Biblio

False data injection attack (FDIA) is a real threat to smart grids due to its wide range of vulnerabilities and impacts. Designing a proper detection scheme for FDIA is the 1stcritical step in defending the attack in smart grids. In this paper, we investigate two main statistical techniques-based approaches in this regard. The first is based on the principal component analysis (PCA), and the second is based on the canonical correlation analysis (CCA). The test cases illustrate a better characterization performance of FDIA using CCA compared to the PCA. Further, CCA provides a better differentiation of FDIA from normal grid contingencies. On the other hand, PCA provides a significantly reduced false alarm rate.

The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.

This paper presents a computational model for managing an Embodied Conversational Agent's first impressions of warmth and competence towards the user. These impressions are important to manage because they can impact users' perception of the agent and their willingness to continue the interaction with the agent. The model aims at detecting user's impression of the agent and producing appropriate agent's verbal and nonverbal behaviours in order to maintain a positive impression of warmth and competence. User's impressions are recognized using a machine learning approach with facial expressions (action units) which are important indicators of users' affective states and intentions. The agent adapts in real-time its verbal and nonverbal behaviour, with a reinforcement learning algorithm that takes user's impressions as reward to select the most appropriate combination of verbal and non-verbal behaviour to perform. A user study to test the model in a contextualized interaction with users is also presented. Our hypotheses are that users' ratings differs when the agents adapts its behaviour according to our reinforcement learning algorithm, compared to when the agent does not adapt its behaviour to user's reactions (i.e., when it randomly selects its behaviours). The study shows a general tendency for the agent to perform better when using our model than in the random condition. Significant results shows that user's ratings about agent's warmth are influenced by their a-priori about virtual characters, as well as that users' judged the agent as more competent when it adapted its behaviour compared to random condition.

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.

Containers have transformed the cluster management into an application oriented endeavor, thus being widely used as the deployment units (i.e., micro-services) of large scale cloud services. As opposed to VMs, containers allow for resource provisioning with fine granularity and their resource usage directly reflects the micro-service behaviors. Container management systems like Kubernetes and Mesos provision resources to containers according to the capacity requested by the developers. Resource usages estimated by the developers are grossly inaccurate. They tend to be risk-averse and over provision resources, as under-provisioning would cause poor runtime performance or failures. Without actually running the workloads, resource provisioning is challenging. However, benchmarking production workloads at scale requires huge manual efforts. In this work, we leverage IBM Monitoring service to profile the resource usage of production IBM Watson services in rolling windows by focusing on both evaluating how developers request resources and characterizing the actual resource usage. Our resource profiling study reveals two important characteristics of the cognitive workloads. 1. Stationarity. According to Augmented Dickey-Fuller test with 95% confidence, more than 95% of the container instances have stationary CPU usage while more than 85% have stationary memory usage, indicating that resource usage statistics do not change over time. We find for the majority of containers that the stationarity can be detected at the early stage of container execution and can hold throughout their lifespans. In addition, containers with non-stationary CPU or memory usage are also observed to implement predictable usage trends and patterns (e.g., trend stationarity or seasonality). 2. Predictability by container image. By clustering the containers based on their images, container resource usages within the same cluster are observed to exhibit strong statistical similarity. This suggests that the history of resource usage for one instance can be used to predict usage for future instances that run the same container image. Based on profiling results of running containers in rolling windows, we propose a resource usage advisory system to refine the requested resource values of the running and arriving containers as illustrated in Fig. 1. Our system continuously retrieves the resource usage metrics of running containers from IBM monitoring service and predicts the resource usage profiles in a container resource usage prediction agent. Upon the arrival of a new pod1, the resource profile advisor, proposed as a module in the web-hooked admission controller in Kubernetes, checks whether the resource profile of each container in the pod has been predicted with confidence. If a container's profile has been predicted and cached in the container resource profile database, the default requested values of containers are refined by the predicted ones; otherwise, containers are forwarded to the scheduler without any change. Similarly, a resource profile auto-scaler is proposed to update the requested resource values of containers for running pods2 as soon as the database is updated. Our study shows that developers request at least 1 core-per-second (cps) CPU and 1 GB memory for ≥ 70% of the containers, while ≥ 80% of the containers actually use less than 1 cps and 1GB. Additionally, \textbackslashtextasciitilde 20% of the containers are significantly under provisioned. We use resource usage data in one day to generate container resource profiles and evaluate our approach based on the actual usage on the following day. Without our system, average CPU (memory) usage for \textbackslashtextgreater90% of containers lies outside of 50% - 100% (70% - 100%) of the requested values. Our evaluation shows that our system can advise request values appropriately so that average and 95th percentile CPU (memory) usage for \textbackslashtextgreater90% of the containers are within 50% - 100% (70% - 100%) of the requested values. Furthermore, average CPU (memory) utilization across all pods is raised from 10% (26%) to 54% (88%).

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) are the first choice of most consumers to authorize the payment. This work 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, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios.

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.

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.