Conference
Information Security
About: Information Security is an academic conference. The conference publishes majorly in the area(s): Computer science & Edge computing. Over the lifetime, 1183 publications have been published by the conference receiving 13387 citations.
Topics: Computer science, Edge computing, Cloud computing, Information security, Security information and event management
Papers published on a yearly basis
Papers
9 Aug 2012
TL;DR: A static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware and shows that the recall rate of the approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis.
Abstract: Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. Although understanding Android malware using dynamic analysis can provide a comprehensive view, it is still subjected to high cost in environment deployment and manual efforts in investigation. In this study, we propose a static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware. The mechanism considers the static information including permissions, deployment of components, Intent messages passing and API calls for characterizing the Android applications behavior. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed mechanism and develop a system, called Droid Mat. First, the Droid Mat extracts the information (e.g., requested permissions, Intent messages passing, etc) from each applicationi¦s manifest file, and regards components (Activity, Service, Receiver) as entry points drilling down for tracing API Calls related to permissions. Next, it applies K-means algorithm that enhances the malware modeling capability. The number of clusters are decided by Singular Value Decomposition (SVD) method on the low rank approximation. Finally, it uses kNN algorithm to classify the application as benign or malicious. The experiment result shows that the recall rate of our approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis. In addition, Droid Mat is efficient since it takes only half of time than Androguard to predict 1738 apps as benign apps or Android malware.
717 citations
1 Jan 2018
TL;DR: This work proposes VideoEdge, a system that introduces dominant demand to identify the best tradeoff between multiple resources and accuracy, and narrows the search space by identifying a "Pareto band" of promising configurations.
Abstract: Organizations deploy a hierarchy of clusters - cameras, private clusters, public clouds - for analyzing live video feeds from their cameras. Video analytics queries have many implementation options which impact their resource demands and accuracy of outputs. Our objective is to select the "query plan" - implementations (and their knobs) - and place it across the hierarchy of clusters, and merge common components across queries to maximize the average query accuracy. This is a challenging task, because we have to consider multi-resource (network and compute) demands and constraints in the hierarchical cluster and search in an exponentially large search space for plans, placements, and merging. We propose VideoEdge, a system that introduces dominant demand to identify the best tradeoff between multiple resources and accuracy, and narrows the search space by identifying a "Pareto band" of promising configurations. VideoEdge also balances the resource benefits and accuracy penalty of merging queries. Deployment results show that VideoEdge improves accuracy by 25:4 and 5:4 compared to fair allocation of resources and a recent solution for video query planning (VideoStorm), respectively, and is within 6% of optimum.
328 citations
11 Jun 2001
TL;DR: A key agreement protocol previously proposed by Steer et al. is resurrected and extended to handle dynamic groups and network failures such as network partitions and merges and provides key independence, i.e. a passive adversary who knows any proper subset of group keys cannot discover any other group keys not included in the subset.
Abstract: Traditionally, research in secure group key agreement focuses on minimizing the computational overhead for cryptographic operations, and minimizing the communication overhead and the number of protocol rounds is of secondary concern. The dramatic increased in computation power that we witnessed during the past years exposed network delay in WANs as the primary culprit for a negative performance impact on key agreement protocols. The majority of previously proposed protocols optimize the cryptographic overhead of the protocol. However, high WAN delay negatively impacts their efficiency. The goal of this work is to construct a new protocol that trades off computation with communication efficiency. We resurrect a key agreement protocol previously proposed by Steer et al. We extend it to handle dynamic groups and network failures such as network partitions and merges. The resulting protocol suite is provably secure against passive adversaries and provides key independence, i.e. a passive adversary who knows any proper subset of group keys cannot discover any other group keys not included in the subset. Furthermore, the protocol is simple, fault-tolerant, and well suited for high-delay wide area network.
247 citations
12 Oct 2017
TL;DR: LAVEA is a system built on top of an edge computing platform, which offloads computation between clients and edge node, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users.
Abstract: Along the trend pushing computation from the network core to the edge where the most of data are generated, edge computing has shown its potential in reducing response time, lowering bandwidth usage, improving energy efficiency and so on. At the same time, low-latency video analytics is becoming more and more important for applications in public safety, counter-terrorism, self-driving cars, VR/AR, etc. As those tasks are either computation intensive or bandwidth hungry, edge computing fits in well here with its ability to flexibly utilize computation and bandwidth from and between each layer. In this paper, we present LAVEA, a system built on top of an edge computing platform, which offloads computation between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design and formulated an optimization problem for offloading task selection and prioritized offloading requests received at the edge node to minimize the response time. In case of a saturating workload on the front edge node, we have designed and compared various task placement schemes that are tailed for inter-edge collaboration. We have implemented and evaluated our system. Our results reveal that the client-edge configuration has a speedup ranging from 1.3x to 4x (1.2x to 1.7x) against running in local (client-cloud configuration). The proposed shortest scheduling latency first scheme outputs the best overall task placement performance for inter-edge collaboration.
247 citations
1 Oct 2018
TL;DR: This work describes four strategies to build an adaptive computer vision pipeline for search tasks in domains such as search-and-rescue, surveillance, and wildlife conservation, and shows that a judicious combination of drone-based processing and edge-basedprocessing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or result latency.
Abstract: Real-time video analytics on small autonomous drones poses several difficult challenges at the intersection of wireless bandwidth, processing capacity, energy consumption, result accuracy, and timeliness of results. In response to these challenges, we describe four strategies to build an adaptive computer vision pipeline for search tasks in domains such as search-and-rescue, surveillance, and wildlife conservation. Our experimental results show that a judicious combination of drone-based processing and edge-based processing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or result latency.
233 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 49 |
| 2020 | 137 |
| 2019 | 148 |
| 2018 | 137 |
| 2017 | 130 |
| 2016 | 100 |