Visible to the public Biblio

Filters: Keyword is data centers  [Clear All Filters]
2021-07-08
Flores, Hugo, Tran, Vincent, Tang, Bin.  2020.  PAM PAL: Policy-Aware Virtual Machine Migration and Placement in Dynamic Cloud Data Centers. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2549—2558.
We focus on policy-aware data centers (PADCs), wherein virtual machine (VM) traffic traverses a sequence of middleboxes (MBs) for security and performance purposes, and propose two new VM placement and migration problems. We first study PAL: policy-aware virtual machine placement. Given a PADC with a data center policy that communicating VM pairs must satisfy, the goal of PAL is to place the VMs into the PADC to minimize their total communication cost. Due to dynamic traffic loads in PADCs, however, above VM placement may no longer be optimal after some time. We thus study PAM: policy-aware virtual machine migration. Given an existing VM placement in the PADC and dynamic traffic rates among communicating VMs, PAM migrates VMs in order to minimize the total cost of migration and communication of the VM pairs. We design optimal, approximation, and heuristic policyaware VM placement and migration algorithms. Our experiments show that i) VM migration is an effective technique, reducing total communication cost of VM pairs by 25%, ii) our PAL algorithms outperform state-of-the-art VM placement algorithm that is oblivious to data center policies by 40-50%, and iii) our PAM algorithms outperform the only existing policy-aware VM migration scheme by 30%.
Long, Saiqin, Li, Zhetao, Xing, Yun, Tian, Shujuan, Li, Dongsheng, Yu, Rong.  2020.  A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers. :223—230.
{With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.
2021-06-01
Xu, Lei, Gao, Zhimin, Fan, Xinxin, Chen, Lin, Kim, Hanyee, Suh, Taeweon, Shi, Weidong.  2020.  Blockchain Based End-to-End Tracking System for Distributed IoT Intelligence Application Security Enhancement. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1028–1035.
IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
2021-02-16
Mujib, M., Sari, R. F..  2020.  Performance Evaluation of Data Center Network with Network Micro-segmentation. 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). :27—32.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.

2020-12-11
Sabek, I., Chandramouli, B., Minhas, U. F..  2019.  CRA: Enabling Data-Intensive Applications in Containerized Environments. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :1762—1765.
Today, a modern data center hosts a wide variety of applications comprising batch, interactive, machine learning, and streaming applications. In this paper, we factor out the commonalities in a large majority of these applications, into a generic dataflow layer called Common Runtime for Applications (CRA). In parallel, another trend, with containerization technologies (e.g., Docker), has taken a serious hold on cloud-scale data centers, with direct implications on building next generation of data center applications. Container orchestrators (e.g., Kubernetes) have made deployment a lot easy, and they solve many infrastructure level problems, e.g., service discovery, auto-restart, and replication. For best in class performance, there is a need to marry the next generation applications with containerization technologies. To that end, CRA leverages and builds upon the containerization and resource orchestration capabilities of Kubernetes/Docker, and makes it easy to build a wide range of cloud-edge applications on top. To the best of our knowledge, we are the first to present a cloud native runtime for building data center applications. We show the efficiency of CRA through various micro-benchmarking experiments.
2020-12-01
Yang, R., Ouyang, X., Chen, Y., Townend, P., Xu, J..  2018.  Intelligent Resource Scheduling at Scale: A Machine Learning Perspective. 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE). :132—141.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

Zhang, Y., Deng, L., Chen, M., Wang, P..  2018.  Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse? IEEE/ACM Transactions on Networking. 26:1049—1062.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

2020-11-30
Zhou, K., Sun, S., Wang, H., Huang, P., He, X., Lan, R., Li, W., Liu, W., Yang, T..  2019.  Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme. IEEE Transactions on Parallel and Distributed Systems. 30:2033–2045.
Photo service providers are facing critical challenges of dealing with the huge amount of photo storage, typically in a magnitude of billions of photos, while ensuring national-wide or world-wide satisfactory user experiences. Distributed photo caching architecture is widely deployed to meet high performance expectations, where efficient still mysterious caching policies play essential roles. In this work, we present a comprehensive study on internet-scale photo caching algorithms in the case of QQPhoto from Tencent Inc., the largest social network service company in China. We unveil that even advanced cache algorithms can only perform at a similar level as simple baseline algorithms and there still exists a large performance gap between these cache algorithms and the theoretically optimal algorithm due to the complicated access behaviors in such a large multi-tenant environment. We then expound the reasons behind this phenomenon via extensively investigating the characteristics of QQPhoto workloads. Finally, in order to realistically further improve QQPhoto cache efficiency, we propose to incorporate a prefetcher in the cache stack based on the observed immediacy feature that is unique to the QQPhoto workload. The prefetcher proactively prefetches selected photos into cache before they are requested for the first time to eliminate compulsory misses and promote hit ratios. Our extensive evaluation results show that with appropriate prefetching we improve the cache hit ratio by up to 7.4 percent, while reducing the average access latency by 6.9 percent at a marginal cost of 4.14 percent backend network traffic compared to the original system that performs no prefetching.
2020-11-23
Dong, C., Liu, Y., Zhang, Y., Shi, P., Shao, X., Ma, C..  2018.  Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network. 2018 IEEE International Conference on Computational Science and Engineering (CSE). :171–176.
In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.
2020-08-28
Singh, Kuhu, Sajnani, Anil Kumar, Kumar Khatri, Sunil.  2019.  Data Security Enhancement in Cloud Computing Using Multimodel Biometric System. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). :175—179.
Today, data is all around us, every device that has computation power is generating the data and we can assume that in today's world there is about 2 quintillion bytes of data is been generating every day. as data increase in the database of the world servers so as the risk of data leak where we are talking about unlimited confidential data that is available online but as humans are developing their data online so as its security, today we've got hundreds of way to secure out data but not all are very successful or compatible there the big question arises that how to secure our data to hide our all the confidential information online, in other words one's all life work can be found online which is on risk of leak. all that says is today we have cloud above all of our data centers that stores all the information so that one can access anything from anywhere. in this paper we are introducing a new multimodal biometric system that is possible for the future smartphones to be supported where one can upload, download or modify the files using cloud without worrying about the unauthorized access of any third person as this security authentication uses combination of multiple security system available today that are not easy to breach such as DNA encryption which mostly is based on AES cipher here in this paper there we have designed triple layer of security.
2020-07-27
Babay, Amy, Tantillo, Thomas, Aron, Trevor, Platania, Marco, Amir, Yair.  2018.  Network-Attack-Resilient Intrusion-Tolerant SCADA for the Power Grid. 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :255–266.
As key components of the power grid infrastructure, Supervisory Control and Data Acquisition (SCADA) systems are likely to be targeted by nation-state-level attackers willing to invest considerable resources to disrupt the power grid. We present Spire, the first intrusion-tolerant SCADA system that is resilient to both system-level compromises and sophisticated network-level attacks and compromises. We develop a novel architecture that distributes the SCADA system management across three or more active sites to ensure continuous availability in the presence of simultaneous intrusions and network attacks. A wide-area deployment of Spire, using two control centers and two data centers spanning 250 miles, delivered nearly 99.999% of all SCADA updates initiated over a 30-hour period within 100ms. This demonstrates that Spire can meet the latency requirements of SCADA for the power grid.
2020-07-24
Munsyi, Sudarsono, Amang, Harun Al Rasvid, M. Udin.  2018.  An Implementation of Data Exchange in Environmental Monitoring Using Authenticated Attribute-Based Encryption with Revocation. 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC). :359—366.
Internet of things era grown very rapidly in Industrial Revolution 4.0, there are many researchers use the Wireless Sensor Network (WSN) technology to obtain the data for environmental monitoring. The data obtained from WSN will be sent to the Data Center, where users can view and collect all of data from the Data Center using end devices such as personal computer, laptop, and mobile phone. The Data Center would be very dangerous, because everyone can intercept, track and even modify the data. Security requirement to ensure the confidentiality all of stored data in the data center and give the authenticity in data has not changed during the collection process. Ciphertext Policy Attribute-Based Encryption (CP-ABE) can become a solution to secure the confidentiality for all of data. Only users with appropriate rule of policy can get the original data. To guarantee there is no changes during the collection process of the data then require the time stamp digital signature for securing the data integrity. To protect the confidentiality and data integrity, we propose a security mechanism using CP-ABE with user revocation and Time Stamp Digital Signature using Elliptic Curve Cryptography (ECC) 384 bits. Our system can do the revocation for the users who did the illegal access. Our system is not only securing the data but also providing the guarantee that is no changes during the collection process of the data from the Data Center.
2020-07-13
Mahmood, Shah.  2019.  The Anti-Data-Mining (ADM) Framework - Better Privacy on Online Social Networks and Beyond. 2019 IEEE International Conference on Big Data (Big Data). :5780–5788.
The unprecedented and enormous growth of cloud computing, especially online social networks, has resulted in numerous incidents of the loss of users' privacy. In this paper, we provide a framework, based on our anti-data-mining (ADM) principle, to enhance users' privacy against adversaries including: online social networks; search engines; financial terminal providers; ad networks; eavesdropping governments; and other parties who can monitor users' content from the point where the content leaves users' computers to within the data centers of these information accumulators. To achieve this goal, our framework proactively uses the principles of suppression of sensitive data and disinformation. Moreover, we use social-bots in a novel way for enhanced privacy and provide users' with plausible deniability for their photos, audio, and video content uploaded online.
2020-06-29
Blazek, Petr, Gerlich, Tomas, Martinasek, Zdenek.  2019.  Scalable DDoS Mitigation System. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). :617–620.
Distributed Denial of Service attacks (DDoS) are used by attackers for their effectiveness. This type of attack is one of the most devastating attacks in the Internet. Every year, the intensity of DDoS attacks increases and attackers use sophisticated multi-target DDoS attacks. In this paper, a modular system that allows to increase the filtering capacity linearly and allows to protect against the combination of DDoS attacks is designed and implemented. The main motivation for development of the modular filtering system was to find a cheap solution for filtering DDoS attacks with possibility to increase filtering capacity. The proposed system is based on open-source detection and filtration tools.
2020-05-15
Khorsandroo, Sajad, Tosun, Ali Saman.  2018.  Time Inference Attacks on Software Defined Networks: Challenges and Countermeasures. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :342—349.

Through time inference attacks, adversaries fingerprint SDN controllers, estimate switches flow-table size, and perform flow state reconnaissance. In fact, timing a SDN and analyzing its results can expose information which later empowers SDN resource-consumption or saturation attacks. In the real world, however, launching such attacks is not easy. This is due to some challenges attackers may encounter while attacking an actual SDN deployment. These challenges, which are not addressed adequately in the related literature, are investigated in this paper. Accordingly, practical solutions to mitigate such attacks are also proposed. Discussed challenges are clarified by means of conducting extensive experiments on an actual cloud data center testbed. Moreover, mitigation schemes have been implemented and examined in details. Experimental results show that proposed countermeasures effectively block time inference attacks.

2020-05-04
Chaisuriya, Sarayut, Keretho, Somnuk, Sanguanpong, Surasak, Praneetpolgrang, Prasong.  2018.  A Security Architecture Framework for Critical Infrastructure with Ring-based Nested Network Zones. 2018 10th International Conference on Knowledge and Smart Technology (KST). :248–253.
The defense-in-depth approach has been widely recommended for designing critical information infrastructure, however, the lack of holistic design guidelines makes it difficult for many organizations to adopt the concept. Therefore, this paper proposes a holistic architectural framework and guidelines based on ring-based nested network zones for designing such highly secured information systems. This novel security architectural framework and guidelines offer the overall structural design and implementation options for holistically designing the N-tier/shared nothing system architectures. The implementation options, e.g. for the zone's perimeters, are recommended to achieve different capability levels of security or to trade off among different required security attributes. This framework enables the adaptive capability suitable for different real-world contexts. This paper also proposes an attack-hops verification approach as a tool to evaluate the architectural design.
2020-02-18
Quan, Guocong, Tan, Jian, Eryilmaz, Atilla.  2019.  Counterintuitive Characteristics of Optimal Distributed LRU Caching Over Unreliable Channels. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :694–702.
Least-recently-used (LRU) caching and its variants have conventionally been used as a fundamental and critical method to ensure fast and efficient data access in computer and communication systems. Emerging data-intensive applications over unreliable channels, e.g., mobile edge computing and wireless content delivery networks, have imposed new challenges in optimizing LRU caching systems in environments prone to failures. Most existing studies focus on reliable channels, e.g., on wired Web servers and within data centers, which have already yielded good insights with successful algorithms on how to reduce cache miss ratios. Surprisingly, we show that these widely held insights do not necessarily hold true for unreliable channels. We consider a single-hop multi-cache distributed system with data items being dispatched by random hashing. The objective is to achieve efficient cache organization and data placement. The former allocates the total memory space to each of the involved caches. The latter decides data routing strategies and data replication schemes. Analytically we characterize the unreliable LRU caches by explicitly deriving their asymptotic miss probabilities. Based on these results, we optimize the system design. Remarkably, these results sometimes are counterintuitive, differing from the ones obtained for reliable caches. We discover an interesting phenomenon: asymmetric cache organization is optimal even for symmetric channels. Specifically, even when channel unreliability probabilities are equal, allocating the cache spaces unequally can achieve a better performance. We also propose an explicit unequal allocation policy that outperforms the equal allocation. In addition, we prove that splitting the total cache space into separate LRU caches can achieve a lower asymptotic miss probability than resource pooling that organizes the total space in a single LRU cache. These results provide new and even counterintuitive insights that motivate novel designs for caching systems over unreliable channels. They can potentially be exploited to further improve the system performance in real practice.
2019-12-16
Pal, Manjish, Sahu, Prashant, Jaiswal, Shailesh.  2018.  LevelTree: A New Scalable Data Center Networks Topology. 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). :482-486.

In recent time it has become very crucial for the data center networks (DCN) to broaden the system limit to be able to meet with the increasing need of cloud based applications. A decent DCN topology must comprise of numerous properties for low diameter, high bisection bandwidth, ease of organization and so on. In addition, a DCN topology should depict aptness in failure resiliency, scalability, construction and routing. In this paper, we introduce a new Data Center Network topology termed LevelTree built up with several modules grows as a tree topology and each module is constructed from a complete graph. LevelTree demonstrates great topological properties and it beats critical topologies like Jellyfish, VolvoxDC, and Fattree regarding providing a superior worthwhile plan with greater capacity.

2019-03-22
Liu, Y., Li, X., Xiao, L..  2018.  Service Oriented Resilience Strategy for Cloud Data Center. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :269-274.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

2019-01-21
Dixit, Vaibhav Hemant, Kyung, Sukwha, Zhao, Ziming, Doupé, Adam, Shoshitaishvili, Yan, Ahn, Gail-Joon.  2018.  Challenges and Preparedness of SDN-based Firewalls. Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization. :33–38.

Software-Defined Network (SDN) is a novel architecture created to address the issues of traditional and vertically integrated networks. To increase cost-effectiveness and enable logical control, SDN provides high programmability and centralized view of the network through separation of network traffic delivery (the "data plane") from network configuration (the "control plane"). SDN controllers and related protocols are rapidly evolving to address the demands for scaling in complex enterprise networks. Because of the evolution of modern SDN technologies, production networks employing SDN are prone to several security vulnerabilities. The rate at which SDN frameworks are evolving continues to overtake attempts to address their security issues. According to our study, existing defense mechanisms, particularly SDN-based firewalls, face new and SDN-specific challenges in successfully enforcing security policies in the underlying network. In this paper, we identify problems associated with SDN-based firewalls, such as ambiguous flow path calculations and poor scalability in large networks. We survey existing SDN-based firewall designs and their shortcomings in protecting a dynamically scaling network like a data center. We extend our study by evaluating one such SDN-specific security solution called FlowGuard, and identifying new attack vectors and vulnerabilities. We also present corresponding threat detection techniques and respective mitigation strategies.

2018-12-10
Maas, Martin, Asanović, Krste, Kubiatowicz, John.  2017.  Return of the Runtimes: Rethinking the Language Runtime System for the Cloud 3.0 Era. Proceedings of the 16th Workshop on Hot Topics in Operating Systems. :138–143.
The public cloud is moving to a Platform-as-a-Service model where services such as data management, machine learning or image classification are provided by the cloud operator while applications are written in high-level languages and leverage these services. Managed languages such as Java, Python or Scala are widely used in this setting. However, while these languages can increase productivity, they are often associated with problems such as unpredictable garbage collection pauses or warm-up overheads. We argue that the reason for these problems is that current language runtime systems were not initially designed for the cloud setting. To address this, we propose seven tenets for designing future language runtime systems for cloud data centers. We then outline the design of a general substrate for building such runtime systems, based on these seven tenets.
2018-05-09
Navid, W., Bhutta, M. N. M..  2017.  Detection and mitigation of Denial of Service (DoS) attacks using performance aware Software Defined Networking (SDN). 2017 International Conference on Information and Communication Technologies (ICICT). :47–57.

Software Defined Networking (SDN) stands to transmute our modern networks and data centers, opening them up into highly agile frameworks that can be reconfigured depending on the requirement. Denial of Service (DoS) attacks are considered as one of the most destructive attacks. This paper, is about DoS attack detection and mitigation using SDN. DoS attack can minimize the bandwidth utilization, leaving the network unavailable for legitimate traffic. To provide a solution to the problem, concept of performance aware Software Defined Networking is used which involves real time network monitoring using sFlow as a visibility protocol. So, OpenFlow along with sFlow is used as an application to fight DoS attacks. Our analysis and results demonstrate that using this technique, DoS attacks are successfully defended implying that SDN has promising potential to detect and mitigate DoS attacks.

2018-03-19
Mehta, N. P., Sahai, A. K..  2017.  Internet of Things: Raging Devices and Standardization in Low-Powered Protocols. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). :1–5.

This paper addresses the need for standard communication protocols for IoT devices with limited power and computational capabilities. The world is rapidly changing with the proliferation and deployment of IoT devices. This will bring in new communication challenges as these devices are connected to Internet and need to communicate with each other in real time. The paper provides an overview of IoT system architecture and the forthcoming challenges it will bring. There is an urging need to establish standards for communication in the IoT world. With the recent development of new protocols like CoAP, 6LowPAN, IEEE 802.15.4 and Thread in different layers of OSI model, additional challenges also present themselves. Performance and data management is becoming more critical than ever before due to the complexity of connecting raging number of IoT devices. The performance of the systems dealing with IoT devices will require appropriate capacity planning the associated development of data centers. Finally, the paper also presents some reasonable approaches to address the above issues in the IoT world.

2018-01-16
Ba-Hutair, M. N., Kamel, I..  2016.  A New Scheme for Protecting the Privacy and Integrity of Spatial Data on the Cloud. 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). :394–397.

As the amount of spatial data gets bigger, organizations realized that it is cheaper and more flexible to keep their data on the Cloud rather than to establish and maintain in-house huge data centers. Though this saves a lot for IT costs, organizations are still concerned about the privacy and security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the data set, which is impractical. In this paper, we propose a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. The proposed technique suggests a new index structure to support answering range query over encrypted data set. The proposed indexing scheme is based on the Z-curve. The paper describes a distributed algorithm for answering range queries over spatial data stored on the Cloud. We carried many simulation experiments to measure the performance of the proposed scheme. The experimental results show that the proposed scheme outperforms the most recent schemes by Kim et al. in terms of data redundancy.

Ghutugade, K. B., Patil, G. A..  2016.  Privacy preserving auditing for shared data in cloud. 2016 International Conference on Computing, Analytics and Security Trends (CAST). :300–305.

Cloud computing, often referred to as simply “the cloud,” is the delivery of on-demand computing resources; everything from applications to data centers over the Internet. Cloud is used not only for storing data, but also the stored data can be shared by multiple users. Due to this, the integrity of cloud data is subject to doubt. Every time it is not possible for user to download all data and verify integrity, so proposed system contain Third Party Auditor (TPA) to verify the integrity of shared data. During auditing, the shared data is kept private from public verifiers, who are able to verify shared data integrity without downloading or retrieving the entire data file. Group signature is used to preserve identity privacy of group members from third party auditor. Privacy preserving is done to ensure that the TPA cannot derive user's data content from the information collected during the auditing process.