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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.
Ajay, K, Bharath, B, Akhil, M V, Akanksh, R, Hemavathi, P.  2018.  Intellectual Property Management Using Blockchain. 2018 3rd International Conference on Inventive Computation Technologies (ICICT). :428—430.

With the advent of blockchain technology, multiple avenues of use are being explored. The immutability and security afforded by blockchain are the key aspects of exploitation. Extending this to legal contracts involving digital intellectual properties provides a way to overcome the use of antiquated paperwork to handle digital assets.

Iuhasz, Gabriel, Petcu, Dana.  2019.  Perspectives on Anomaly and Event Detection in Exascale Systems. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :225–229.
The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time. In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.
Kanewala, Thejaka Amila, Zalewski, Marcin, Lumsdaine, Andrew.  2018.  Distributed, Shared-Memory Parallel Triangle Counting. Proceedings of the Platform for Advanced Scientific Computing Conference. :5:1–5:12.
Triangles are the most basic non-trivial subgraphs. Triangle counting is used in a number of different applications, including social network mining, cyber security, and spam detection. In general, triangle counting algorithms are readily parallelizable, but when implemented in distributed, shared-memory, their performance is poor due to high communication, imbalance of work, and the difficulty of exploiting locality available in shared memory. In this paper, we discuss four different (but related) triangle counting algorithms and how their performance can be improved in distributed, shared-memory by reducing in-node load imbalance, improving cache utilization, minimizing network overhead, and minimizing algorithmic work. We generalize the four different triangle counting algorithms into a common framework and show that for all four algorithms the in-node load imbalance can be minimized while utilizing caches by partitioning work into blocks of vertices, the network overhead can be minimized by aggregation of blocks of work, and algorithm work can be reduced by partitioning vertex neighbors by degree. We experimentally evaluate the weak and the strong scaling performance of the proposed algorithms with two types of synthetic graph inputs and three real-world graph inputs. We also compare the performance of our implementations with the distributed, shared-memory triangle counting algorithms available in PowerGraph-GraphLab and show that our proposed algorithms outperform those algorithms, both in terms of space and time.
Villalobos, J. J., Rodero, Ivan, Parashar, Manish.  2017.  An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics. Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. :103–112.

A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.

Haq, E. U., Xu, H., Pan, L., Khattak, M. I..  2017.  Smart Grid Security: Threats and Solutions. 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). :188–193.

the terms Smart grid, IntelliGrid, and secure astute grid are being used today to describe technologies that automatically and expeditiously (separate far from others) faults, renovate potency, monitor demand, and maintain and recuperate (firm and steady nature/lasting nature/vigor) for more reliable generation, transmission, and distribution of electric potency. In general, the terms describe the utilization of microprocessor-predicated astute electronic contrivances (IEDs) communicating with one another to consummate tasks afore now done by humans or left undone. These IEDs watch/ notice/ celebrate/ comply with the state of the puissance system, make edified decisions, and then take action to preserve the (firm and steady nature/lasting nature/vigor) and performance of the grid. Technology use/military accommodation in the home will sanction end users to manage their consumption predicated on their own predilections. In order to manage their consumption or the injuctive authorization placed on the grid, people (who utilize a product or accommodation) need information and an (able to transmute and get better) power distribution system. The astute grid is an accumulation of information sources and the automatic control system that manages the distribution of puissance, understands the transmutations in demand, and reacts to it by managing demand replication. Different billing (prosperity plans/ways of reaching goals) for mutable time and type of avail, as well as conservation and use or sale of distributed utilizable things/valuable supplies, will become part of perspicacious solutions. The traditional electrical power grid is currently evolving into the perspicacious grid. Perspicacious grid integrates the traditional electrical power grid with information and communication technologies (ICT). Such integration empowers the electrical utilities providers and consumers, amends the efficiency and the availability of the puissance system while perpetually monitoring, - ontrolling and managing the authoritative ordinances of customers. A keenly intellective grid is an astronomically immense intricate network composed of millions of contrivances and entities connected with each other. Such a massive network comes with many security concerns and susceptibilities. In this paper, we survey the latest on keenly intellective grid security. We highlight the involution of the keenly intellective grid network and discuss the susceptibilities concrete to this sizably voluminous heterogeneous network. We discuss then the challenges that subsist in securing the keenly intellective grid network and how the current security solutions applied for IT networks are not adequate to secure astute grid networks. We conclude by over viewing the current and needed security solutions for the keenly intellective gird.

Zhao, Dexin, Ma, Zhen, Zhang, Degan.  2016.  A Distributed and Adaptive Trust Evaluation Algorithm for MANET. Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :47–54.

We propose a distributed and adaptive trust evaluation algorithm (DATEA) to calculate the trust between nodes. First, calculate the communication trust by using the number of data packets between nodes, and predict the trust based on the trend of this value, calculate the comprehensive trust by combining the history trust with the predict value; calculate the energy trust based on the residual energy of nodes; calculate the direct trust by using the communication trust and energy trust. Second, calculate the recommendation trust based on the recommendation reliability and the recommendation familiarity; put forward the adaptively weighting method, and calculate the integrate direct trust by combining the direct trust with recommendation trust. Third, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. Simulation experiments show that the proposed algorithm can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

Dong Jin, Illinois Institute of Technology, David Nicol, University of Illinois at Urbana-Champaign.  2015.  Parallel Simulation and Virtual-machine-based Emulation of Software-defined Network. ACM Transactions on Modeling and Computer Simulation. 26(1)

The emerging software-defined networking (SDN) technology decouples the control plane from the data plane in a computer network with open and standardized interfaces, and hence opens up the network designers’ options and ability to innovate. The wide adoption of SDN in industry has motivated the development of large-scale, high-fidelity testbeds for evaluation of systems that incorporate SDN. In this article, we develop a framework to support OpenFlow-based SDN simulation and distributed emulation, by leveraging our prior work on a hybrid network testbed with a parallel network simulator and a virtual-machine-based emulation system. We show how to exploit typical SDN controller behaviors to handle performance issues caused by the centralized controller in parallel discrete-event simulation. In particular, we develop an asynchronous synchronization algorithm for passive SDN controllers and design a two-level architecture for active SDN controllers. We evaluate the system performance, showing good scalability. Finally, we present a case study, using the testbed, to evaluate network verification applications in an SDN-based data center network. CCS Concepts: Networks→Network simulations; Computing methodologies→Simulation