Cyber-physical systems (CPSs) integrate the computation with physical processes. Embedded computers and networks monitor control processes, usually feedback loops where processes affect computations vice versa. CPS was identified as one of eight research priority areas in August 2007 report President's Council Advisors on Science Technology, will be core component many critical infrastructures industrial near future. However, a variety random failures cyber attacks exist CPS, which greatly...

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted lot of attention its applications in computer WSNs. However, the nature lazy-learning makes kNN-based schemes difficult to be used online manner, especially when communication cost constrained. In this paper, new scheme based...

ADFA-LD is a recently released data set for evaluating host-based anomaly detection systems, aiming to substitute the existing benchmark sets which have failed reflect characteristics of modern computer systems. In previous work, we had attempted evaluate with highly efficient frequency model but performance inferior. this paper, focus on other typical technical category that detects anomalies short sequence model. collaboration one-class SVM algorithm, novel system proposed ADFA-LD. The...

In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously collection neighboring nodes and lasts for significant period time. Existing point-based techniques, context, are not very effective efficient. With the proposed distributed segment-based recursive kernel density estimation, global probability function can be tracked its difference between every two periods time is continuously measured decision making....

Host-based intrusion detection systems (HIDSs), especially anomaly-based, have received much attention over the past few decades. Over time, however, existing data sets used for evaluation of a HIDS lost most their relevance due to substantial development computer systems. To fill this gap, ADFA Linux set (ADFA-LD) is recently released, which composed thousands system call traces collected from contemporary local server and expects be new benchmark evaluating HIDS. In paper, we perform...

In wireless sensor networks (WSNs), it has been observed that most abnormal events persist over a considerable period of time instead being transient. As existing anomaly detection techniques usually operate in point-based manner handles each observation individually, they are unable to reliably and efficiently report such long-term anomalies appeared an individual node. Therefore, this paper, we focus on new technique for handling data segment-based manner. Considering collection...

Crowdsourced testing is an emerging trend, in which test tasks are entrusted to the online crowd workers. Typically, a crowdsourced task aims detect as many bugs possible within limited budget. However not all workers equally skilled at finding bugs; Inappropriate may miss bugs, or report duplicate while hiring them requires nontrivial Therefore, it of great value recommend set appropriate for so that more software can be detected with fewer This paper first presents new characterization and...

Online anomaly detection is critical for protecting wireless sensor networks (WSNs) from cyber-attacks and random faults, which handles the streaming data in real-time. Comparing to other techniques, histogram-based cheaper computation, should be suitable WSNs. However, performing with an online manner WSNs not a straightforward issue. Most of existing schemes have depend on verification procedure, costs great amount computational overhead as well communication overhead. Thus, it almost...

Cloud storage can simplify data management and reduce maintenance costs. However, many users companies hesitate to move their cloud because of security privacy concerns about third-party service providers. Oblivious RAM (ORAM) aims enable privacy-preserving access stored in the cloud. This article offers a tutorial on ORAM surveys recent literature. The authors also study load-balancing problem when applying big They propose heuristic algorithms achieve load balancing both static dynamic deployments.

Crowdsourced testing is an emerging trend in software testing, which relies on crowd workers to accomplish test tasks. Due the cost constraint, a task usually involves limited number of workers. Furthermore, more does not necessarily result detecting bugs. Different workers, who may have different experience and expertise, make much differences outcomes. For example, some inappropriate miss true bug, introduce false bugs or report duplicated bugs, decreases quality. In current practice,...

Host-based anomaly detection systems (HADS) serves as the second line of defense after cyber attacks have penetrated network level defense. The major components reliable HADS includes enriched data source (DS), computational efficient feature retrieval (DFR), accurate and fast decision engine (DE). ADFA-LD is a recently published set which reflects invisible threat environment modern computer system. existing utilizing DS, exhibits high DFR inferior performance DE at real-time. drawback...

Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles heterogeneity spectral features as well implicit anisotropic spatial dependency across class labels. In this paper, we propose geographical hidden Markov tree, probabilistic graphical model that generalizes common from one dimensional sequence to two map. Partial order is incorporated layer...

Mobile app testing is challenging since each test needs to be executed in a variety of operating contexts including heterogeneous devices, various wireless networks and different locations. Crowdsourcing enables mobile distributed as crowdsourced task leverage crowd workers accomplish the test. However, high quality expected context coverage are difficult achieve testing. Upon distributing task, providers neither know who participate nor predict whether all can covered task. To address this...

Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce continuous time constrained problem for dynamic diffusion networks, based on novel model called DynaDiffuse. Although NP-hard, functions are monotonic and submodular, enabling fast approximations top an innovative stochastic checking approach. Experiments real network data show that our...

Action recognition has been an active research area for many years. Extracting discriminative spatial and temporal features of different actions plays a key role in accomplishing this task. Current popular methods action are mainly based on two‐stream Convolutional Networks (ConvNets) or 3D ConvNets. However, the computational cost ConvNets is high requirement optical flow while takes too much memory because they have large amount parameters. To alleviate such problems, authors propose Dual...

Wireless multimedia sensor networks (WMSNs) are an emerging type of which contain nodes equipped with microphones, cameras, and other sensors that producing content. These have the potential to enable a large class applications ranging from military modern healthcare. Since in WMSNs information is by nature it uses wireless link as mode communication so this posse's serious security threat network. Thereby, mechanisms protect found importance lately. However given fact WMSN resources...

Given a spatial raster framework with explanatory feature layers, contextual layer (e.g., potential field), as well set of training samples class labels, the prediction problem aims to learn model that can predict layer. The is important in societal applications such flood extent mapping for disaster response and national water forecasting, but challenging due noise, obstacles, heterogeneity maps, implicit dependency between locations based on gradient directions large number sample...

Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make nodes unable to sustain computation, and result quite expensive communication overhead. The feature reduction great sense through of dimensionality when features are strongly interrelated. Among these UAD techniques, distance-based (DB-AD) is a special one that allows be described by probability model. Based on this...

Extracting good action representations from video frames is an intricate challenge due to the presence of moving objects various sizes across current recognition datasets. Most methodologies have paid scant attention this characteristic and relied on deep learning models automatically solve it. In paper, we introduce a multi-scale receptive fields convolutional network (MSRFNet), which fashioned after pseudo-3D residual architecture mitigate impact scale variation in objects. The crux...