Biblio

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.

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.

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.

In this work, a new human-computer assistive technology gadget designed for people with impairments is evaluated. The developed human-in-the-loop interface device has an embedded assistance controller and can replace the traditional mouse, gamepad and keyboard, enabling human-computer hands-free full access. This work is concerned with the assistive device performance characterization aspects. Based on the experiments carried out, the human-computer performance improvement with the embedded controller is analysed in detail. Results show that adding the human-in-the-loop assistance controller improves human-computer hands-free skills, which is an innovative contribution for the replacement of computer interfaces that depend on the human hands.

Image hash regimes have been widely used for authenticating content, recovery of images and digital forensics. In this article we propose a new algorithm for image haunting (SSL) with the most stable key points and regional features, strong against various manipulation of content conservation, including multiple combinatorial manipulations. In order to extract most stable keypoint, the proposed algorithm combines the Speed Up Robust Features (SURF) with Saliency detection. The keyboards and characteristics of the local area are then combined in a hash vector. There is also a sperate secret key that is randomly given for the hash vector to prevent an attacker from shaping the image and the new hash value. The proposed hacking algorithm shows that similar or initial images, which have been individually manipulated, combined and even multiple manipulated contents, can be visently identified by experimental result. The probability of collision between hacks of various images is almost nil. Furthermore, the key-dependent security assessment shows the proposed regime safe to allow an attacker without knowing the secret key not to forge or estimate the right havoc value.

Multi-Factor Authentication is an electronic authentication method in which a computer user is granted access to an application or a website only after successfully presenting two or more factors, or pieces of evidence. It is the first step to protect systems against intruders since the traditional log-in methods (username and password) are not completely protected from hackers, since they can guess them easily using tools. Current Systems use additional methods to increase security, such as using two-factor authentication based on a one-time password via mobile or email, or authentication based on biometrics (fingerprint, eye iris or retina, and face recognition) or via token devices. However, these methods require additional hardware equipment with high cost at the level of small and medium companies. This paper proposes a multi-factor authentication system that combines ease of use and low-cost factors. The system does not need any special settings or infrastructure. It relies on graphical passwords, so the user, in registration phase, chooses three images and memorizes them. In the login phase, the user needs only to choose the correct images that he considered during the registration process in a specific order. The proposed system overcomes many different security threats, such as key-loggers, screen capture attack or shoulder surfing. The proposed method was applied to 170 participants, 75% of them are males and 25% are females, classified according to their age, education level, web experience. One-third of them did not have sufficient knowledge about various security threats.

Side-channel attacks occur on smartphone keystrokes, where the input can be intercepted by a tapping sound. Ilia et al. reported that keystrokes can be predicted with 61% accuracy from tapping sounds listened to by the built-in microphone of a legitimate user's device. Li et al. reported that by emitting sonar sounds from an attacker smartphone's built-in speaker and analyzing the reflected waves from a legitimate user's finger at the time of tap input, keystrokes can be estimated with 90% accuracy. However, the method proposed by Ilia et al. requires prior penetration of the target smartphone and the attack scenario lacks plausibility; if the attacker's smartphone can be penetrated, the keylogger can directly acquire the keystrokes of a legitimate user. In addition, the method proposed by Li et al. is a side-channel attack in which the attacker actively interferes with the terminals of legitimate users and can be described as an active attack scenario. Herein, we analyze the extent to which a user's keystrokes are leaked to the attacker in a passive attack scenario, where the attacker wiretaps the sounds of the legitimate user's keystrokes using an external microphone. First, we limited the keystrokes to the personal identification number input. Subsequently, mel-frequency cepstrum coefficients of tapping sound data were represented as image data. Consequently, we found that the input is discriminated with high accuracy using a convolutional neural network to estimate the key input.

Keystroke Inference has been a hot topic since it poses a severe threat to our privacy from typing. Existing learning-based Keystroke Inference suffers the domain adaptation problem because the training data (from attacker) and the test data (from victim) are generally collected in different environments. Recently, Optimal Transport (OT) is applied to address this problem, but suffers the “ground metric” limitation. In this work, we propose a novel method, OTDA, by incorporating Discriminant Analysis into OT through an iterative learning process to address the ground metric limitation. By embedding OTDA into a vibration-based Keystroke Inference platform, we conduct extensive studies about domain adaptation with different factors, such as people, keyboard position, etc.. Our experiment results show that OTDA can achieve significant performance improvement on classification accuracy, i.e., outperforming baseline by 15% to 30%, state-of-the-art OT and other domain adaptation methods by 10% to 20%.

The use of a very wide windows operating system is undeniably also followed by increasing attacks on the operating system. Universal Serial Bus (USB) is one of the mechanisms used by many people with plug and play functionality that is very easy to use, making data transfers fast and easy compared to other hardware. Some research shows that the Windows operating system has weaknesses so that it is often exploited by using various attacks and malware. There are various methods used to exploit the Windows operating system, one of them by using a USB device. By using a USB device, a criminal can plant a backdoor reverse shell to exploit the victim's computer just by connecting the USB device to the victim's computer without being noticed. This research was conducted by planting a reverse shell backdoor through a USB device to exploit the victim's device, especially the webcam and microphone device on the target computer. From 35 experiments that have been carried out, it was found that 83% of spying attacks using USB devices on the Windows operating system were successfully carried out.

Windows is one of the popular operating systems in use today, while Universal Serial Bus (USB) is one of the mechanisms used by many people with practical plug and play functions. USB has long been used as a vector of attacks on computers. One method of attack is Keylogger. The Keylogger can take advantage of existing vulnerabilities in the Windows 10 operating system attacks carried out in the form of recording computer keystroke activity without the victim knowing. In this research, an attack will be carried out by running a Powershell Script using BadUSB to be able to activate the Keylogger program. The script is embedded in the Arduino Pro Micro device. The results obtained in the Keyboard Injection Attack research using Arduino Pro Micro were successfully carried out with an average time needed to run the keylogger is 7.474 seconds with a computer connected to the internet. The results of the keylogger will be sent to the attacker via email.

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.

Nowadays, computers are used everywhere to carry out daily routine tasks. The input devices i.e. keyboard or mouse are used to feed input to computers. The surveillance of input devices is much important as monitoring the users logging activity. A keylogger also referred as a keystroke logger, is a software or hardware device which monitors every keystroke typed by a user. Keylogger runs in the background that user cannot identify its presence. It can be used as monitoring software for parents to keep an eye on children activity on computers and for the owner to monitor their employees. A keylogger (which can be either spyware or software) is a kind of surveillance software that has the ability to store every keystroke in a log file. It is very dangerous for those systems which use their system for daily transaction purpose i.e. Online Banking Systems. A keylogger is a tool, made to save all the keystroke generated through the machine which sanctions hackers to steal sensitive information without user's intention. Privileged also relies on the access for both implementation and placement by Kernel keylogger, the entire message transmitted from the keyboard drivers, while the programmer simply relies on kernel level facilities that interrupt. This certainly needs a large power and expertise for real and error-free execution. However, it has been observed that 90% of the current keyloggers are running in userspace so they do not need any permission for execution. Our aim is focused on detecting userspace keylogger. Our intention is to forbid userspace keylogger from stealing confidential data and information. For this purpose, we use a strategy which is clearly based on detection manner techniques for userspace keyloggers, an essential category of malware packages. We intend to achieve this goal by matching I/O of all processes with some simulated activity of the user, and we assert detection in case the two are highly correlated. The rationale behind this is that the more powerful stream of keystrokes, the more I/O operations are required by the keylogger to log the keystrokes into the file.

Modern operating systems for personal computers (including Linux, MAC, and Windows) provide user-level APIs for an application to access the I/O paths of another application. This design facilitates information sharing between applications, enabling applications such as screenshots. However, it also enables user-level malware to log a user's keystrokes or scrape a user's screen output. In this work, we explore a design called SwitchMan to protect a user's I/O paths against user-level malware attacks. SwitchMan assigns each user with two accounts: a regular one for normal operations and a protected one for inputting and outputting sensitive data. Each user account runs under a separate virtual terminal. Malware running under a user's regular account cannot access sensitive input/output under a user's protected account. At the heart of SwitchMan lies a secure protocol that enables automatic account switching when an application requires sensitive input/output from a user. Our performance evaluation shows that SwitchMan adds acceptable performance overhead. Our security and usability analysis suggests that SwitchMan achieves a better tradeoff between security and usability than existing solutions.

Using the keyboard LEDs to send data optically was proposed in 2002 by Loughry and Umphress [1] (Appendix A). In this paper we extensively explore this threat in the context of a modern cyber-attack with current hardware and optical equipment. In this type of attack, an advanced persistent threat (APT) uses the keyboard LEDs (Caps-Lock, Num-Lock and Scroll-Lock) to encode information and exfiltrate data from airgapped computers optically. Notably, this exfiltration channel is not monitored by existing data leakage prevention (DLP) systems. We examine this attack and its boundaries for today's keyboards with USB controllers and sensitive optical sensors. We also introduce smartphone and smartwatch cameras as components of malicious insider and 'evil maid' attacks. We provide the necessary scientific background on optical communication and the characteristics of modern USB keyboards at the hardware and software level, and present a transmission protocol and modulation schemes. We implement the exfiltration malware, discuss its design and implementation issues, and evaluate it with different types of keyboards. We also test various receivers, including light sensors, remote cameras, 'extreme' cameras, security cameras, and smartphone cameras. Our experiment shows that data can be leaked from air-gapped computers via the keyboard LEDs at a maximum bit rate of 3000 bit/sec per LED given a light sensor as a receiver, and more than 120 bit/sec if smartphones are used. The attack doesn't require any modification of the keyboard at hardware or firmware levels.

Many websites induce the browser to send network traffic in response to user input events. This includes websites with autocomplete, a popular feature on search engines that anticipates the user's query while they are typing. Websites with this functionality require HTTP requests to be made as the query input field changes, such as when the user presses a key. The browser responds to input events by generating network traffic to retrieve the search predictions. The traffic emitted by the client can expose the timings of keyboard input events which may lead to a keylogging side channel attack whereby the query is revealed through packet inter-arrival times. We investigate the feasibility of such an attack on several popular search engines by characterizing the behavior of each website and measuring information leakage at the network level. Three out of the five search engines we measure preserve the mutual information between keystrokes and timings to within 1% of what it is on the host. We describe the ways in which two search engines mitigate this vulnerability with minimal effects on usability.

Keystroke Dynamics is the study of typing patterns and rhythm for personal identification and traits. Keystrokes may be analysed as fixed text such as passwords or as continuous typed text such as documents. This paper reviews different classification metrics for continuous text, such as the A and R metrics, Canberra, Manhattan and Euclidean and introduces a variant of the Minkowski distance. To test the metrics, we adopted a substantial dataset containing 239 thousand records acquired under real, harsh, and unidealised conditions. We propose a new parameter for the Minkowski metric, and we reinforce another for the A metric, as initially stated by its authors.

Keystroke dynamics study the way in which users input text via their keyboards, which is unique to each individual, and can form a component of a behavioral biometric system to improve existing account security. Keystroke dynamics systems on free-text data use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Many algorithms require 500, 1,000, or more keystrokes to achieve EERs of below 10%. In this paper, we propose an instance-based graph comparison algorithm to reduce the number of keystrokes required to authenticate users. Commonly used features such as monographs and digraphs are investigated. Feature importance is determined and used to construct a fused classifier. Detection error tradeoff (DET) curves are produced with different numbers of keystrokes. The fused classifier outperforms the state-of-the-art with EERs of 7.9%, 5.7%, 3.4%, and 2.7% for test samples of 50, 100, 200, and 500 keystrokes.

One of the basic behavioural biometric methods is keystroke element. Being less expensive and not requiring any extra bit of equipment is the main advantage of keystroke element. The primary concentration of this paper is to give an inevitable review of behavioural biometrics strategies, measurements and different methodologies and difficulties and future bearings specially of keystroke analysis and mouse dynamics. Keystrokes elements frameworks utilize insights, e.g. time between keystrokes, word decisions, word mixes, general speed of writing and so on. Mouse Dynamics is termed as the course of actions captured from the moving mouse by an individual when interacting with a GUI. These are representative factors which may be called mouse dynamics signature of an individual, and may be used for verification of identity of an individual. In this paper, we compare the authentication system based on keystroke dynamics and mouse dynamics.

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.

Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.

The paper considers an issues of protecting data from unauthorized access by users' authentication through keystroke dynamics. It proposes to use keyboard pressure parameters in combination with time characteristics of keystrokes to identify a user. The authors designed a keyboard with special sensors that allow recording complementary parameters. The paper presents an estimation of the information value for these new characteristics and error probabilities of users' identification based on the perceptron algorithms, Bayes' rule and quadratic form networks. The best result is the following: 20 users are identified and the error rate is 0.6%.

Web identifiers such as usernames, hashtags, and domain names serve important roles in online navigation, communication, and community building. Therefore the entities that choose such names must ensure that end-users are able to quickly and accurately enter them in applications. Uniqueness requirements, a desire for short strings, and an absence of delimiters often constrain this name selection process. To gain perspective on the speed and correctness of name entry, we crowdsource the typing of 51,000+ web identifiers. Surface level analysis reveals, for example, that typing speed is generally a linear function of identifier length. Examining keystroke dynamics at finer granularity proves more interesting. First, we identify features predictive of typing time/accuracy, finding: (1) the commonality of character bi-grams inside a name, and (2) the degree of ambiguity when tokenizing a name - to be most indicative. A machine-learning model built over 10 such features exhibits moderate predictive capability. Second, we evaluate our hypothesis that users subconsciously insert pauses in their typing cadence where text delimiters (e.g., spaces) would exist, if permitted. The data generally supports this claim, suggesting its application alongside algorithmic tokenization methods, and possibly in name suggestion frameworks.

Free text keystroke dynamics is a behavioral biometric that has the strong potential to offer unobtrusive and continuous user authentication. Unfortunately, due to the limited data availability, free text keystroke dynamics have not been tested adequately. Based on a novel large dataset of free text keystrokes from our ongoing data collection using behavior in natural settings, we present the first study to evaluate keystroke dynamics while respecting the temporal order of the data. Specifically, we evaluate the performance of different ways of forming a test sample using sessions, as well as a form of continuous authentication that is based on a sliding window on the keystroke time series. Instead of accumulating a new test sample of keystrokes, we update the previous sample with keystrokes that occur in the immediate past sliding window of n minutes. We evaluate sliding windows of 1 to 5, 10, and 30 minutes. Our best performer using a sliding window of 1 minute, achieves an FAR of 1% and an FRR of 11.5%. Lastly, we evaluate the sensitivity of the keystroke dynamics algorithm to short quick insider attacks that last only several minutes, by artificially injecting different portions of impostor keystrokes into the genuine test samples. For example, the evaluated algorithm is found to be able to detect insider attacks that last 2.5 minutes or longer, with a probability of 98.4%.

In this paper, an innovative approach to keyboard user monitoring (authentication), using keyboard dynamics and founded on the concept of time series analysis, is presented. The work is motivated by the need for robust authentication mechanisms in the context of on-line assessment such as those featured in many online learning platforms. Four analysis mechanisms are considered: analysis of keystroke time series in their raw form (without any translation), analysis consequent to translating the time series into a more compact form using either the Discrete Fourier Transform or the Discrete Wavelet Transform, and a "benchmark" feature vector representation of the form typically used in previous related work. All four mechanisms are fully described and evaluated. A best authentication accuracy of 99% was obtained using the wavelet transform.

Third-party IME (Input Method Editor) apps are often the preference means of interaction for Android users' input. In this paper, we first discuss the insecurity of IME apps, including the Potentially Harmful Apps (PHA) and malicious IME apps, which may leak users' sensitive keystrokes. The current defense system, such as I-BOX, is vulnerable to the prefix-substitution attack and the colluding attack due to the post-IME nature. We provide a deeper understanding that all the designs with the post-IME nature are subject to the prefix-substitution and colluding attacks. To remedy the above post-IME system's flaws, we propose a new idea, pre-IME, which guarantees that "Is this touch event a sensitive keystroke?" analysis will always access user touch events prior to the execution of any IME app code. We designed an innovative TrustZone-based framework named IM-Visor which has the pre-IME nature. Specifically, IM-Visor creates the isolation environment named STIE as soon as a user intends to type on a soft keyboard, then the STIE intercepts, translates and analyzes the user's touch input. If the input is sensitive, the translation of keystrokes will be delivered to user apps through a trusted path. Otherwise, IM-Visor replays non-sensitive keystroke touch events for IME apps or replays non-keystroke touch events for other apps. A prototype of IM-Visor has been implemented and tested with several most popular IMEs. The experimental results show that IM-Visor has small runtime overheads.