Finding Cyber Threats With Big Data Analytics

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Traffic on optical transport networks is growing exponentially, leaving cyber intelligence agencies in charge of monitoring these networks with the unenviable task of trying to sift through ever-increasing amounts of data to search for cyber threats. However, new technologies capable of filtering exploding volumes of real-time traffic are being embedded within emerging network monitoring applications supporting big data and analytics capabilities. Waves of change are washing through long-haul transport networks because of emerging optical transmission technologies. Innovative optical coherent signaling techniques have expanded individual optical wavelength capacity from 10 gigabits per second to 100G, opening the door for carriers to deploy transport equipment capable of 400 gigabits per second. Of course, this model offers scalability for the future because it can be deployed directly to the existing fiber base to avoid the exorbitant cost of laying new fiber optic cables.


IoT Network Data #Analytics @ThingsExpo #BigData #AI #IoT #IIoT #API

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While the focus and objectives of IoT initiatives are many and diverse, they all share a few common attributes, and one of those is the network. Commonly, that network includes the Internet, over which there isn't any real control for performance and availability. The current state of the art for Big Data analytics, as applied to network telemetry, offers new opportunities for improving and assuring operational integrity. In his session at @ThingsExpo, Jim Frey, Vice President of Strategic Alliances at Kentik, discussed tactics and tools to bridge the gap between IoT project teams and the network planning and operations functions that play a significant role in project success. Speaker Bio: Jim Frey is Vice President of Strategic Alliances at Network Traffic Intelligence company Kentik.


[slides] IoT Network Data #Analytics @ThingsExpo #BigData #IoT #AI #DX

#artificialintelligence

While the focus and objectives of IoT initiatives are many and diverse, they all share a few common attributes, and one of those is the network. Commonly, that network includes the Internet, over which there isn't any real control for performance and availability. The current state of the art for Big Data analytics, as applied to network telemetry, offers new opportunities for improving and assuring operational integrity. In his session at @ThingsExpo, Jim Frey, Vice President of Strategic Alliances at Kentik, discussed tactics and tools to bridge the gap between IoT project teams and the network planning and operations functions that play a significant role in project success. Speaker Bio: Jim Frey is Vice President of Strategic Alliances at Network Traffic Intelligence company Kentik.


Streaming Telemetry: Unleashing Big Data's Power in Network Management

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Dr. Danish Rafique is Senior Innovation Manager at ADVA. It may sound counterintuitive at first, since it's the telecom operators who sit over much larger data lakes, so what gives? While applications drive cloud service providers' core business success, it's the unequivocal access, smart consumption and intelligent processing of the underlying data center infrastructure―be it a physical or virtual resource―that sets them apart. With current business challenges and increasing traffic requirements, the boundaries are starting to blur between the two network segments. On one hand, traditional operators are aiming to run service-centric and app-driven business on top of their platforms, whereas cloud data centre infrastructures are expanding to metro and core connectivity solutions.


Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

arXiv.org Machine Learning

In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g., trying a suboptimal network configuration). Therefore, intuitively, we could explore more when the load is low and exploit more when the load is high. Inspired by this intuition, we propose an Adaptive Upper-Confidence-Bound (AdaUCB) algorithm to adaptively balance the exploration-exploitation tradeoff for opportunistic bandits. We prove that AdaUCB achieves $O(\log T)$ regret with a smaller coefficient than the traditional UCB algorithm. Furthermore, AdaUCB achieves $O(1)$ regret when the exploration cost is zero if the load level is below a certain threshold. Last, based on both synthetic data and real-world traces, experimental results show that AdaUCB significantly outperforms other bandit algorithms, such as UCB and TS (Thompson Sampling), under large load fluctuations.