scholarly journals SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Wang ◽  
Zhizhong Wu ◽  
Xi Li ◽  
Xuehai Zhou ◽  
Aili Wang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Mobile Devices ◽  
Service Oriented Architecture ◽  
Malware Detection ◽  
Detection Algorithm ◽  
Main Task ◽  
Detection Algorithms ◽  
Usage Patterns ◽  
Service Oriented ◽  
And Behavior

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users’ regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices.

Leveraging Pervasive and Ubiquitous Service Computing

2009 ◽  
pp. 262-278
Author(s):  
Zhijun Zhang
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Service Oriented Architecture ◽  
Wireless Networking ◽  
Service Computing ◽  
Service Oriented ◽  
Promising Application ◽  
Networking Technologies ◽  
Computer Interaction

The advancement of technologies to connect people and objects anywhere has provided many opportunities for enterprises. This chapter will review the different wireless networking technologies and mobile devices that have been developed, and discuss how they can help organizations better bridge the gap between their employees or customers and the information they need. The chapter will also discuss the promising application areas and human-computer interaction modes in the pervasive computing world, and propose a service-oriented architecture to better support such applications and interactions.

Leveraging Pervasive and Ubiquitous Service Computing

2009 ◽  
pp. 232-248
Author(s):  
Zhijun Zhang
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Service Oriented Architecture ◽  
Wireless Networking ◽  
Service Computing ◽  
Service Oriented ◽  
Promising Application ◽  
Networking Technologies ◽  
Computer Interaction

The advancement of technologies to connect people and objects anywhere has provided many opportunities for enterprises. This chapter will review the different wireless networking technologies and mobile devices that have been developed, and discuss how they can help organizations better bridge the gap between their employees or customers and the information they need. The chapter will also discuss the promising application areas and human-computer interaction modes in the pervasive computing world, and propose a service-oriented architecture to better support such applications and interactions.

scholarly journals A Novel Active Anomaly Discovery Method and its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
The State ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Unsupervised Anomaly Detection ◽  
Discovery Method

Anomaly detection aims to identify the true anomalies from a given set of data instances. Unsupervised anomaly detection algorithms are applied to an unlabeled dataset by producing a ranked list based on anomaly scores. Unfortunately, due to the inherent limitations, many of the top-ranked instances by unsupervised algorithms are not anomalies or not interesting from an application perspective, which leads to high false-positive rates. Active anomaly discovery (AAD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information and incorporate it into the anomaly detection algorithm to improve the detection accuracy iteratively. However, labeling is often costly. Therefore, the way to balance detection accuracy and labeling cost is essential. Along this line, this paper proposes a novel AAD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised anomaly detection algorithm, namely, Isolation Forest, to extract features. Thereafter, the sparsity of the extracted features is utilized to improve its anomaly detection performance. To enforce the sparsity of the features and subsequent improvement of the detection analysis, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Anomaly Discovery (SALAD), is proposed with its theoretical Regret analysis. Extensive experiments on both open-source and additive manufacturing datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art algorithms for anomaly detection.

scholarly journals Duomenų apdorojimas ir vizualizavimas mobiliuosiuose įrenginiuose

2013 ◽  
Vol 65 ◽  
pp. 75-84
Author(s):  
Kristina Lapin ◽  
Sigitas Dapkūnas
Keyword(s):  
Data Processing ◽  
Web Service ◽  
Mobile Devices ◽  
Mobile Device ◽  
Service Oriented Architecture ◽  
Font Size ◽  
Service Oriented

Straipsnyje nagrinėjami duomenų apdorojimo mobiliuosiuose įrenginiuose bei klasikiniai ir neseniai sukurti vizualizavimo metodai, tinkantys vaizduoti duomenis mažame ekrane. Analizuojami mobiliųjų įrenginių apdorojimo ir duomenų pateikimo ribojimai, į kuriuos atsižvelgiama kuriant programas visur esančių skaičiavimų aplinkose. Apžvalginio tyrimo rezultatus planuojama pritaikyti kuriant saityno paslaugos vizualizavimo modelį paslaugų architektūros stiliaus sistemoje.Data Processing and Visualization on Mobile Devices Kristina Lapin, Sigitas Dapkūnas SummaryThe paper surveys the data processing and visualization peculiarities in mobile devices. Developing the visualisation application to a mobile device a researcher has to consider their computational and size limitations. The mobile and traditional visualization methods for small screens are analysed. Researchers start to address the clutter problem showing the stream of data. The outcome of this research is planned to develop a model of visualisation of web service for a system based on the service-oriented architecture.="font-size: 11pt; line-height: 115%; font-family: Calibri, sans-serif;">

Composite Process-Personalization with Service-Oriented Architecture

2009 ◽  
pp. 226-244
Author(s):  
Rajani Shankar Sadasivam
Keyword(s):  
Web Services ◽  
Mobile Devices ◽  
Business Process ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Agent Based ◽  
Service Oriented ◽  
Composite Services ◽  
Large Systems

The integration of large systems remains problematic in spite of advances in composite services approaches, such as Web services and business process technologies. The next challenge in integration is composite process-personalization (CPP), which involves addressing the needs of the interaction worker. An interaction worker participates and drives business processes. As these workers increasingly perform their work from mobile devices, CPP becomes an important area of mobile research. In this chapter, an agent-based approach to composite services development is introduced, addressing the lack of CPP in integration. A case study is used to demonstrate the steps in the agent-based approach.

Leveraging Pervasive and Ubiquitous Service Computing

2011 ◽  
pp. 1997-2013
Author(s):  
Zhijun Zhang
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Service Oriented Architecture ◽  
Wireless Networking ◽  
Service Computing ◽  
Service Oriented ◽  
Promising Application ◽  
Networking Technologies ◽  
Computer Interaction

The advancement of technologies to connect people and objects anywhere has provided many opportunities for enterprises. This chapter will review the different wireless networking technologies and mobile devices that have been developed, and discuss how they can help organizations better bridge the gap between their employees or customers and the information they need. The chapter will also discuss the promising application areas and human-computer interaction modes in the pervasive computing world, and propose a service-oriented architecture to better support such applications and interactions.

Leveraging Pervasive and Ubiquitous Service Computing

2011 ◽  
pp. 261-284
Author(s):  
Zhijun Zhang
Keyword(s):  
Human Computer Interaction ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Service Oriented Architecture ◽  
Wireless Networking ◽  
Service Computing ◽  
Service Oriented ◽  
Promising Application ◽  
Networking Technologies ◽  
Computer Interaction

The advancement of technologies to connect people and objects anywhere has provided many opportunities for enterprises. This chapter will review the different wireless networking technologies and mobile devices that have been developed, and discuss how they can help organizations better bridge the gap between their employees or customers and the information they need. The chapter will also discuss the promising application areas and human-computer interaction modes in the pervasive computing world, and propose a service-oriented architecture to better support such applications and interactions.

scholarly journals A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

2020 ◽  
Vol 12 (20) ◽  
pp. 3387
Author(s):  
Ferdi Andika ◽  
Mia Rizkinia ◽  
Masahiro Okuda
Keyword(s):  
Anomaly Detection ◽  
Computing Time ◽  
Area Under The Curve ◽  
Detection Algorithm ◽  
High Spectral Resolution ◽  
Band Selection ◽  
Detection Accuracy ◽  
Maximum Area ◽  
Detection Algorithms ◽  
Rich Information

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.

scholarly journals A Novel Active Anomaly Discovery Method and its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
The State ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Unsupervised Anomaly Detection ◽  
Discovery Method

Anomaly detection aims to identify the true anomalies from a given set of data instances. Unsupervised anomaly detection algorithms are applied to an unlabeled dataset by producing a ranked list based on anomaly scores. Unfortunately, due to the inherent limitations, many of the top-ranked instances by unsupervised algorithms are not anomalies or not interesting from an application perspective, which leads to high false-positive rates. Active anomaly discovery (AAD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information and incorporate it into the anomaly detection algorithm to improve the detection accuracy iteratively. However, labeling is often costly. Therefore, the way to balance detection accuracy and labeling cost is essential. Along this line, this paper proposes a novel AAD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised anomaly detection algorithm, namely, Isolation Forest, to extract features. Thereafter, the sparsity of the extracted features is utilized to improve its anomaly detection performance. To enforce the sparsity of the features and subsequent improvement of the detection analysis, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Anomaly Discovery (SALAD), is proposed with its theoretical Regret analysis. Extensive experiments on both open-source and additive manufacturing datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art algorithms for anomaly detection.

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