Biblio

Despite the additional protection it affords, two-factor authentication (2FA) adoption reportedly remains low. To better understand 2FA adoption and its barriers, we observed the deployment of a 2FA system at Carnegie Mellon University (CMU). We explore user behaviors and opinions around adoption, surrounding a mandatory adoption deadline. Our results show that (a) 2FA adopters found it annoying, but fairly easy to use, and believed it made their accounts more secure; (b) experience with CMU Duo often led to positive perceptions, sometimes translating into 2FA adoption for other accounts; and, (c) the differences between users required to adopt 2FA and those who adopted voluntarily are smaller than expected. We also explore the relationship between different usage patterns and perceived usability, and identify user misconceptions, insecure practices, and design issues. We conclude with recommendations for large-scale 2FA deployments to maximize adoption, focusing on implementation design, use of adoption mandates, and strategic messaging.

To find software vulnerabilities using software vulnerability detection technology is an important way to ensure the system security. Existing software vulnerability detection methods have some limitations as they can only play a certain role in some specific situations. To accurately analyze and evaluate the existing vulnerability detection methods, an integrated testing and evaluation system (iTES) is designed and implemented in this paper. The main functions of the iTES are:(1) Vulnerability cases with source codes covering common vulnerability types are collected automatically to form a vulnerability cases library; (2) Fourteen methods including static and dynamic vulnerability detection are evaluated in iTES, involving the Windows and Linux platforms; (3) Furthermore, a set of evaluation metrics is designed, including accuracy, false positive rate, utilization efficiency, time cost and resource cost. The final evaluation and test results of iTES have a good guiding significance for the selection of appropriate software vulnerability detection methods or tools according to the actual situation in practice.

Cheap ubiquitous computing enables the collectionof massive amounts of personal data in a wide variety of domains.Many organizations aim to share such data while obscuring fea-tures that could disclose identities or other sensitive information.Much of the data now collected exhibits weak structure (e.g.,natural language text) and machine learning approaches havebeen developed to identify and remove sensitive entities in suchdata. Learning-based approaches are never perfect and relyingupon them to sanitize data can leak sensitive information as aconsequence. However, a small amount of risk is permissiblein practice, and, thus, our goal is to balance the value ofdata published and the risk of an adversary discovering leakedsensitive information. We model data sanitization as a gamebetween 1) a publisher who chooses a set of classifiers to applyto data and publishes only instances predicted to be non-sensitiveand 2) an attacker who combines machine learning and manualinspection to uncover leaked sensitive entities (e.g., personal names). We introduce an iterative greedy algorithm for thepublisher that provably executes no more than a linear numberof iterations, and ensures a low utility for a resource-limitedadversary. Moreover, using several real world natural languagecorpora, we illustrate that our greedy algorithm leaves virtuallyno automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations.

Domestic service robots become increasingly prevalent and autonomous, which will make task priority conflicts more likely. The robot must be able to effectively and appropriately negotiate to gain priority if necessary. In previous human-robot interaction (HRI) studies, imitating human negotiation behavior was effective but long-term effects have not been studied. Filling this research gap, an interactive online study (\$N=103\$) with two sessions and six trials was conducted. In a conflict scenario, participants repeatedly interacted with a domestic service robot that applied three different conflict resolution strategies: appeal, command, diminution of request. The second manipulation was reinforcement (thanking) of compliance behavior (yes/no). This led to a 3×2×6 mixed-subject design. User acceptance, trust, user compliance to the robot, and self-reported compliance to a household member were assessed. The diminution of a request combined with positive reinforcement was the most effective strategy and perceived trustworthiness increased significantly over time. For this strategy only, self-reported compliance rates to the human and the robot were similar. Therefore, applying this strategy potentially seems to make a robot equally effective as a human requester. This paper contributes to the design of acceptable and effective robot conflict resolution strategies for long-term use.

Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.

We performed a large-scale online survey (n=1,880) to study the padlock icon, an established security indicator in web browsers that denotes connection security through HTTPS. In this paper, we evaluate users’ understanding of the padlock icon, and how removing or replacing it might influence their expectations and decisions. We found that the majority of respondents (89%) had misconceptions about the padlock’s meaning. While only a minority (23%-44%) referred to the padlock icon at all when asked to evaluate trustworthiness, these padlock-aware users reported that they would be deterred from a hypothetical shopping transaction when the padlock icon was absent. These users were reassured after seeing secondary UI surfaces (i.e., Chrome Page Info) where more verbose information about connection security was present.We conclude that the padlock icon, displayed by browsers in the address bar, is still misunderstood by many users. The padlock icon guarantees connection security, but is often perceived to indicate the general privacy, security, and trustworthiness of a website. We argue that communicating connection security precisely and clearly is likely to be more effective through secondary UI, where there is more surface area for content. We hope that this paper boosts the discussion about the benefits and drawbacks of showing passive security indicators in the browser UI.

The compressed sensing (CS) method can reconstruct images with a small amount of under-sampling data, which is an effective method for fast magnetic resonance imaging (MRI). As the traditional optimization-based models for MRI suffered from non-adaptive sampling and shallow” representation ability, they were unable to characterize the rich patterns in MRI data. In this paper, we propose a CS MRI method based on iterative shrinkage threshold algorithm (ISTA) and adaptive sparse sampling, called DSLS-ISTA-Net. Corresponding to the sampling and reconstruction of the CS method, the network framework includes two folders: the sampling sub-network and the improved ISTA reconstruction sub-network which are coordinated with each other through end-to-end training in an unsupervised way. The sampling sub-network and ISTA reconstruction sub-network are responsible for the implementation of adaptive sparse sampling and deep sparse representation respectively. In the testing phase, we investigate different modules and parameters in the network structure, and perform extensive experiments on MR images at different sampling rates to obtain the optimal network. Due to the combination of the advantages of the model-based method and the deep learning-based method in this method, and taking both adaptive sampling and deep sparse representation into account, the proposed networks significantly improve the reconstruction performance compared to the art-of-state CS-MRI approaches.

In the era of Industry 4.0 (IR 4.0), information leakage has become a critical issue for information security. The basic approach to addressing information leakage threats is to implement an information security policy (ISP) that defines the standards, boundaries, and responsibilities of users of information and technology of an organization. ISPs are one of the most commonly used methods for controlling internal user security behaviours, which include, but not limited to, computer usage ethics; organizational system usage policies; Internet and email usage policies; and the use of social media. Human error is the main security threat to information security, resulting from negligence, ignorance, and failure to adhere to organizational information security policies. Information security incidents are a problem related to human behaviour because technology is designed and operated by humans, presenting the opportunities and spaces for human error. In addition to the factor of human error as the main source of information leakage, this study aims to systematically analyse the fundamental issues of information security policy compliance. An analysis of these papers identifies and categories critical factor that effect an employee's attitude toward compliance with ISP. The human, process, technology element and information governance should be thought as a significant scope for more efficiency of information security policy compliance and in any further extensive studies to improve on information security policy compliance. Therefore, to ensure these are properly understood, further study is needed to identity the information governance that needs to be included in organizations and current best practices for developing an information security policy compliance within organizations.

Contemporary enterprise systems focus primarily on performance and development/maintenance costs. Dealing with cyber-threats and system compromise is relegated to good coding (i.e., defensive programming) and secure environment (e.g., patched OS, firewalls, etc.). This approach, while a necessary start, is not sufficient. Such security relies on no missteps, and compromise only need a single flaw; consequently, we must design for compromise and mitigate its impact. One approach is to utilize fine-grained modularization and isolation. In such a system, decomposition ensures that compromise of a single module presents limited and known risk to data/resource theft and denial. We propose mechanisms for automating such modular composition and consider its system performance impact.

Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.

Code optimization is an essential feature for compilers and almost all software products are released by compiler optimizations. Consequently, bugs in code optimization will inevitably cast significant impact on the correctness of software systems. Locating optimization bugs in compilers is challenging as compilers typically support a large amount of optimization configurations. Although prior studies have proposed to locate compiler bugs via generating witness test programs, they are still time-consuming and not effective enough. To address such limitations, we propose an automatic bug localization approach, ODFL, for locating compiler optimization bugs via differentiating finer-grained options in this study. Specifically, we first disable the fine-grained options that are enabled by default under the bug-triggering optimization levels independently to obtain bug-free and bug-related fine-grained options. We then configure several effective passing and failing optimization sequences based on such fine-grained options to obtain multiple failing and passing compiler coverage. Finally, such generated coverage information can be utilized via Spectrum-Based Fault Localization formulae to rank the suspicious compiler files. We run ODFL on 60 buggy GCC compilers from an existing benchmark. The experimental results show that ODFL significantly outperforms the state-of-the-art compiler bug isolation approach RecBi in terms of all the evaluated metrics, demonstrating the effectiveness of ODFL. In addition, ODFL is much more efficient than RecBi as it can save more than 88% of the time for locating bugs on average.

With the rapid development of Android systems and the growing of Android market, Android system has become a focus of developers and users. MTK6795 is System-on-a-chip (SoC), which is specially designed by MediaTek for high-end smart phones. It integrates the application processor and the baseband processor in just one chip. In this paper, a new encryption method based on the baseband processor of MT6795 SoC is proposed and successfully applied on one Android-based smart phone to protect user data. In this method, the encryption algorithm and private user data are isolated into two processors, which improves the security of users' private data.

A secure device identifier (DevID) is cryptographically bound to a device and supports authentication of the devices identity. Locally significant identities can be securely associated with an initial manufacturer-provisioned DevID and used in provisioning and authentication protocols toallow a network administrator to establish the trustworthiness of a device and select appropriate policies for transmission and reception of data and control protocols to and from the device.

This paper shows the benefits of synchrophasor technology for islanding detection and resynchronization in the control room at Empresa de Transmisión Eléctrica Dominicana (ETED) in the Dominican Republic. EPG's Real Time Dynamics Monitoring System (RTDMS®) deployed at ETED was tested during operator training with the event data after an islanding event occurred on October 26, 2019, which caused the ETED System to split into two islands. RTDMS's islanding detection algorithm quickly detected and identified the event. The islanding situation was not clear for operators during the time of the event with the use of traditional SCADA tools. The use of synchophasor technology also provides valuable information for a quick and safe resynchronization. By monitoring the system frequency in each island and voltage angle differences between islands, operators can know the exact time of circuit breaker closure for a successful resynchronization. Synchrophasors allow the resynchronization in a relatively short time, avoiding the risk of additional load loss, generator outages or even a wider system blackout.

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.

This paper introduces lronMask, a new versatile verification tool for masking security. lronMask is the first to offer the verification of standard simulation-based security notions in the probing model as well as recent composition and expandability notions in the random probing model. It supports any masking gadgets with linear randomness (e.g. addition, copy and refresh gadgets) as well as quadratic gadgets (e.g. multiplication gadgets) that might include non-linear randomness (e.g. by refreshing their inputs), while providing complete verification results for both types of gadgets. We achieve this complete verifiability by introducing a new algebraic characterization for such quadratic gadgets and exhibiting a complete method to determine the sets of input shares which are necessary and sufficient to perform a perfect simulation of any set of probes. We report various benchmarks which show that lronMask is competitive with state-of-the-art verification tools in the probing model (maskVerif, scVerif, SILVEH, matverif). lronMask is also several orders of magnitude faster than VHAPS -the only previous tool verifying random probing composability and expandability- as well as SILVEH -the only previous tool providing complete verification for quadratic gadgets with nonlinear randomness. Thanks to this completeness and increased performance, we obtain better bounds for the tolerated leakage probability of state-of-the-art random probing secure compilers.

Biometric is uses to identify authorized person based on specific physiological or behavioral features. Template protection is a crucial requirement when designing an authentication system, where the template could be modified by attacker. Hill Cipher is a block cipher and symmetric key algorithm it has several advantages such as simplicity, high speed and high throughput can be used to protect Biometric Template. Unfortunately, Hill Cipher has some disadvantages such as takes smaller sizes of blocks, very simple and vulnerable for exhaustive key search attack and known plain text attack, also the key matrix which entered should be invertible. This paper proposed an enhancement to overcome these drawbacks of Hill Cipher by using a large and random key with large data block, beside overcome the Invertible-key Matrix problem. The efficiency of encryption has been checked out by Normalized Correlation Coefficient (NCC) and running time.

Today, most conversational agents are limited to simple tasks supported by standalone commands, such as getting directions or scheduling an appointment. To support more complex tasks, agents must be able to generalize from and combine the commands they already understand. This paper presents a new approach to designing conversational agents inspired by linguistic theory, where agents can execute complex requests interactively by combining commands through nested conversations. We demonstrate this approach in Iris, an agent that can perform open-ended data science tasks such as lexical analysis and predictive modeling. To power Iris, we have created a domain-specific language that transforms Python functions into combinable automata and regulates their combinations through a type system. Running a user study to examine the strengths and limitations of our approach, we find that data scientists completed a modeling task 2.6 times faster with Iris than with Jupyter Notebook.

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.

Process variation is the critical issue in hardware Trojan detection. In the state-of-art works, ring oscillators are employed to address this problem. But ring oscillators are very sensitive to IR-drop effect, which exists ICs. In this paper, based on circuit theory, a IR-drop calibration method is proposed. The nominal power supply voltage and the others power supply voltage with a very small difference of the nominal power supply voltage are applied to the test chip. It is assumed that they have the same IR-drop $Δ$V. Combined with these measured data, the value of Vth + $Δ$V, can be obtained by mathematic analysis. The typical Vth from circuit simulation is used to compute $Δ$V. We studied the proposed method in a tested chip.

Modern databases contain an enormous amount of information stored in a structured format. This information is processed to acquire knowledge. However, the process of information extraction from a Database System is cumbersome for non-expert users as it requires an extensive knowledge of DBMS languages. Therefore, an inevitable need arises to bridge the gap between user requirements and the provision of a simple information retrieval system whereby the role of a specialized Database Administrator is annulled. In this paper, we propose a methodology for building an Intelligent Querying System (IQS) by which a user can fire queries in his own (natural) language. The system first parses the input sentences and then generates SQL queries from the natural language expressions of the input. These queries are in turn mapped with the desired information to generate the required output. Hence, it makes the information retrieval process simple, effective and reliable.

Smart Grid is a major element of the Smart City concept that enables two-way communication of energy data between electric utilities and their consumers. These communication technologies are going through sharp modernization to meet future demand growth and to achieve reliability, security, and efficiency of the electric grid. In this paper, we implement an IPv6 based two-way communication system between the transformer agent (TA), installed at local electric transformer and various customer agents (CAs), connected to customer's smart meter. Various homes share their energy usage with the TA which in turn sends the utility's recommendations to the CAs. Raspberry Pi is used as hardware for all the CAs and the TA. We implement a self-healing mesh network between all nodes using OpenLab IEEE 802.15.4 chips and Routing Protocol for Low-Power and Lossy Networks (RPL), and the data is secured by RSA/AES keys. Several tests have been conducted in real environments, inside and outside of Carleton University, to test the performance of this communication network in various obstacle settings. In this paper, we highlight the details behind the implementation of this IPv6-based smart grid communication system, the related challenges, and the proposed solutions.

Wireless Sensor Networks (WSN) are widely used to monitor and control physical environments. An efficient energy management system is needed to be able to deploy these networks in lossy environments while maintaining reliable communication. The IPv6 Routing Protocol for Low-Power and Lossy networks is a routing protocol designed to properly manage energy without compromising reliability. This protocol has currently been implemented in Contiki OS, TinyOS, and OMNeT++ Castalia. But these applications also simulate all operation mechanics of a specified hardware model instead of just simulating the protocol only, thus adding unnecessary overhead and slowing down simulations on RPL. In light of this, we have implemented a working ns-3 implementation of RPL with support for multiple RPL instances with the use of a global repair mechanism. The behavior and output of our simulator was compared to Cooja for verification, and the results are similar with a minor difference in rank computation.

The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.

The increasing diffusion of malware endowed with steganographic techniques requires to carefully identify and evaluate a new set of threats. The creation of a covert channel to hide a communication within network traffic is one of the most relevant, as it can be used to exfiltrate information or orchestrate attacks. Even if network steganography is becoming a well-studied topic, only few works focus on IPv6 and consider real network scenarios. Therefore, this paper investigates IPv6 covert channels deployed in the wild. Also, it presents a performance evaluation of six different data hiding techniques for IPv6 including their ability to bypass some intrusion detection systems. Lastly, ideas to detect IPv6 covert channels are presented.