Atomic layer deposition (ALD) is gaining attention as a thin film deposition method, uniquely suitable for depositing uniform and conformal films on complex three-dimensional topographies. The deposition of a film of a given material by ALD relies on the successive, separated, and self-terminating gas–solid reactions of typically two gaseous reactants. Hundreds of ALD chemistries have been found for depositing a variety of materials during the past decades, mostly for inorganic materials but lately also for organic and inorganic–organic hybrid compounds. One factor that often dictates the properties of ALD films in actual applications is the crystallinity of the grown film: Is the material amorphous or, if it is crystalline, which phase(s) is (are) present. In this thematic review, we first describe the basics of ALD, summarize the two-reactant ALD processes to grow inorganic materials developed to-date, updating the information of an earlier review on ALD [R. L. Puurunen, J. Appl. Phys. 97, 121301 (2005)], and give an overview of the status of processing ternary compounds by ALD. We then proceed to analyze the published experimental data for information on the crystallinity and phase of inorganic materials deposited by ALD from different reactants at different temperatures. The data are collected for films in their as-deposited state and tabulated for easy reference. Case studies are presented to illustrate the effect of different process parameters on crystallinity for representative materials: aluminium oxide, zirconium oxide, zinc oxide, titanium nitride, zinc zulfide, and ruthenium. Finally, we discuss the general trends in the development of film crystallinity as function of ALD process parameters. The authors hope that this review will help newcomers to ALD to familiarize themselves with the complex world of crystalline ALD films and, at the same time, serve for the expert as a handbook-type reference source on ALD processes and film crystallinity.

Crystallinity of inorganic films grown by atomic layer deposition: Overview and general trends

Systems and methods for discovery and classification of denial of service attacks in a distributed computing system may employ local agents on nodes thereof to detect resource-related events. An information later agent may determine if events indicate attacks, perform clustering analysis to determine if they represent known or unknown attack patterns, classify the attacks, and initiate appropriate responses to prevent and/or mitigate the attack, including sending warnings and/or modifying resource pool(s). The information layer agent may consult a knowledge base comprising information associated with known attack patterns, including state-action mappings. An attack tree model and an overlay network (over which detection and/or response messages may be sent) may be constructed for the distributed system. They may be dynamically modified in response to changes in system configuration, state, and/or workload. Reinforcement learning may be applied to the tuning of attack detection and classification techniques and to the identification of appropriate responses.

System and method for distributed denial of service identification and prevention

An electronic message is analyzed for malware contained in the message. Text of an electronic message may be analyzed to detect and process malware content in the electronic message itself. The present technology may analyze an electronic message and attachments to electronic messages to detect a uniform resource location (URL), identify whether the URL is suspicious, and analyze all suspicious URLs to determine if they are malware. The analysis may include re-playing the suspicious URL in a virtual environment which simulates the intended computing device to receive the electronic message. If the re-played URL is determined to be malicious, the malicious URL is added to a black list which is updated throughout the computer system.

Electronic message analysis for malware detection

A system and method are disclosed for network-based file analysis for malware detection. Network content is received from a network tap. A binary packet is identified in the network content. A binary file, including the binary packet, is extracted from the network content. It is determined whether the extracted binary file is detected to be malware.

Network-Based Binary File Extraction and Analysis for Malware Detection

A system and method for providing distributed security of a network. Several device profilers are placed at different locations of a network to assess vulnerabilities from different perspectives. The device profiler identifies the hosts on the network, and characteristics such as operating system and applications running on the hosts. The device profiler traverses a vulnerability tree having nodes representative of characteristics of the hosts, each node having an associated set of potential vulnerabilities. Verification rules can verify the potential vulnerabilities. A centralized correlation server, at a centrally accessible location in the network, stores the determined vulnerabilities of the network and associates the determined vulnerabilities with attack signatures. Traffic monitors access the attack signatures and monitor network traffic for attacks against the determined vulnerabilities.

Network security system having a device profiler communicatively coupled to a traffic monitor

Recent network environment has been rapidly evolved to cloud computing environment based on the development of the Internet technologies. Furthermore there is an increasing demand on mobile cloud computing due to explosive growth of smart devices and wide deployment of LTE-based cellular networks. Thus mobile Desktop-as-a-Service(DaaS) could be a pervasive service for nomadic users. In addition, video streaming traffic is currently more than two-thirds of mobile traffic and ever increasing. All such trends enable that video streaming in mobile DaaS could be an important concern for mobile cloud computing. It should be noted that the performance of the Transmission Control Protocol(TCP) on cloud host servers greatly affects Quality of Service(QoS) of video streams for mobile users. With widely deployed Linux server platforms for cloud computing, in this paper, we conduct an experimental analysis of the twelve Linux TCP variants in mobile DaaS environments. The results of our experiments show that the TCP Illinois outperforms the other TCP variants in terms of wide range of packet loss rate and propagation delay over LTE-based wireless links between cloud servers and mobile devices, even though TCP CUBIC is usually used in default in the current Linux systems.

An Experimental Analysis of Linux TCP Variants for Video Streaming in LTE-based Mobile DaaS Environments

Disclosed herein are a method and apparatus for virtual desktop service. The apparatus includes a connection manager configured to perform an assignment task of assigning a virtual machine to a user terminal using virtual desktop service, a resource pool configured to allocate software resources to a virtual desktop, wherein the software resources include an OS, applications, and user profiles, and a virtual machine infrastructure configured to support hardware resources including a CPU and a memory, wherein the connection manager is configured to perform a coordination task of coordinating a delivery protocol used between the user terminal and servers that provide the virtual desktop service, wherein the resource pool has a management function, wherein the management function is based on usage pattern information about a user's average usage of resources, and wherein the management function uses a physical distance on network from the user terminal to a server.

Providing a virtual desktop service based on physical distance on network from the user terminal and improving network I/O performance based on power consumption

The present invention relates to a method for managing a fault in a smart device. The method comprises the following steps: comparing a value of a fault detection indicator (FDI), indicating faults of at least one performance indicator generated in a smart device, in a normal status with a measured FDI value, and detecting a fault by computing a relative change level; and generating a diagnosis object (DO) including a cause for the detected fault and action against the detected fault, and analyzing the detected fault.

Dae-won Kim

Papers

An Experimental Analysis of Linux TCP Variants for Video Streaming in LTE-based Mobile DaaS Environments

Providing a virtual desktop service based on physical distance on network from the user terminal and improving network I/O performance based on power consumption

Apparatus and method for fault management of smart devices