Cyber Security



Computationally Efficient CP Tensor Decomposition Update Framework for Emerging Component Discovery in Streaming Data



Publication Source: 2018 IEEE High Performance Extreme Computing Conference (HPEC '18), Waltham, MA, USA [Best Paper Award]

We present streaming CP update, an algorithmic framework for updating CP tensor decompositions that possesses the capability of identifying emerging components and can produce decompositions of large, sparse tensors streaming along multiple modes at a low computational cost. We discuss a large-scale implementation of the proposed scheme integrated within the ENSIGN tensor analysis package, and we evaluate and demonstrate the performance of the framework, in terms of computational efficiency and capability to discover emerging components, on a real cyber dataset.

Accelerating Dijkstra's Algorithm Using Multiresolution Priority Queues



Publication Source: 2018 IEEE High Performance Extreme Computing Conference (HPEC '18), Waltham, MA, USA

Multiresolution priority queues are data structures recently discovered by Reservoir Labs that reduce the entropy of some critical graph algorithms—such as Dijkstra’s or Prim’s algorithms—and deliver new lower computational complexity bounds. These new data structures are capable of exploiting the multiresolution properties of discrete algorithms, a characteristic that has been otherwise overlooked in the field of graph algorithms. Similar to the concept of resolution found in signal processing—by which a signal can be undersampled while information loss is zero or very small—graphs’ entropy tends to be concentrated in regions that can be efficiently exploited by multiresolution data structures. In this approach, a small controllable bounded discrete error is introduced in a way that entropy is substantially reduced, resulting in new lower computational complexity algorithms.

While the fastest currently known graph algorithms provide exact solutions at the expense of incurring high computational costs, a multiresolution graph algorithm is capable of softening graph problems and breaking their current information theoretic barriers, introducing a small amount of controlled error in a way that the problem’s entropy is reduced. As a result, a new class of higher performance graph algorithms is enabled, enabling the solution of previously deemed intractable problems by identifying solutions that are close to optimal and within a known bounded error.


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High Speed Elephant Flow Detection Under Partial Information



Publication Source: 2018 IEEE International Symposium on Networks, Computers and Communications

In this paper we introduce a new framework to detect elephant flows at very high speed rates and under uncertainty. The framework provides exact mathematical formulas to compute the detection likelihood and introduces a new flow reconstruction lemma under partial information. These  theoretical results lead to the design of BubbleCache, a new elephant flow detection algorithm  designed to operate near the optimal tradeoff between computational scalability and accuracy by dynamically tracking the traffic’s natural cutoff sampling rate. We demonstrate on a real world 100 Gbps network that the BubbleCache algorithm helps reduce the computational cost by a factor of 1000 and the memory requirements by a factor of 100 while detecting the top flows on the network with very high probability.


Algorithms and Data Structures to Accelerate Network Analysis (Extended Version)



Publication Source: Elsevier: Future Generation Computer Systems Volume 86, September 2018

As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper, we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed-ups of 5X at traffic rates of 10 Gbps.
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Efficient Packet Forwarding Using Cyber-Security Aware Policies



Publication Source: Patent US9798588B1

For balancing load, a forwarder can selectively direct data from the forwarder to a processor according to a loading parameter. The selective direction includes forwarding the data to the processor for processing, transforming and/or forwarding the data to another node, and dropping the data. The forwarder can also adjust the loading parameter based on, at least in part, feedback received from the processor. One or more processing elements can store values associated with one or more flows into a structure without locking the structure. The stored values can be used to determine how to direct the flows, e.g., whether to process a flow or to drop it. The structure can be used within an information channel providing feedback to a processor.
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