Detecting Anomaly in Large-scale Network using Mobile Crowdsourcing

Li, Yang; Sun, Jiachen; Huang, Wenguang; Tian, Xiaohua

doi:10.1109/infocom.2019.8737541

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…Therefore, obtaining labelled log data for any applications area of interest is often difficult and it is mostly unbalanced or system specific; hence, it needs to be pre-processed before analysis. In addition, obtaining a large-scale log anomaly dataset with high-quality ground truth has been an on-going challenge [38][39][40]. Labelling log anomalies in a dataset requires expert's assistance and therefore it is labor intensive and often expensive.…”

Section: Real-time Security With Unsupervised Deep Learning Modelsmentioning

confidence: 99%

“…Labelling log anomalies in a dataset requires expert's assistance and therefore it is labor intensive and often expensive. Hence, supervised machine learning strategies like [38][39][40] that depend on prior patterns of normal and abnormal behaviours are not suitable for real-time anomaly detection systems. Many recent works like [33,41], propose unsupervised machine learning algorithms for detecting anomalies.…”

Section: Real-time Security With Unsupervised Deep Learning Modelsmentioning

confidence: 99%

Section: Supervised Learning Approachesmentioning

confidence: 99%

“…Supervised learning techniques like [38,39,117,118] have been widely explored for network anomaly detection. For example, [38] employs a tree model based on the random forest method to detect anomalies in the data in crowd sourced networks. In these methods, both normal and abnormal vectors are required to train a binary classifier to detect future anomalies.…”

Section: Supervised Learning Approachesmentioning

confidence: 99%

“…This enables the system administrators to intervene in an on-going attack or fix a system performance issue. In this regard, offline learning strategies like [38,41,44,117], that require the knowledge of prior pattern of normal and abnormal events are not suitable for the new detection systems and also for existing systems trained on outdated events.…”

Section: Unsupervised Learning Approachesmentioning

confidence: 99%

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PHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applications

Adhatarao¹

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The Internet was designed primarily for sharing expensive resources through establishing point-to-point connection using dedicated IP addresses. However, it has since evolved both in complexity and its number of users. Subsequently, various heterogeneous networking architectures and protocols have emerged to fulfill the growing needs of users. Information-Centric Networking (ICN) is a promising new networking paradigm which treats content as the first-class entity. In ICN, nodes exchange information based on the Names of the content instead of the IP addresses of the end points that request or provide the information. Internet of Things (IoT) has gained popularity in recent years and billions of devices are expected to connect in the near future. With advancements in technology and availability of Big data, Artificial Intelligence is producing promising new applications and services. Additionally, many new architectures and services like data centers, Cloud computing, Edge/Fog computing, etc., have also emerged to enhance the Internet by providing cost-effective, flexible and scalable computing platforms for the growing number of applications. However, nowadays, users are more interested in retrieving content(s) of their interest irrespective of its location. While, devices in the Sensor Networks need a larger address space and low communication overhead due to constrained resources. In addition, machine learning applications demand high computing power and often applications have to find a trade-off between resource consumption and desired accuracy. Moreover, these heterogeneous networks have different requirements and hence, they operate with dissimilar protocols. Additionally, there are many important open issues within these heterogeneous networks like naming and mobility in ICN, pub/sub and protocol translation in IoT, optimizing resource-accuracy trade off with machine learning, etc., that need to be resolved. This dissertation analyzes the heterogeneous and evolving Internet architectures research space and identifies six main open issues and reinforces the Internet with efficient and optimal solutions. In particular, the dissertation analyzes the two most important naming schemas in ICN: hierarchical and flat names through examining the various interlinked metrics and proposes the optimal choices for efficient naming schemas at the application and network layers in ICN. This is followed by a proposal for a gateway and additional functions to seamlessly integrate the heterogeneous Sensor Networks to the Internet to realize IoT. Additionally, we study the requirements of resource-constrained IoT devices and provide an efficient lightweight pub/sub system to optimize the resource utilization in IoT networks. We further provide a comprehensive and robust solution to support the network mobility in ICN iv through a distributed Mobility Agent service architecture followed by two distinct frameworks to address the open issues in deep learning 1) EVA: which provides a distributed architecture to optimize the reso...

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Section: Real-time Security With Unsupervised Deep Learning Modelsmentioning

confidence: 99%

Section: Real-time Security With Unsupervised Deep Learning Modelsmentioning

confidence: 99%

Section: Supervised Learning Approachesmentioning

confidence: 99%

Section: Supervised Learning Approachesmentioning

confidence: 99%

Section: Unsupervised Learning Approachesmentioning

confidence: 99%

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PHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applications

Adhatarao¹

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Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

Chen

Wang

Guo

et al. 2022

Concurrency and Computation

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As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.

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Anomaly Detection in Crowdsourced Work with Interval-Valued Labels

Spurling

Zhan

et al. 2022

Communications in Computer and Information Science

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Detecting Anomaly in Large-scale Network using Mobile Crowdsourcing

Cited by 10 publications

References 17 publications

PHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applications

PHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applications

Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

Anomaly Detection in Crowdsourced Work with Interval-Valued Labels

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