role of an institute in the development of the student’s personality. Research findings contribute towards data science by proving analytical data on student’s personality traits. Also, it acts as guide for educational institutes to deeply cope with targete

Personality development of an individual is a continuous process that involves multiple methods and techniques. Study on a person’s personality is conducted by examining inherited and adopted personality traits during their life span. Higher education institutes play a vital role in flourishing student’s personality and significant revolution is noticed by the researchers in this regard. Individual’s personality is being affected by use of social media including Facebook, Twitter, LinkedIn and many more. Facebook (FB) is widely used as an online social network to communicate with other individuals. It is observed that students are also users of FB for learning, interacting, recreating and browsing purposes. Our research falls in the same dimension to study student’s personality traits through their FB use and eventually analyze the change in their personality. For the same, four higher education institutes of Pakistan located in Lahore are targeted. Data is collected for five years from under graduate students from 2015-2019. It has two main purpose; firstly, to present analytical results and secondly, to examine the changes in person’s personality traits (PT) during degree program at the university. Data is collected from FB profiles through an application and Big Five Personality Trait model is used to conduct personality assessment. Five PT are studied namely; extraversion, agreeableness, openness, conscientiousness, and neuroticism. Results interpret the direct relationship of the personality trait with the use of FB and it has shown increased impact on student’s personality over the progress of the degree program. Students of all four universities came up with almost identical results that depict the similar role of an institute in the development of the student’s personality. Research findings contribute towards data science by proving analytical data on student’s personality traits. Also, it acts as guide for educational institutes to deeply cope with targete

Fiverr.com is one of the biggest freelancing websites in the world. It provides a platform to clients and freelancers. In Fiverr, buyers pay in advance for the “gigs” created by sellers. A “gig” on Fiverr shows a specific service or set of skills of any type of freelance service, ranging from copywriting to social media marketing, to web design. This website is used by all types of users. Many people face problems regarding its interface. For many novice and intermediate users, this website is complicated. In this study, SUS is used to measure the usability of the Fiverr.com website. The main focus of this paper will stay on the SUS survey done on Google forms after performing some tasks. Results from the Google forms survey will be used to get the usability of this website either people are satisfied with the website or not. Usability evaluation of Fiverr is conducted by evaluating it with many parameters like efficiency, effectiveness, and the satisfaction of users. Another objective of this research is to improve the interface of this website. A system usability scale (SUS) is a quick and effective approach to evaluate the usability of a product. It is a low-expense usability scale that can be employed for the assessment of interactive systems. The SUS questionnaire is widely used to measure the usability of e-commerce websites. In this study, novice, intermediate and expert users, a total of 30 participant’s feedback was combined to improve its design. Results from Google forms show a 63.3 mean SUS value. It shows that the Fiverr.com website has minor issues that need to be resolved. If these problems are resolved it will help new clients and sellers in the future.

Fiverr.com is one of the biggest freelancing websites in the world. It provides a platform to clients and freelancers. In Fiverr, buyers pay in advance for the “gigs” created by sellers. A “gig” on Fiverr shows a specific service or set of skills of any type of freelance service, ranging from copywriting to social media marketing, to web design. This website is used by all types of users. Many people face problems regarding its interface. For many novice and intermediate users, this website is complicated. In this study, SUS is used to measure the usability of the Fiverr.com website. The main focus of this paper will stay on the SUS survey done on Google forms after performing some tasks. Results from the Google forms survey will be used to get the usability of this website either people are satisfied with the website or not. Usability evaluation of Fiverr is conducted by evaluating it with many parameters like efficiency, effectiveness, and the satisfaction of users. Another objective of this research is to improve the interface of this website. A system usability scale (SUS) is a quick and effective approach to evaluate the usability of a product. It is a low-expense usability scale that can be employed for the assessment of interactive systems. The SUS questionnaire is widely used to measure the usability of e-commerce websites. In this study, novice, intermediate and expert users, a total of 30 participant’s feedback was combined to improve its design. Results from Google forms show a 63.3 mean SUS value. It shows that the Fiverr.com website has minor issues that need to be resolved. If these problems are resolved it will help new clients and sellers in the future.

Fiverr.com is one of the biggest freelancing websites in the world. It provides a platform to clients and freelancers. In Fiverr, buyers pay in advance for the “gigs” created by sellers. A “gig” on Fiverr shows a specific service or set of skills of any type of freelance service, ranging from copywriting to social media marketing, to web design. This website is used by all types of users. Many people face problems regarding its interface. For many novice and intermediate users, this website is complicated. In this study, SUS is used to measure the usability of the Fiverr.com website. The main focus of this paper will stay on the SUS survey done on Google forms after performing some tasks. Results from the Google forms survey will be used to get the usability of this website either people are satisfied with the website or not. Usability evaluation of Fiverr is conducted by evaluating it with many parameters like efficiency, effectiveness, and the satisfaction of users. Another objective of this research is to improve the interface of this website. A system usability scale (SUS) is a quick and effective approach to evaluate the usability of a product. It is a low-expense usability scale that can be employed for the assessment of interactive systems. The SUS questionnaire is widely used to measure the usability of e-commerce websites. In this study, novice, intermediate and expert users, a total of 30 participant’s feedback was combined to improve its design. Results from Google forms show a 63.3 mean SUS value. It shows that the Fiverr.com website has minor issues that need to be resolved. If these problems are resolved it will help new clients and sellers in the future.

Personality development of an individual is a continuous process that involves multiple methods and techniques. Study on a person’s personality is conducted by examining inherited and adopted personality traits during their life span. Higher education institutes play a vital role in flourishing student’s personality and significant revolution is noticed by the researchers in this regard. Individual’s personality is being affected by use of social media including Facebook, Twitter, LinkedIn and many more. Facebook (FB) is widely used as an online social network to communicate with other individuals. It is observed that students are also users of FB for learning, interacting, recreating and browsing purposes. Our research falls in the same dimension to study student’s personality traits through their FB use and eventually analyze the change in their personality. For the same, four higher education institutes of Pakistan located in Lahore are targeted. Data is collected for five years from under graduate students from 2015-2019. It has two main purpose; firstly, to present analytical results and secondly, to examine the changes in person’s personality traits (PT) during degree program at the university. Data is collected from FB profiles through an application and Big Five Personality Trait model is used to conduct personality assessment. Five PT are studied namely; extraversion, agreeableness, openness, conscientiousness, and neuroticism. Results interpret the direct relationship of the personality trait with the use of FB and it has shown increased impact on student’s personality over the progress of the degree program. Students of all four universities came up with almost identical results that depict the similar role of an institute in the development of the student’s personality. Research findings contribute towards data science by proving analytical data on student’s personality traits. Also, it acts as guide for educational institutes to deeply cope with targete

There is ample evidence in the automotive cybersecurity literature that the car brake ECUs can be maliciously reprogrammed. Motivated by such threat, this paper investigates the capabilities of an adversary who can directly control the frictional brake actuators and would like to induce wheel lockup conditions leading to catastrophic road injuries. This paper demonstrates that the adversary despite having a limited knowledge of the tire-road interaction characteristics has the capability of driving the states of the vehicle traction dynamics to a vicinity of the lockup manifold in a finite time by means of a properly designed attack policy for the frictional brakes. This attack policy relies on employing a predefined-time controller and a nonlinear disturbance observer acting on the wheel slip error dynamics. Simulations under various road conditions demonstrate the effectiveness of the proposed attack policy., Comment: Submitted to AutoSec'22@NDSS

Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, a novel spatial estimation for beamforming is proposed to address UAV-based joint sensing and communications (JSC). The proposed spatial estimation algorithm relies on using a delay tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results., Comment: 9 pages, 10 figures, under review in IEEE Sensors Journal

The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry. Because of its simplicity in design, it lacks most of the requirements needed for a security-proven communication protocol. However, a safe and secured environment is imperative for autonomous as well as connected vehicles. Therefore CAN security is considered one of the important topics in the automotive research community. In this paper, we propose a four-stage intrusion detection system that uses the chi-squared method and can detect any kind of strong and weak cyber attacks in a CAN. This work is the first-ever graph-based defense system proposed for the CAN. Our experimental results show that we have a very low 5.26% misclassification for denial of service (DoS) attack, 10% misclassification for fuzzy attack, 4.76% misclassification for replay attack, and no misclassification for spoofing attack. In addition, the proposed methodology exhibits up to 13.73% better accuracy compared to existing ID sequence-based methods., Comment: This paper is accepted to IEEE Transactions on Intelligent Transportation Systems for publication

The evolution of modern voice controlled devices (VCDs) in recent years has revolutionized the Internet of Things, and resulted in increased realization of smart homes, personalization and home automation through voice commands. The introduction of VCDs in IoT is expected to give emergence of new subfield of IoT, called Multimedia of Thing (MoT). These VCDs can be exploited in IoT driven environment to generate various spoofing attacks including the replays. Replay attacks are generated through replaying the recorded audio of legitimate human speaker with the intent of deceiving the VCDs having speaker verification system. The connectivity among the VCDs can easily be exploited in IoT driven environment to generate a chain of replay attacks (multi-order replay attacks). Existing spoofing detection datasets like ASVspoof and ReMASC contain only the first-order replay recordings against the bonafide audio samples. These datasets can not offer evaluation of the anti-spoofing algorithms capable of detecting the multi-order replay attacks. Additionally, these datasets do not capture the characteristics of microphone arrays, which is an important characteristic of modern VCDs. We need a diverse replay spoofing detection corpus that consists of multi-order replay recordings against the bonafide voice samples. This paper presents a novel voice spoofing detection corpus (VSDC) to evaluate the performance of multi-order replay anti-spoofing methods. The proposed VSDC consists of first and second-order-replay samples against the bonafide audio recordings. Additionally, the proposed VSDC can also be used to evaluate the performance of speaker verification systems as our corpus includes the audio samples of fifteen different speakers. To the best of our knowledge, this is the first publicly available replay spoofing detection corpus comprising of first-order and second-order-replay samples.

Fake audio detection is expected to become an important research area in the field of smart speakers such as Google Home, Amazon Echo and chatbots developed for these platforms. This paper presents replay attack vulnerability of voice-driven interfaces and proposes a countermeasure to detect replay attack on these platforms. This paper presents a novel framework to model replay attack distortion, and then use a non-learning-based method for replay attack detection on smart speakers. The reply attack distortion is modeled as a higher-order nonlinearity in the replay attack audio. Higher-order spectral analysis (HOSA) is used to capture characteristics distortions in the replay audio. Effectiveness of the proposed countermeasure scheme is evaluated on original speech as well as corresponding replayed recordings. The replay attack recordings are successfully injected into the Google Home device via Amazon Alexa using the drop-in conferencing feature., Comment: 6 pages, IEEE 2nd International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR 2019), March 28-30, 2019, San Jose, CA, USA

Voice cloning technologies have found applications in a variety of areas ranging from personalized speech interfaces to advertisement, robotics, and so on. Existing voice cloning systems are capable of learning speaker characteristics and use trained models to synthesize a person's voice from only a few audio samples. Advances in cloned speech generation technologies are capable of generating perceptually indistinguishable speech from a bona-fide speech. These advances pose new security and privacy threats to voice-driven interfaces and speech-based access control systems. The state-of-the-art speech synthesis technologies use trained or tuned generative models for cloned speech generation. Trained generative models rely on linear operations, learned weights, and excitation source for cloned speech synthesis. These systems leave characteristic artifacts in the synthesized speech. Higher-order spectral analysis is used to capture differentiating attributes between bona-fide and cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated bicoherence, Gaussianity test statistics, and linearity test statistics are used to capture generative model artifacts. Performance of the proposed method is evaluated on cloned audios generated using speaker adaptation- and speaker encoding-based approaches. Experimental results for a dataset consisting of 126 cloned speech and 8 bona-fide speech samples indicate that the proposed method is capable of detecting bona-fide and cloned audios with close to a perfect detection rate., Comment: 6 pages, The 2nd IEEE International Workshop on "Fake MultiMedia" (FakeMM'19) March 28-30, 2019, San Jose, CA, USA

Nowadays with the help of advanced technology, modern vehicles are not only made up of mechanical devices but also consist of highly complex electronic devices and connections to the outside world. There are around 70 Electronic Control Units (ECUs) in modern vehicle which are communicating with each other over the standard communication protocol known as Controller Area Network (CAN-Bus) that provides the communication rate up to 1Mbps. There are different types of in-vehicle network protocol and bus system namely Controlled Area Network (CAN), Local Interconnected Network (LIN), Media Oriented System Transport (MOST), and FlexRay. Even though CAN-Bus is considered as de-facto standard for in-vehicle network communication, it inherently lacks the fundamental security features by design like message authentication. This security limitation has paved the way for adversaries to penetrate into the vehicle network and do malicious activities which can pose a dangerous situation for both driver and passengers. In particular, nowadays vehicular networks are not only closed systems, but also they are open to different external interfaces namely Bluetooth, GPS, to the outside world. Therefore, it creates new opportunities for attackers to remotely take full control of the vehicle. The objective of this research is to survey the current limitations of CAN-Bus protocol in terms of secure communication and different solutions that researchers in the society of automotive have provided to overcome the CAN-Bus limitation on different layers.

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time- and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively., Comment: 6 pages, 5 figures, 9th IEEE International Workshop on Information Forensics and Security (WIFS 2017

Audio splicing is one of the most common manipulation techniques in the area of audio forensics. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are proposed as the environmental signature. Specifically, the spliced audio segments are detected according to the magnitude correlation between the query frames and reference frames via a statically optimal threshold. The detection accuracy is further refined by comparing the adjacent frames. The effectiveness of the proposed method is tested on two data sets. One is generated from TIMIT database, and the other one is made in four acoustic environments using a commercial grade microphones. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments with near perfect accuracy. Comparison results illustrate that the identification accuracy of the proposed scheme is higher than the previous schemes. In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack, which is also superior to the previous works., Comment: Submitted to IEEE Transactions on Information Forensics and Security