Biblio

To better protect users' privacy, various authentication mechanisms have been applied on smartphones. Android pattern lock has been widely used because it is easy to memorize, however, simple ones are more vulnerable to attack such as shoulder surfing attack. In this paper, we propose a security rating evaluation scheme based on pattern lock. In particular, an entropy function of a pattern lock can be calculated, which is decided by five kinds of attributes: size, length, angle, overlap and intersection for quantitative evaluation of pattern lock. And thus, the security rating thresholds will be determined by the distribution of entropy values. Finally, we design and develop an APP based on Android Studio, which is used to verify the effectiveness of our proposed security rating evaluation scheme.

Language-based information-flow control (IFC) techniques often rely on special purpose, ad-hoc primitives to address different covert channels that originate in the runtime system, beyond the scope of language constructs. Since these piecemeal solutions may not compose securely, there is a need for a unified mechanism to control covert channels. As a first step towards this goal, we argue for the design of a general interface that allows programs to safely interact with the runtime system and the available computing resources. To coordinate the communication between programs and the runtime system, we propose the use of asynchronous exceptions (interrupts), which, to the best of our knowledge, have not been considered before in the context of IFC languages. Since asynchronous exceptions can be raised at any point during execution-often due to the occurrence of an external event-threads must temporarily mask them out when manipulating locks and shared data structures to avoid deadlocks and, therefore, breaking program invariants. Crucially, the naive combination of asynchronous exceptions with existing features of IFC languages (e.g., concurrency and synchronization variables) may open up new possibilities of information leakage. In this paper, we present MACasync, a concurrent, statically enforced IFC language that, as a novelty, features asynchronous exceptions. We show how asynchronous exceptions easily enable (out of the box) useful programming patterns like speculative execution and some degree of resource management. We prove that programs in MACasync satisfy progress-sensitive non-interference and mechanize our formal claims in the Agda proof assistant.

The paper proposes a novel technique of EEG induced Brain-Computer Interface system for user authentication of personal devices. The scheme enables a human user to lock and unlock any personal device using his/her mind generated password. A two stage security verification is employed in the scheme. In the first stage, a 3 × 3 spatial matrix of flickering circles will appear on the screen of which, rows are blinked randomly and user has to mentally select a row which contains his desired circle.P300 is released when the desired row is blinked. Successful selection of row is followed by the selection of a flickering circle in the desired row. Gazing at a particular flickering circle generates SSVEP brain pattern which is decoded to trace the mentally selected circle. User is able to store mentally uttered number in the selected circle, later the number with it's spatial position will serve as the password for the unlocking phase. Here, the user is equipped with a headphone where numbers starting from zero to nine are spelled randomly. Spelled number matching with the mentally uttered number generates auditory P300 in the subject's brain. The particular choice of mentally uttered number is detected by successful detection of auditory P300. A novel weight update algorithm of Recurrent Neural Network (RNN), based on Extended-Kalman Filter and Particle Filter is used here for classifying the brain pattern. The proposed classifier achieves the best classification accuracy of 95.6%, 86.5% and 83.5% for SSVEP, visual P300 and auditory P300 respectively.

Technological advancements have made smartphones to provide wide range of applications that enable users to perform many of their tasks easily and conveniently, anytime and anywhere. For this reason, many users are tend to store their private data in their smart phones. Since conventional methods for security of smartphones, such as passwords, personal identification numbers, and pattern locks are prone to many attacks, this research paper proposes a novel method for authenticating smartphone users based on performing seven different daily physical activity as behavioral biometrics, using smartphone embedded sensor data. This authentication scheme builds a machine learning model which recognizes users by performing those daily activities. Experimental results demonstrate the effectiveness of the proposed framework.

Logic encryption is a powerful hardware protection technique that uses extra key inputs to lock a circuit from piracy or unauthorized use. The recent discovery of the SAT-based attack with Distinguishing Input Pattern (DIP) generation has rendered all traditional logic encryptions vulnerable, and thus the creation of new encryption methods. However, a critical question for any new encryption method is whether security against the DIP-generation attack means security against all other attacks. In this paper, a new high-level SAT-based attack called SigAttack has been discovered and thoroughly investigated. It is based on extracting a key-revealing signature in the encryption. A majority of all known SAT-resilient encryptions are shown to be vulnerable to SigAttack. By formulating the condition under which SigAttack is effective, the paper also provides guidance for the future logic encryption design.

Graphical passwords are most widely used as a mechanism for authentication in today's mobile computing environment. This methodology was introduced to enhance security element and overcome the vulnerabilities of textual passwords, pins, or other trivial password methodologies which were difficult to remember and prone to external attacks. There are many graphical password schemes that are proposed over time, however, most of them suffer from shoulder surfing and could be easily guessed which is quite a big problem. The proposed technique in this paper allows the user to keep the ease-to-use property of the pattern lock while minimizing the risk of shoulder surfing and password guessing. The proposed technique allows the user to divide a picture into multiple chunks and while unlocking, selecting the previously defined chunks results successfully in unlocking the device. This technique can effectively resist the shoulder surfing and smudge attacks, also it is resilient to password guessing or dictionary attacks. The proposed methodology can significantly improve the security of the graphical password system with no cost increase in terms of unlocking time.

In the last decade, the adoption of wireless systems has increased. These systems allow multiple devices to send data wirelessly using radio waves. Moreover, in some applications, authentication is done wirelessly by exchanging authentication data over the air as in wireless locks and keyless entry systems. On the other hand, most of the wireless devices today can control the radio frequency transmission power to optimize the system's performance and minimize interference. In this paper, we explore the possibility of modulating the radio frequency transmission power in wireless systems for authentication purposes and using it for source authentication. Furthermore, we propose two system models that perform authentication using variable power transmission patterns. Then, we discuss possible applications. Finally, we implement and test a prototype system using IEEE 802.11 (Wi-Fi) devices.

Physical Unclonable Functions (PUFs) are considered as an attractive low-cost security anchor. The unique features of PUFs are dependent on the Nanoscale variations introduced during the manufacturing variations. Most PUFs exhibit an unreliability problem due to aging and inherent sensitivity to the environmental conditions. As a remedy to the reliability issue, helper data algorithms are used in practice. A helper data algorithm generates and stores the helper data in the enrollment phase in a secure environment. The generated helper data are used then for error correction, which can transform the unique feature of PUFs into a reproducible key. The key can be used to encrypt secret data in the security scheme. In contrast, this work shows that the fuzzy PUFs can be used to secret important data directly by an error-tolerant protocol without the enrollment phase and error-correction algorithm. In our proposal, the secret data is locked in a vault leveraging the unique fuzzy pattern of PUF. Although the noise exists, the data can then be released only by this unique PUF. The evaluation was performed on the most prominent intrinsic PUF - DRAM PUF. The test results demonstrate that our proposal can reach an acceptable reconstruction rate in various environment. Finally, the security analysis of the new proposal is discussed.

Today, Smartphone is a necessary device for people connected to the Internet world. But user privacy and security are still playing important roles in the usage of mobile devices. The user was asked to enter related characters, numbers or drawing a simple graphic on the touch screen as passwords for unlocking the screensaver. Although it could provide the user with a simple and convenient security authentication mechanism, the process is hard to protect against the privacy information leakage under the strict security policy. Nowadays, various keypad lock screen Apps usually provides different type of schemes in unlocking the mobile device screen, such as simple-customized pattern, swipe-to-unlock with a static image and so on. But the vulnerability could provide a chance to hijacker to find out the leakage of graphic pattern information that influences in user information privacy and security.This paper proposes a new graphic pattern authentication mechanism to enhance the strength of that in the keypad lock screen Apps. It integrates random digital graphics and handwriting graphic input track recognition technologies to provide better and more diverse privacy protection and reduce the risk of vulnerability. The proposed mechanism is based on two factor identification scheme. First of all, it randomly changes digital graphic position based on unique passwords every time to increase the difficulty of the stealer's recording. Second, the input track of handwriting graphics is another identification factor for enhancing the complex strength of user authentication as well.

To manage and handle the issues of physical security in the modern world, there is a dire need for a multilevel security system to ensure the safety of precious belongings that could be money, military equipment or medical life-saving drugs. Security locker solution is proposed which is a multiple layer security system consisting of various levels of authentication. In most cases, only relevant persons should have access to their precious belongings. The unlocking of the box is only possible when all of the security levels are successfully cleared. The five levels of security include entering of password on interactive GUI, thumbprint, facial recognition, speech pattern recognition, and vein pattern recognition. This project is unique and effective in a sense that it incorporates five levels of security in a single prototype with the use of cost-effective equipment. Assessing our security system, it is seen that security is increased many a fold as it is near to impossible to breach all these five levels of security. The Raspberry Pi microcomputers, handling all the traits efficiently and smartly makes it easy for performing all the verification tasks. The traits used involves checking, training and verifying processes with application of machine learning operations.

This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.

In most produced modern vehicles, Passive Keyless Entry and Start System (PKES), a newer form of an entry access system, is becoming more and more popular. The PKES system allows the consumer to enter within a certain range and have the vehicle's doors unlock automatically without pressing any buttons on the key. This technology increases the overall convenience to the consumer; however, it is vulnerable to attacks known as relay and amplified relay attacks. A relay attack consists of placing a device near the vehicle and a device near the key to relay the signal between the key and the vehicle. On the other hand, an amplified relay attack uses only a singular amplifier to increase the range of the vehicle sensors to reach the key. By exploiting these two different vulnerabilities within the PKES system, an attacker can gain unauthorized access to the vehicle, leading to damage or even stolen property. To minimize both vulnerabilities, we propose a coordinate tracing system with an additional Bluetooth communication channel. The coordinate tracing system, or PKES Forcefield, traces the authorized key's longitude and latitude in real time using two proposed algorithms, known as the Key Bearing algorithm and the Longitude and Latitude Key (LLK) algorithm. To further add security, a Bluetooth communication channel will be implemented. With an additional channel established, a second frequency can be traced within a secondary PKES Forcefield. The LLK Algorithm computes both locations of frequencies and analyzes the results to form a pattern. Furthermore, the PKES Forcefield movement-tracing allows a vehicle to understand when an attacker attempts to transmit an unauthenticated signal and blocks any signal from being amplified over a fixed range.

Fourier domain mode locked (FDML) lasers, in which the sweep period of the swept bandpass filter is synchronized with the roundtrip time of the optical field, are broadband and rapidly tunable fiber ring laser systems, which offer rich dynamics. A detailed understanding is important from a fundamental point of view, and also required in order to improve current FDML lasers which have not reached their coherence limit yet. Here, we study the formation of localized patterns in the intensity trace of FDML laser systems based on a master equation approach [1] derived from the nonlinear Schrödinger equation for polarization maintaining setups, which shows excellent agreement with experimental data. A variety of localized patterns and chaotic or bistable operation modes were previously discovered in [2–4] by investigating primarily quasi-static regimes within a narrow sweep bandwidth where a delay differential equation model was used. In particular, the formation of so-called holes which are characterized by a dip in the intensity trace and a rapid phase jump are described. Such holes have tentatively been associated with Nozaki-Bekki holes which are solutions to the complex Ginzburg-Landau equation. In Fig. 1 (b) to (d) small sections of a numerical solution of our master equation are presented for a partially dispersion compensated polarization maintaining FDML laser setup. Within our approach, we are able to study the full sweep dynamics over a broad sweep range of more than 100 nm. This allows us to identify different co-existing intensity patterns within a single sweep. In general, high frequency distortions in the intensity trace of FDML lasers [5] are mainly caused by synchronization mismatches caused by the fiber dispersion or a detuning of the roundtrip time of the optical field to the sweep period of the swept bandpass filter. This timing errors lead to rich and complex dynamics over many roundtrips and are a major source of noise, greatly affecting imaging and sensing applications. For example, the imaging quality in optical coherence tomography where FDML lasers are superior sources is significantly reduced [5].

This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.

Graph pattern matching in network topologies is a building block of many distributed algorithms. Based on a limited local view of the topology, pattern-based algorithms substantiate the decision-making of each device on the occurrence of graph patterns in its surrounding topology. Existing pattern-based algorithms require that each device has a sufficiently large local view to match patterns without support of other devices. In practical environments, the local view is often restricted to one hop. Thus, algorithms matching non-trivial patterns are locked out from such environments today. This paper presents the first algorithm for distributed topology pattern matching, enabling pattern matching beyond the local view. Outgoing from initiating devices, our pattern matcher delegates the matching procedure to further devices in the network. Exploring major contextual parameters of our algorithm, we show that the optimal local view size depends on scenario-specific conditions. Our pattern matcher provides the flexibility for adaptations of the local view size at runtime. Making use of this flexibility, we optimize the execution of an established pattern-based algorithm and evaluate our pattern matcher in two topology control case studies for the Internet of Things. By scaling the view size of each device in a distributed way, our adaptive approach achieves significant communication cost savings in face of dynamic conditions.

Data races are often hard to detect in device drivers, due to the non-determinism of concurrent execution. According to our study of Linux driver patches that fix data races, more than 38% of patches involve a pattern that we call inconsistent lock protection. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held around each access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a data race may occur.In this paper, we present a runtime analysis approach, named DILP, to detect data races caused by inconsistent lock protection in device drivers. By monitoring driver execution, DILP collects the information about runtime variable accesses and executed functions. Then after driver execution, DILP analyzes the collected information to detect and report data races caused by inconsistent lock protection. We evaluate DILP on 12 device drivers in Linux 4.16.9, and find 25 real data races.

User's behavioral biometrics are promising as authentication factors in particular if accuracy is sufficiently guaranteed. They can be used to augment security in combination with other authentication factors. A gesture-based pattern lock system is a good example of such multi-factor authentication, using touch dynamics in a smartphone. However, touch dynamics can be significantly affected by a shape of gestures with regard to the performance and accuracy, and our concern is that user-chosen patterns are likely far from producing such a good shape of gestures. In this poster, we raise this problem and show our experimental study conducted in this regard. We investigate if there is a reproducible correlation between shape and accuracy and if we can derive effective attribute values for user guidance, based on the gesture-based pattern lock system. In more general, we discuss a guided approach to behavioral authentication.

Pattern lock has been widely used for authentication to protect user privacy on mobile devices (e.g., smartphones and tablets). Several attacks have been constructed to crack the lock. However, these approaches require the attackers to be either physically close to the target device or able to manipulate the network facilities (e.g., wifi hotspots) used by the victims. Therefore, the effectiveness of the attacks is highly sensitive to the setting of the environment where the users use the mobile devices. Also, these attacks are not scalable since they cannot easily infer patterns of a large number of users. Motivated by an observation that fingertip motions on the screen of a mobile device can be captured by analyzing surrounding acoustic signals on it, we propose PatternListener, a novel acoustic attack that cracks pattern lock by leveraging and analyzing imperceptible acoustic signals reflected by the fingertip. It leverages speakers and microphones of the victim's device to play imperceptible audio and record the acoustic signals reflected from the fingertip. In particular, it infers each unlock pattern by analyzing individual lines that are the trajectories of the fingertip and composed of the pattern. We propose several algorithms to construct signal segments for each line and infer possible candidates of each individual line according to the signal segments. Finally, we produce a tree to map all line candidates into grid patterns and thereby obtain the candidates of the entire unlock pattern. We implement a PatternListener prototype by using off-the-shelf smartphones and thoroughly evaluate it using 130 unique patterns. The real experimental results demonstrate that PatternListener can successfully exploit over 90% patterns in five attempts.

Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.

Brain Computer Interface (BCI) aims at providing a better quality of life to people suffering from neuromuscular disability. This paper establishes a BCI paradigm to provide a biometric security option, used for locking and unlocking personal computers or mobile phones. Although it is primarily meant for the people with neurological disorder, its application can safely be extended for the use of normal people. The proposed scheme decodes the electroencephalogram signals liberated by the brain of the subjects, when they are engaged in selecting a sequence of dots in(6×6)2-dimensional array, representing a pattern lock. The subject, while selecting the right dot in a row, would yield a P300 signal, which is decoded later by the brain-computer interface system to understand the subject's intention. In case the right dots in all the 6 rows are correctly selected, the subject would yield P300 signals six times, which on being decoded by a BCI system would allow the subject to access the system. Because of intra-subjective variation in the amplitude and wave-shape of the P300 signal, a type 2 fuzzy classifier has been employed to classify the presence/absence of the P300 signal in the desired window. A comparison of performances of the proposed classifier with others is also included. The functionality of the proposed system has been validated using the training instances generated for 30 subjects. Experimental results confirm that the classification accuracy for the present scheme is above 90% irrespective of subjects.

This paper presents a possible implementation of progressive authentication using the Android pattern lock. Our key idea is to use one pattern for two access levels to the device; an abridged pattern is used to access generic applications and a second, extended and higher-complexity pattern is used less frequently to access more sensitive applications. We conducted a user study of 89 participants and a consecutive user survey on those participants to investigate the usability of such a pattern scheme. Data from our prototype showed that for unlocking lowsecurity applications the median unlock times for users of the multiple pattern scheme and conventional pattern scheme were 2824 ms and 5589 ms respectively, and the distributions in the two groups differed significantly (Mann-Whitney U test, p-value less than 0.05, two-tailed). From our user survey, we did not find statistically significant differences between the two groups for their qualitative responses regarding usability and security (t-test, p-value greater than 0.05, two-tailed), but the groups did not differ by more than one satisfaction rating at 90% confidence.

Ransomware, as a specialized form of malicious software, has recently emerged as a major threat in computer security. With an ability to lock out user access to their content, recent ransomware attacks have caused severe impact at an individual and organizational level. While research in malware detection can be adapted directly for ransomware, specific structural properties of ransomware can further improve the quality of detection. In this paper, we adapt the deep learning methods used in malware detection for detecting ransomware from emulation sequences. We present specialized recurrent neural networks for capturing local event patterns in ransomware sequences using the concept of attention mechanisms. We demonstrate the performance of enhanced LSTM models on a sequence dataset derived by the emulation of ransomware executables targeting the Windows environment.

The goal of this work is to enable user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. We propose VibWrite that extends finger-input authentication beyond touch screens to any solid surface for smart access systems (e.g., access to apartments, vehicles or smart appliances). It integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. VibWrite builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. It is significantly different from traditional password-based approaches, which only authenticate the password itself rather than the legitimate user, and the behavioral biometrics-based solutions, which usually involve specific or expensive hardware (e.g., touch screen or fingerprint reader), incurring privacy concerns and suffering from smudge attacks. VibWrite is based on new algorithms to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics such as contacting area, touching force, and etc. VibWrite is implemented using a single pair of low-cost vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a desk or an appliance). Our extensive experiments demonstrate that VibWrite can authenticate users with high accuracy (e.g., over 95% within two trials), low false positive rate (e.g., less 3%) and is robust to various types of attacks.

With the steady increase of offered cloud storage services, they became a popular alternative to local storage systems. Beside several benefits, the usage of cloud storage services can offer, they have also some downsides like potential vendor lock-in or unavailability. Different pricing models, storage technologies and changing storage requirements are further complicating the selection of the best fitting storage solution. In this work, we present a heuristic optimization approach that optimizes the placement of data on cloud-based storage services in a redundant, cost- and latency-efficient way while considering user-defined Quality of Service requirements. The presented approach uses monitored data access patterns to find the best fitting storage solution. Through extensive evaluations, we show that our approach saves up to 30% of the storage cost and reduces the upload and download times by up to 48% and 69% in comparison to a baseline that follows a state-of-the-art approach.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.