Biblio

The simplest and purest practical object-oriented language designs today are seen in dynamically-typed languages, such as Smalltalk and Self. Static types, however, have potential benefits for productivity, security, and reasoning about programs. In this paper, we describe the design of Wyvern, a statically typed, pure object-oriented language that attempts to retain much of the simplicity and expressiveness of these iconic designs. Our goals lead us to combine pure object-oriented and functional abstractions in a simple, typed setting. We present a foundational object-based language that we believe to be as close as one can get to simple typed lambda calculus while keeping object-orientation. We show how this foundational language can be translated to the typed lambda calculus via standard encodings. We then define a simple extension to this language that introduces classes and show that classes are no more than sugar for the foundational object-based language. Our future intention is to demonstrate that modules and other object-oriented features can be added to our language as not more than such syntactical extensions while keeping the object-oriented core as pure as possible. The design of Wyvern closely follows both historical and modern ideas about the essence of object-orientation, suggesting a new way to think about a minimal, practical, typed core language for objects.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

Non-Volatile Memories (NVMs) require security mechanisms, e.g., counter mode encryption and integrity tree verification, which are important to protect systems in terms of encryption and data integrity. These security mechanisms heavily rely on extra security metadata that need to be efficiently and accurately recovered after system crashes or power off. Established SGX integrity tree (SIT) becomes efficient to protect system integrity and however fails to be restored from leaves, since the computations of SIT nodes need their parent nodes as inputs. To recover the security metadata with low write overhead and short recovery time, we propose an efficient and instantaneous persistence scheme, called STAR, which instantly persists the modifications of security metadata without extra memory writes. STAR is motivated by our observation that the parent nodes in cache are modified due to persisting their child nodes. STAR stores the modifications of parent nodes in their child nodes and persists them just using one atomic memory write. To eliminate the overhead of persisting the modifications, STAR coalesces the modifications and MACs in the evicted metadata. For fast recovery and verification of the metadata, STAR uses bitmap lines in asynchronous DRAM refresh (ADR) to indicate the locations of stale metadata, and constructs a cached merkle tree to verify the correctness of the recovery process. Our evaluation results show that compared with state-of-the-art work, our proposed STAR delivers high performance, low write traffic, low energy consumption and short recovery time.

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Smartwatches, with motion sensors, are becoming a common utility for users. With the increasing popularity of practical wearable computers, and in particular smartwatches, the security risks linked with sensors on board these devices have yet to be fully explored. Recent research literature has demonstrated the capability of using a smartphone's own accelerometer and gyroscope to infer tap locations; this paper expands on this work to demonstrate a method for inferring smartphone PINs through the analysis of smartwatch motion sensors. This study determines the feasibility and accuracy of inferring user keystrokes on a smartphone through a smartwatch worn by the user. Specifically, we show that with malware accessing only the smartwatch's motion sensors, it is possible to recognize user activity and specific numeric keypad entries. In a controlled scenario, we achieve results no less than 41% and up to 92% accurate for PIN prediction within 5 guesses.

The Structured Query Language Injection Attack (SQLIA) is one of the most serious and popular threats of web applications. The results of SQLIA include the data loss or complete host takeover. Detection of SQLIA is always an intractable challenge because of the heterogeneity of the attack payloads. In this paper, a novel method to detect SQLIA based on word vector of SQL tokens and LSTM neural networks is described. In the proposed method, SQL query strings were firstly syntactically analyzed into tokens, and then likelihood ratio test is used to build the word vector of SQL tokens, ultimately, an LSTM model is trained with sequences of token word vectors. We developed a tool named WOVSQLI, which implements the proposed technique, and it was evaluated with a dataset from several sources. The results of experiments demonstrate that WOVSQLI can effectively identify SQLIA.

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

Evaluating the trade-offs involved in cybersecurity professionalization.

Entropy sources are designed to provide unpredictable random numbers for cryptographic systems. As an assessment of the sources, Shannon entropy is usually adopted to quantitatively measure the unpredictability of the outputs. In several related works about the entropy evaluation of ring oscillator-based (RO-based) entropy sources, authors evaluated the unpredictability with the average conditional Shannon entropy (ACE) of the source, moreover provided a lower bound of the ACE (LBoACE). However, in this paper, we have demonstrated that when the adversaries have access to the history outputs of the entropy source, for example, by some intrusive attacks, the LBoACE may overestimate the actual unpredictability of the next output for the adversaries. In this situation, we suggest to adopt the specific conditional Shannon entropy (SCE) which exactly measures the unpredictability of the future output with the knowledge of previous output sequences and so is more consistent with the reality than the ACE. In particular, to be conservative, we propose to take the lower bound of the SCE (LBoSCE) as an estimation of the worst-case entropy of the sources. We put forward a detailed method to estimate this worst-case entropy of RO-based entropy sources, which we have also verified by experiment on an FPGA device. We recommend to adopt this method to provide a conservative assessment of the unpredictability when the entropy source works in a vulnerable environment and the adversaries might obtain the previous outputs.

Mobile Ad hoc networks (MANET) are becoming extremely popular because of the expedient features that also make them more exposed to various kinds of security attacks. The Wormhole attack is considered to be the most unsafe attack due to its unusual pattern of tunnel creation between two malevolent nodes. In it, one malevolent node attracts all the traffic towards the tunnel and forwards it to another malevolent node at the other end of the tunnel and replays them again in the network. Once the Wormhole tunnel is created it can launch different kind of other attacks such as routing attack, packet dropping, spoofing etc. In past few years a lot of research is done for securing routing protocols. Dynamic Source Routing (DSR) protocol is considered foremost MANET routing protocols. In this paper we are forming the wormhole tunnel in which malevolent nodes use different interfaces for communication in DSR protocol. NS3 simulator is being used for the analysis of the DSR routing protocol under the wormhole attack. This paper provides better understanding of the wormhole attack in DSR protocol which can benefit further research.

In Mobile Ad hoc Network (MANET) is a self-organizing session of communication between wireless mobile nodes build up dynamically regardless of any established infrastructure or central authority. In MANET each node behaves as a sender, receiver and router which are connected directly with one another if they are within the range of communication or else will depend on intermediate node if nodes are not in the vicinity of each other (hop-to-hop). MANET, by nature are very open, dynamic and distributed which make it more vulnerable to various attacks such as sinkhole, jamming, selective forwarding, wormhole, Sybil attack etc. thus acute security problems are faced more related to rigid network. A Wormhole attack is peculiar breed of attack, which cause a consequential breakdown in communication by impersonating legitimate nodes by malicious nodes across a wireless network. This attack can even collapse entire routing system of MANET by specifically targeting route establishment process. Confidentiality and Authenticity are arbitrated as any cryptographic primitives are not required to launch the attack. Emphasizing on wormhole attack attributes and their defending mechanisms for detection and prevention are discussed in this paper.

In Mobile Ad hoc Network (MANET) is a self-organizing session of communication between wireless mobile nodes build up dynamically regardless of any established infrastructure or central authority. In MANET each node behaves as a sender, receiver and router which are connected directly with one another if they are within the range of communication or else will depend on intermediate node if nodes are not in the vicinity of each other (hop-to-hop). MANET, by nature are very open, dynamic and distributed which make it more vulnerable to various attacks such as sinkhole, jamming, selective forwarding, wormhole, Sybil attack etc. thus acute security problems are faced more related to rigid network. A Wormhole attack is peculiar breed of attack, which cause a consequential breakdown in communication by impersonating legitimate nodes by malicious nodes across a wireless network. This attack can even collapse entire routing system of MANET by specifically targeting route establishment process. Confidentiality and Authenticity are arbitrated as any cryptographic primitives are not required to launch the attack. Emphasizing on wormhole attack attributes and their defending mechanisms for detection and prevention are discussed in this paper.

The mobile ad-hoc networks comprised of nodes that are communicated through dynamic request and also by static table driven technique. The dynamic route discovery in AODV routing creates an unsecure transmission as well as reception. The reason for insecurity is the route request is given to all the nodes in the network communication. The possibility of the intruder nodes are more in the case of dynamic route request. Wormhole attacks in MANETs are creating challenges in the field of network analysis. In this paper the wormhole scenario is realized using high power transmission. This is implemented using energy model of ns2 simulator. The Apptool simulator identifies the energy level of each node and track the node of high transmission power. The performance curves for throughput, node energy for different encrypted values, packet drop ratio, and end to end delay are plotted.

In this paper, we explore the use of machine learning technique for wormhole attack detection in ad hoc network. This work has categorized into three major tasks. One of our tasks is a simulation of wormhole attack in an ad hoc network environment with multiple wormhole tunnels. A next task is the characterization of packet attributes that lead to feature selection. Consequently, we perform data generation and data collection operation that provide large volume dataset. The final task is applied to machine learning technique for wormhole attack detection. Prior to this, a wormhole attack has detected using traditional approaches. In those, a Multirate-DelPHI is shown best results as detection rate is 90%, and the false alarm rate is 20%. We conduct experiments and illustrate that our method performs better resulting in all statistical parameters such as detection rate is 93.12% and false alarm rate is 5.3%. Furthermore, we have also shown results on various statistical parameters such as Precision, F-measure, MCC, and Accuracy.

Because the underwater acoustic communication network transmits data through the underwater acoustic wireless link, the Underwater Acoustic Communication Network is easy to suffer from the external artificial interference, in this paper, the detection algorithm of wormhole attack in Underwater Acoustic Communication Network based on Azimuth measurement technology is studied. The existence of wormhole attack is judged by Azimuth or distance outliers, and the security performance of underwater acoustic communication network is evaluated. The influence of different azimuth direction errors on the detection probability of wormhole attack is analyzed by simulation. The simulation results show that this method has a good detection effect for Underwater Acoustic Communication Network.

The human side of cyber is fundamentally important to understanding and improving cyber operations. With the exception of Capture the Flag (CTF) exercises, cyber testing and experimentation tends to ignore the human attacker. While traditional CTF events include a deeply rooted human component, they rarely aim to measure human performance, cognition, or psychology. We argue that CTF is not sufficient for measuring these aspects of the human; instead, we examine the value in performing red team behavioral and cognitive testing in a large-scale, controlled human-subject experiment. In this paper we describe the pros and cons of performing this type of experimentation and provide detailed exposition of the data collection and experimental controls used during a recent cyber deception experiment-the Tularosa Study. Finally, we will discuss lessons learned and how our experiences can inform best practices in future cyber operations studies of human behavior and cognition.

Fruit-80, an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource constrained devices in the Internet of Things. In this paper, we propose area and speed optimization architectures of Fruit-80 on FPGAs. The area optimization architecture reuses NFSR&LFSR feedback functions and achieves the most suitable ratio of look-up-tables and flip-flops. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. In conclusion, the optimal throughput-to-area ratio of the speed optimization architecture is better than that of Grain v1. The area optimization architecture occupies only 35 slices on Xilinx Spartan-3 FPGA, smaller than that of Grain and other common stream ciphers. To the best of our knowledge, this result sets a new record of the minimum area in lightweight cipher implementations on FPGA.

The SCADA (Supervisory Control And Data Acquisition) has become ubiquitous in industrial control systems. However, it may be exposed to cyber attack threats when it accesses the Internet. We propose a three-layer IDS (Intrusion Detection System) model, which integrates three main functions: access control, flow detection and password authentication. We use the reliability test system IEEE RTS-79 to evaluate the reliability. The experimental results provide insights into the establishment of the power SCADA system reliability enhancement strategies.

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

Scientific datasets and the experiments that analyze them are growing in size and complexity, and scientists are facing difficulties to share such resources. Some initiatives have emerged to try to solve this problem. One of them involves the use of scientific workflows to represent and enact experiment execution. There is an increasing number of workflows that are potentially relevant for more than one scientific domain. However, it is hard to find workflows suitable for reuse given an experiment. Creating a workflow takes time and resources, and their reuse helps scientists to build new workflows faster and in a more reliable way. Search mechanisms in workflow repositories should provide different options for workflow discovery, but it is difficult for generic repositories to provide multiple mechanisms. This paper presents WorkflowHunt, a hybrid architecture for workflow search and discovery for generic repositories, which combines keyword and semantic search to allow finding relevant workflows using different search methods. We validated our architecture creating a prototype that uses real workflows and metadata from myExperiment, and compare search results via WorkflowHunt and via myExperiment's search interface.

Cyber Threat Intelligence (CTI) sharing platforms are valuable tools in cybersecurity. However, despite the fact that effective CTI exchange highly depends on human aspects, cyber behavior in CTI sharing platforms has been notably less investigated by the security research community.Motivated by this research gap, we ground our work in the concrete challenge of understanding users’ perceptions of information sharing in CTI platforms. To this end, we propose a conceptual workflow and toolchain that would seek to verify whether users have an accurate comprehension of how far information travels when shared in a CTI sharing platform.We contextualize our concept within MISP as a use case, and discuss the benefits of our socio-technical approach as a potential tool for security analysis, simulation, or education/training support. We conclude with a brief outline of future work that would seek to evaluate and validate the proposed model.

Workarounds to computer access in healthcare are sufficiently common that they often go unnoticed. Clinicians focus on patient care, not cybersecurity. We argue and demonstrate that understanding workarounds to healthcare workers’ computer access requires not only analyses of computer rules, but also interviews and observations with clinicians. In addition, we illustrate the value of shadowing clinicians and conducing focus groups to understand their motivations and tradeoffs for circumvention. Ethnographic investigation of the medical workplace emerges as a critical method of research because in the inevitable conflict between even well-intended people versus the machines, it’s the people who are the more creative, flexible, and motivated. We conducted interviews and observations with hundreds of medical workers and with 19 cybersecurity experts, CIOs, CMIOs, CTO, and IT workers to obtain their perceptions of computer security. We also shadowed clinicians as they worked. We present dozens of ways workers ingeniously circumvent security rules. The clinicians we studied were not “black hat” hackers, but just professionals seeking to accomplish their work despite the security technologies and regulations.