CARSA: A context-aware reasoning-based service agent model for AI planning of web service composition

Niu, Wenjia; Li, Gang; Tang, Hui; Zhou, Xu; Shi, Zhongzhi

doi:10.1016/j.jnca.2011.06.008

Cited by 27 publications

(12 citation statements)

References 16 publications

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“…In such attack, the spoofing is just a causing factor, while the mechanism of COP algorithm is the key. In comparison, for GPS spoofing attack, our work focuses on the revealing of algorithm-level security analysis caused by spoofing, not the security of GPS spoofing or context-aware sensing [21][22][23][24] itself.…”

Section: Defense Discussionmentioning

confidence: 99%

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

Xiang

Tong

et al. 2020

Wireless Communications and Mobile Computing

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With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

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Section: Defense Discussionmentioning

confidence: 99%

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

Xiang

Tong

et al. 2020

Wireless Communications and Mobile Computing

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“…Arti cial Intelligence is accepting the phenomenal changes of the business framework and transforming the business culture how to work in the digital era (Davenport, 2018, El-Najdawi et al, 1993, Hamscher, 1994, Metelskaia et al, 2018. Basically it is not just changing the culture of business but also fundamentally renovating the conventional thinking and signi cance of alliance and collaboration, vigorous competition, and digital and enriched innovation (Hagras, 2018, El-Najdawi et al, 1993, Niu et al, 2011, Schultel et al, 2017. While most AI inventiveness craft economical improvement by recognizing an exclusively innovative opening and breaks, pleasing to the eye contemporary energies, delivering a market fragment that others business tycoons have overlooked, or crafting fresh markets, linked devices that nourish a continual creek of digital data about functionality, treatment, fabrication, requests, automated and robotic help center and more to a signi cant setting will make even more captivating economical revolutions (Gentsch, 2018, Hagras, 2018, Liu, et al, 2020, Scheer, 2012, Maedche, 2019.…”

Section: Business Model Transformationmentioning

confidence: 99%

AI Business Model: An Integrative Business Approach

Mishra

Tripathi

2020

Preprint

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Artificial Intelligence is the ecosphere’s prevalent and most comprehensive general knowledge and common-sense cognitive engine. The Artificial Intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in digital data fluid through these coordination, fueling business enhancements over phase. In this business model, the business will safekeeping a recurrent subscription. This study endeavors to emphasis on the preemptive side of the use of AI and Machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics.

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“…More specifically, the GPS spoofing [18,22] by sending interfering signals is very common. In addition to GPS sensors, other sensors including optical sensors [20,21] and context-aware services [23][24][25][26] can also be threatened to cause data spoofing physically [8,19,20,[27][28][29], wirelessly [9,30,31], or through malware [10,11]. Similar to previous work, our data spoofing is launched through a node in the ad hoc network.…”

Section: Data Spoofing In Uavmentioning

confidence: 99%

Exposing Spoofing Attack on Flocking-Based Unmanned Aerial Vehicle Cluster: A Threat to Swarm Intelligence

Huang

Tian

et al. 2020

Security and Communication Networks

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With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.

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CARSA: A context-aware reasoning-based service agent model for AI planning of web service composition

Cited by 27 publications

References 16 publications

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

AI Business Model: An Integrative Business Approach

Exposing Spoofing Attack on Flocking-Based Unmanned Aerial Vehicle Cluster: A Threat to Swarm Intelligence

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