Client Machine Research Articles

A BitTorrent Mechanism-Based Solution for Massive System Deployment

System deployment of a computer environment plays a critical role in the daily administration of computer systems, and tasks of massive deployments may take a lot of time for data center administrator(s). The existing solutions for massive deployment normally consist of storage spaces and extra computer servers for running deployment services. Existing solutions of multicast massive deployment are not robust because the overall deployment performance will worsen if one client machine fails. Because of the limitation of the network protocol, a multicast solution is not scalable. Although scholars have proposed solutions based on BitTorrent (BT) to overcome performance and scalability problems, solutions are not good enough because they still require the storage space to save the image file. In this paper, we present a novel mechanism of massive deployment called “BT deployment mechanism from the raw device” (BDMfRD), which differs from conventional solutions in that it avoids creating any image file in the deployment process or using external storage for it. The proposed solution was verified by conducting 10 experiments to replicate the 50 GB system of the source machine to 1–32 destination computers. Experimental results showed that the proposed method reduced the total time for deploying 32 computers by 45.289%. The implemented software is the first massive deployment solution that provides light, robust, efficient, and scalable capabilities simultaneously.

Mobile Cloud computing architecture for massively parallelizable geometric computation

Cloud Computing is one of the most disruptive technologies of this century. This technology has been widely adopted in many areas of the society. In the field of manufacturing industry, it can be used to provide advantages in the execution of the complex geometric computation algorithms involved on CAD/CAM processes. The idea proposed in this research consists in outsourcing part of the load to be computed in the client machines to the cloud through the Mobile Cloud Computing paradigm. This practice gives substantial benefits to both the clients and the software-provider in terms of costs, flexibility, ubiquity and performance. In this document, an outsourcing architecture is proposed based on this paradigm. Extensive experiments have been done using highly parallelizable computational geometry operations to show the strengths and weaknesses of the proposal in combination of specialized computing platforms in the cloud. The results suggest that there are some issues that affect the overall performance and the stability of the QoS: the network communication delay, and the number of simultaneous clients and multiple requests. Some solutions have been proposed to face these challenges.

Deep learning to combat phishing

With the tremendous increase in our dependence on online services for daily needs, cyber threats and related crime have also increased at an alarming rate. Phishing is one of the most common cyber threats used these days to steal secure information like personal details and financial credentials that are often misused for impersonation and committing cyber fraud. Although numerous solutions exist for detection and prevention from phishing scenarios, no solution can combat phishing attacks in real-time efficiently. This paper is focussed on studying the real-time security aspects of providing safe online services to every user. The primary aim of this paper is to propose an anti-phishing framework that could be implemented by Internet Security Providers (ISPs) to handle phishing attacks at their own level and providing secure connections to end users irrespective of the client machine configurations. The proposed framework utilizes the advantages of deep learning classification implemented as the backend for identifying phishing websites at the end of ISPs. This approach is capable of handling phishing attacks efficiently as compared to other client-specific approaches as the ISPs proactively can validate and flag the new domains added for HTTP/HTTPS as well as SMTP and prevent millions of users from getting affected.

Cloud Based Reasoning Health Data using Homomorphic Encryption

Cloud computing is an abundant heterogeneous paradigm. The clients are given access to cloud for storing large amount of data for many purposes. The major cloud security issues are data breaches, insider threat and insufficient due diligence etc. Most of the service providers save the Client data as a plain text format which makes the data less secured. Aim of the system is to protect the health data that are outsourced for storing in cloud. In this system, the data is encrypted using paillier cryptosystem before outsourcing, which preserves the privacy of patient’s health data. Computations are performed over this encrypted data using decision tree algorithm. The results are displayed on the client machine. Hence, it ensures the privacy preservation and cautions the patient about his health.

SERVER LOAD BALANCING USING LOAD REBALANCING ALGORITHM

Distributed systems are the building block for cloud computing applications. In which the data is stored on a server and accessed as if it is stored on the local client machine. Our proposal is to allocate files uniformly as possible. Such that node has larger number of chunks by load rebalancing algorithm.

Malicious Threats Detection of Executable File

Malware is a general problems faced in the present day. Malware is a file that may be on the client machine. Malware can root an uncorrectable risk to the safety and protection of personal workstation clients as an expansion in the spiteful threats. In this paper explain a malware threats detection using data mining and machine learning. Malware detection algorithms with machine learning approach and data file. Also explained break executable files, create instruction set and take a look at different machine learning and data mining algorithm for feature extraction, reduction for detection of malware. In the system precisely distinguishes both new and known malware occurrences even though the double distinction among malware and real software is ordinarily little. There is a demand to present a skeleton which can come across latest, malicious executable files.

High-performance method for identification of super enhancers from ChIP-Seq data with configurable cloud virtual machines

A universal method for rapid identifying super-enhancers which are large domains of multiple closely-spaced enhancers is proposed. The method applies configurable cloud virtual machines (cVMs) and the rank-ordering of super-enhancers (ROSE) algorithm. To identify super-enhancers a сVM-based analysis of the ChIP-seq binding patterns of the active enhancer-associated mark is employed. The use of the proposed method is described step-by-step: configuration of cVM; ChIP-seq data alignment; peak calling; ROSE algorithm; interpretation of the results on a client machine. The method was validated for the search of super-enhancers using the H3K27ac mark in the sample datasets of a cell line (human MCF-7), mouse tissue (heart), and human tissue (adrenal gland). The total analysis cycle time of raw ChIP-seq data ranges from 15 to 48 min, depending on the number of initial short reads. Depending on the data processing step and availability of multi-threading, a cVM can be scaled up to a multi-CPU configuration with large amount of RAM. An important feature of the method is that it can run on a client machine that has low-performance with virtually any OS. The proposed method allows for simultaneous and independent processing of different sample datasets on multiple clones of a single cVM.•Cloud VMs were used for rapid processing of ChIP-seq data to identify super-enhancers.•The method can use a low-performance computer with virtually any OS on it.•It can be scaled up for parallel processing of individual sample datasets on their own VMs for rapid high-throughput processing.

Energy saving mechanism in fog assisted IoT enabled health monitoring framework

This proposed research work discusses fog assisted IoT enabled health monitoring system performance. This system support local processing nearer the client machine for faster service and removing redundant data. This architecture is suitable for heavy traffic health data monitoring system like old or child monitoring or fitness care monitoring system. After processing huge data, only filtering data will be forwarded to cloud for processing. To transfer the huge amount of health monitoring data form IoT layer to Data Management and Processing Layer (DMPL) consume lots of energy. To minimize the energy requirement in the fog assisted DMPL layer, we adopt sleep mode operation and batch transferring data packet transfer technology. This paper studies the effect of sleeping mode on mean delay, number of data packets in the buffer and probability of blocking. The results show that proposed approach saves energy and effective framework for fog assisted health monitoring framework.

User-Oriented Augmented Reality Content Delivery and Management for Ubiquitous Manufacturing

Abstract Ubiquitous manufacturing provides users with advanced control over the physical manufacturing environment. Ubiquitous augmented reality (UAR) interface enables direct manipulation in an environment containing smart objects. This paper describes a framework for implementing augmented reality (AR) in a ubiquitous manufacturing environment. The proposed system makes use of real-time data captured from client machines to update and manage AR contents. Ubiquitous augmented reality is utilized as a user interface for the client users to receive instructions and guidance from the system and to update the system on task parameters. A review of related work, and the framework, methodology, and implementation of the proposed system, and a case study are presented in this paper to demonstrate the UAR content delivery and update.

Detecting TCP/IP Connections via IPID Hash Collisions

Abstract We present a novel attack for detecting the presence of an active TCP connection between a remote Linux server and an arbitrary client machine. The attack takes advantage of side-channels present in the Linux kernel’s handling of the values used to populate an IPv4 packet’s IPID field and applies to kernel versions of 4.0 and higher. We implement and test this attack and evaluate its real world effectiveness and performance when used on active connections to popular web servers. Our evaluation shows that the attack is capable of correctly detecting the IP-port 4-tuple representing an active TCP connection in 84% of our mock attacks. We also demonstrate how the attack can be used by the middle onion router in a Tor circuit to test whether a given client is connected to the guard entry node associated with a given circuit. In addition we discuss the potential issues an attacker would face when attempting to scale it to real world attacks, as well as possible mitigations against the attack. Our attack does not exhaust any global resource, and therefore challenges the notion that there is a direct one-to-one connection between shared, limited resources and non-trivial network side-channels. This means that simply enumerating global shared resources and considering the ways in which they can be exhausted will not suffice for certifying a kernel TCP/IP network stack to be free of privacy risk side-channels.

A Framework for Developing Measurement Systems Based on Software Metrics Using SMMT

In this research paper Software Metrics Monitoring Tool (SMMT) being developed to be a master of all applications. Software testing is really requisite to direct out the defects and observe the bugs that were made during the development phases, which will occur in development or maintenance. It is important to ensure that the application should not result into any defects or failures. Because, in future application may be very expensive or in the later stages of the development. This is required one to stay in all kind of the business. Through software testing tools we can proceeds software implementation and maintenance with error free way. SMMT generated to reduce 24/7 support for applications and auto error recovering process. This TOOL has a circle method for monitoring running application in all aspects from executing application, retrieving request, response, queue, service provider status, alternative procedures; trigger generation, server issues, client machine performance and finding errors. In this paper we can get solution for software maintainability, engineering approaches, software metrics, reduce demand for functionality, quality, flexibility and to cost effectiveness.

Remote access protocols for Desktop-as-a-Service solutions.

The use of remote desktop services on virtualized machines is a general trend to reduce the cost of desktop seats. Instead of assigning a physical machine with its operating system and software to each user, it is considerably easier to manage a light client machine that connects to a server where the instance of the user’s desktop machine actually executes. Citrix and VMware have been major suppliers of these systems in private clouds. Desktop-as-a-Service solutions such as Amazon WorkSpaces offer a similar functionality, yet in a public cloud environment. In this paper, we review the main offerings of remote desktop protocols for a cloud deployment. We evaluate the necessary network resources using a traffic model based on self-similar processes. We also evaluate the quality of experience perceived by the user, in terms of image quality and interactivity, providing values of Mean Opinion Score (MOS). The results confirm that the type of application running on the remote servers and the mix of users must be considered to determine the bandwidth requirements. Applications such as web browsing result in unexpectedly high traffic rates and long bursts, more than the case of desktop video playing, because the on-page animations are rendered on the server.

Experience

We present lessons learned related to data collection and analysis from 5 years of experience with the eTextbook system OpenDSA. The use of such cyberlearning systems is expanding rapidly in both formal and informal educational settings. Although the precise issues related to any such project are idiosyncratic based on the data collection technology and goals of the project, certain types of data collection problems will be common. We begin by describing the nature of the data transmitted between the student’s client machine and the database server, and our initial database schema for storing interaction log data. We describe many problems that we encountered, with the nature of the problems categorized as syntactic-level data collection issues, issues with relating events to users, or issues with tracking users over time. Relating events to users and tracking the time spent on tasks are both prerequisites to converting syntactic-level interaction streams to semantic-level behavior needed for higher-order analysis of the data. Finally, we describe changes made to our database schema that helped to resolve many of the issues that we had encountered. These changes help advance our ultimate goal of encouraging a change from ineffective learning behavior by students to more productive behavior.

A Prefetching Technique using Apriori and Logistic Methods for the DFS in Cloud

This paper presents an creativity data prefetching scheme on the loading servers in distributed file systems for cloud computing. The server will get and piggybacked the frequent data from the client system, after analyzing the fetched data is forward to the client machine from the server. To place this technique to work, the data about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, dual prediction algorithms have been proposed to calculation future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetching data can be pressed to the relevant client device from the storage server. Over a series of evaluation experiments with a group of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud are not answerable for predicting I/O access operations, which can certainly contribute to preferable system performance on them.

Emphasizing on Various Security Issues in Cloud Forensic Framework

Objectives: To provide a competent secure framework for the cloud forensics system with enhancement of security in various phases of forensics investigation method. Methods/Statistical analysis: The various issues has been raised in above study about existing cloud forensics framework as security concern has been discussed during the evidence identification and collection, during acquisition, during preservation and during analysis and reporting. The study unveils about the requirement of various security parameters during the entire chain of custody in cloud system. Findings: The major finding is related to security concern in various phases. Coming towards the initial stage of identification and collection, it is not easy to execute these tasks. Since cloud data is stored in multiple data center and using conventional method for identification of evidence is a tedious job. Because of cloud distributing nature it is not easy to collect all evidence from these servers, as thousands of servers are running globally across data center and securing this evidence is the major task. After that level key challenges is locations of data centers and data acquisition from a large data set. The remote location of data centers and client machine may require stateless communication. Predictable time duration for data acquisition from remote data centers in comparison with local computer demoralize the investigation process and make it overwhelming for a moment of time. Furthermore, erroneous evidence acquisition not only wastes investigation effort but may lead the complete investigation into the wrong manner. After that concern is about log file format, analyzing logs is challenge due to unification issue. Also at time of reporting main challenge occur for choosing the right court of law; Cross boarder verification is a big issue during investigation, and then exchanging data between two countries needs more privacy. Application/Improvements: Strong secrecy for evidence identification and collection, with enhanced privacy level and improved chain of custody method. Recovery of data in cloud can be faster, which will help in disaster recovery also due to a unified format. And finally appropriate secure integrated framework may lead to resolve cases quickly.

A new architecture of cloud computing to enhance the load balancing

One of the emerging trends in IT environment is the cloud computing with various requirements and huge resources. Therefore, efficient load balancing guarantees an efficient resource utilisation to cope with the increasing users' demands. Consequently, this paper proposes a new architecture in a cloud environment for load balancing. The proposed architecture taking advantage of the client's machines resources for distributing the cloud tasks among the clients' machines rather than relying only on datacenters. The proposed architecture has been implemented using the cloudAnalyst simulator. The simulation results show an efficient load balancing through minimising the computational load on the cloud servers and hence, improve the overall cloud performance compared to the traditional architectures.

Implementation of Advanced Encryption Standard Algorithm with Key Length of 256 Bits for Preventing Data Loss in an Organization

Data and network security is one among the foremost necessary factors in today’s business world. In recent, businesses and firms like financial institutions, law firms, schools, health sectors, telecommunications, mining and a number of government agencies want a strategic security technique of managing its data. Organizations managing bigger monetary information, bio-data and alternative relevant info are losing its valuable information or data at rest, in usage or in motion to unauthorized parties or competitors as a result of activities of hackers. Organizations are losing millions of dollars as a result of unprotected data that gets into the hands of malicious persons. Data protection in an organization has become vital in today’s business. In order to possess secure information, this information should be protected in order that although malicious persons get access to the info, it becomes wealthless and useless to them. Advanced Encryption Standard (AES), could be a scientific discipline rule that may be used for secured data and communication in an organization, it uses same key that's isobilateral key for transmission additionally as reception. The AES rule is capable of using cryptographic keys of 128, 192, and 256 bits, this paper implement AES block cipher of 256-bits and 256-bit key size, developed with C# as a front-end client machine and MS SQL used for the database as a back-end machine.

Round-Trip Delay Estimation in OPC UA Server-Client Communication Channel

In this paper an estimation of round-trip delay (RTD) in OPC UA server-client channel was investigated in various data communication networks including Ethernet, WiFi, and 3G. Testing was carried out using the developed IoT gateway device running OPC UA server and remote computer running OPC UA client. The server and the client machines were configured to operate in Virtual Private Network powered by OpenVPN. Experimental analysis revealed that RTD values are distributed in the wide range exhibiting difficult-to-explain outliers significantly exceeding average RTD value. A preliminary exploration of the correlation between instantaneous load of communication gateway processor and RTD peaks was carried out on ARM Cortex A8 Texas instruments processors running at 600 MHz and 800 MHz clock frequency.DOI: http://dx.doi.org/10.5755/j01.eie.22.6.17229

Inhibiting and Detecting Offline Password Cracking Using ErsatzPasswords

In this work, we present a simple, yet effective and practical scheme to improve the security of stored password hashes, increasing the difficulty to crack passwords and exposing cracking attempts. We utilize a hardware-dependent function (HDF), such as a physically unclonable function (PUF) or a hardware security module (HSM), at the authentication server to inhibit offline password discovery. Additionally, a deception mechanism is incorporated to alert administrators of cracking attempts. Using an HDF to generate password hashes hinders attackers from recovering the true passwords without constant access to the HDF. Our scheme can integrate with legacy systems without needing additional servers, changing the structure of the hashed password file, nor modifying client machines. When using our scheme, the structure of the hashed passwords file, e.g., etc/shadow or etc/master.passwd , will appear no different than traditional hashed password files. 1 However, when attackers exfiltrate the hashed password file and attempt to crack it, the passwords they will receive are ErsatzPasswords—“fake passwords.” The ErsatzPasswords scheme is flexible by design, enabling it to be integrated into existing authentication systems without changes to user experience. The proposed scheme is integrated into the pam_unix module as well as two client/server authentication schemes: Lightweight Directory Access Protocol (LDAP) authentication and the Pythia pseudorandom function (PRF) Service [Everspaugh et al. 2015]. The core library to support ErsatzPasswords written in C and Python consists of 255 and 103 lines of code, respectively. The integration of ErsatzPasswords into each explored authentication system required less than 100 lines of additional code. Experimental evaluation of ErsatzPasswords shows an increase in authentication latency on the order of 100ms, which maybe acceptable for real world systems. We also describe a framework for implementing ErsatzPasswords using a Trusted Platform Module (TPM).

Prefetching on Storage Servers through Mining Access Patterns on Blocks

Distributed file systems have been widely deployed as back-end storage systems to offer I/O services for parallel/distributed applications that process large amounts of data. Data prefetching in distributed file systems is a well-known optimization technique which can mask both network and disk latency and consequently boost I/O performance. Traditionally, data prefetching is initiated by the client file systems, however, conventional prefetching schemes are not well suited for client machines that have limited memory and computing capacity. To offer an efficient prefetching approach for resource-limited client machines, this paper proposes a novel server-side prefetching mechanism. Specifically, we propose to piggyback client identification to I/O requests so that server side block access history can be put into context. On the server side, we utilize the horizontal visibility graph technique to transform per-client time series of block access sequences into a connected graph for which we employ Tarjan’s algorithm to disclose cut points in the connected graph. We express these patterns with feature tuples and we propose the X -step pattern matching algorithm to find a matching access pattern (i.e., a feature tuple) for a given block access history. Experimental results indicate that our newly proposed prefetching mechanism can ease client machines and their applications from the process of data prefetching, boosting client performance accordingly, and that it yields an attractive increase in data throughput as well.