Storytelling Security: User-Intention Based Traffic Sanitization

Malicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level. We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security. We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool

Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filtering framework to address the problem. Within the framework, nearest neighbor field estimation is utilized to compute the importance proposal probabilities, which guide the Markov chain search towards promising regions and thus enhance the sampling efficiency; given the motion priors, a smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate the posterior distribution through a smoothing weight-updating scheme. Moreover, to track the abrupt and the smooth motions simultaneously, we develop an abrupt-motion detection scheme which can discover the presence of abrupt motions during online tracking. Extensive experiments on challenging image sequences demonstrate the effectiveness and the robustness of our algorithm in handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin

Overview of Upgrading of Pyrolysis Oil of Biomass

AbstractPyrolysis oil, obtained from fast pyrolysis of biomass, is a promising renewable energy source which has received widespread interests for its characteristics as combustion fuels used in boiler, engines or gas turbines and resources in chemical industries. However, the pyrolysis oil as a fuel has many unfavourable properties due to its chemical composition, making it corrosive, viscose and thermally instability. Therefore, bio-oil must be properly upgraded to produce high quality biofuel for using as transportation fuels. In this review article, various types of upgrading processes have been discussed in detail including physical refining routes, chemical refining and total pyrolysis refined routes. Finally, a new upgrading route, Physical-Chemical Refining (PCR) is proposed, which will be a very promising refining route of bio-oil

Utilitarian vs. hedonic roles of service robots and customer stereotypes: A person-environment fit theory perspective

Purpose – Drawing on person-environment fit theory, this study investigates how the relationships between service task types (i.e., utilitarian and hedonic service tasks) and perceived authenticity (i.e., service and brand authenticity) differ under different conditions of service providers (human employee vs. service robot). This study further examines whether customers’ stereotypes toward service robots (competence vs. warmth) moderate the relationship between service types and perceived authenticity. Design/methodology/approach – Using a 2 x 2 between-subjects experimental design, Study 1 examines a casual restaurant, while Study 2 assesses a theme park restaurant. Analysis of covariance and PROCESS are used to analyze the data. Findings – Both studies reveal that human service providers in hedonic services positively affect service and brand authenticity more than robotic employees. Additionally, the robot competence stereotype moderates the relationship between hedonic services, service, and brand authenticity, while the robot warmth stereotype moderates the relationship between hedonic services and brand authenticity in Study 2. Practical implications – Restaurant managers need to understand which functions and types of service outlets are best suited for service robots in different service contexts. Robot-environment fit should be considered when developers design and managers select robots for their restaurants. Originality/value – This study blazes a new theoretical trail of service robot research to systematically propose customer experiences with different service types by drawing upon person-environment fit theory and examining the moderating role of customers’ stereotypes toward service robots