Artificial Intelligence Security: Threats and Countermeasures

2023 ◽  
Vol 55 (1) ◽  
pp. 1-36
Author(s):  
Yupeng Hu ◽  
Wenxin Kuang ◽  
Zheng Qin ◽  
Kenli Li ◽  
Jiliang Zhang ◽  
...  

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.


2017 ◽  
Vol 7 (2) ◽  
pp. 20160151 ◽  
Author(s):  
Angela Logan ◽  
Michael P. Murphy

Our understanding of the role of mitochondria in biomedical sciences has expanded considerably over the past decade. In addition to their well-known metabolic roles, mitochondrial are also central to signalling for various processes through the generation of signals such as ROS and metabolites that affect cellular homeostasis, as well as other processes such as cell death and inflammation. Thus, mitochondrial function and dysfunction are central to the health and fate of the cell. Consequently, there is considerable interest in better understanding and assessing the many roles of mitochondria. Furthermore, there is also a growing realization that mitochondrial are a promising drug target in a wide range of pathologies. The application of interdisciplinary approaches at the interface between chemistry and biology are opening up new opportunities to understand mitochondrial function and in assessing the role of the organelle in biology. This work and the experience thus gained are leading to the development of new classes of therapies. Here, we overview the progress that has been made to date on exploring the chemical biology of the organelle and then focus on future challenges and opportunities that face this rapidly developing field.


2021 ◽  
Vol 12 ◽  
Author(s):  
Desia Bacon ◽  
Haley Weaver ◽  
Jenny Saffran

Online data collection methods pose unique challenges and opportunities for infant researchers. Looking-time measures require relative timing precision to link eye-gaze behavior to stimulus presentation, particularly for tasks that require visual stimuli to be temporally linked to auditory stimuli, which may be disrupted when studies are delivered online. Concurrently, by widening potential geographic recruitment areas, online data collection may also provide an opportunity to diversify participant samples that are not possible given in-lab data collection. To date, there is limited information about these potential challenges and opportunities. In Study 1, twenty-one 23- to 26-month-olds participated in an experimenter-moderated looking-time paradigm that was administered via the video conferencing platform Zoom, attempting to recreate in-lab data collection using a looking-while-listening paradigm. Data collected virtually approximated results from in-lab samples of familiar word recognition, after minimal corrections to account for timing variability. We also found that the procedures were robust to a wide range of internet speeds, increasing the range of potential participants. However, despite the use of an online task, the participants in Study 1 were demographically unrepresentative, as typically observed with in-person studies in our geographic area. The potentially wider reach of online data collection methods presents an opportunity to recruit larger, more representative samples than those traditionally found in lab-based infant research, which is crucial for conducting generalizable human-subjects research. In Study 2, microtargeted Facebook advertisements for online studies were directed at two geographic locations that are comparable in population size but vary widely in demographic and socioeconomic factors. We successfully elicited sign-up responses from caregivers in neighborhoods that are far more diverse than the local University community in which we conduct our in-person studies. The current studies provide a framework for infancy researchers to conduct remote eye-gaze studies by identifying best practices for recruitment, design, and analysis. Moderated online data collection can provide considerable benefits to the diversification of infant research, with minimal impact on the timing precision and usability of the resultant data.


2021 ◽  
Vol 6 (2) ◽  
pp. 71-74
Author(s):  
Lance Valcour

The path to improved police transparency in Canada includes the use of advanced technology with capabilities such as artificial intelligence, machine learning, “cloud” enabled services, and an ever-increasing number of data collection and management tools. However, these innovations need to be closely linked with a national—not federal—stakeholder review of current legal, legislative, and privacy frameworks. This article provides readers with a high-level overview of the issue of police transparency in Canada. It then outlines a number of key challenges and opportunities for improving this transparency. It concludes with a call to action for key Canadian stakeholders to work collaboratively to improve police transparency in Canada.


Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 384
Author(s):  
Mohammad Reza Jabbarpour ◽  
Ali Mohammad Saghiri ◽  
Mehdi Sookhak

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.


Author(s):  
Jorge F. Arinez ◽  
Qing Chang ◽  
Robert X. Gao ◽  
Chengying Xu ◽  
Jianjing Zhang

Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.


2020 ◽  
Vol 10 (12) ◽  
pp. 4400 ◽  
Author(s):  
Luis A. Curiel-Ramirez ◽  
Ricardo A. Ramirez-Mendoza ◽  
Rolando Bautista-Montesano ◽  
M. Rogelio Bustamante-Bello ◽  
Hugo G. Gonzalez-Hernandez ◽  
...  

Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution.


The Internet of Things (IoT) has been growing to market from the past several years with great potential. Many several devices have been now available in the market based on IoT, which enables it to connect with your smart phones or with any other kind of smart resources, and then that device is ready to perform smart work via the Internet. With the help of IoT, we are now able to make our devices connect with the internet and then can be operated from anywhere from the geo location as well as it can store and retrieve a large amount of data for better communication between the end-user and the device. IoT also has a wide range of applications that are being used on many platforms. However, this great technology also has to face many problems and among all the problems the main issue arises with its security aspects. The major concern on using IoT security is the hacker wants to enter into the large network system using a particular device as all the devices are connected over the network. Not only this, many other security threats and malware are also a major concern in IoT. So taking these security aspects as a major concern this research paper reviews several security issues and challenges that occur in IoT. As there in every field when it comes to cyber security for any kind of data, we need to follow CIA Security Triangle i.e., Confidentiality, Integrity, and Availability of data. CIA security triangle is the most important concept in terms of security and also must be taken into consideration in the IoT domain. Therefore, considering all these facts and reviewing some of the latest documents as well as researches in the field of IoT, this paper has been based on all the facts related to IoT security issues and its desirable solution which is needed to be done and should follow the security triangle to an extent.


Author(s):  
Okolie S.O. ◽  
Kuyoro S.O. ◽  
Ohwo O. B

Cyber-Physical Systems (CPS) will revolutionize how humans relate with the physical world around us. Many grand challenges await the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defence, aerospace and buildings. Exploration of these potentialities around space and time would create applications which would affect societal and economic benefit. This paper looks into the concept of emerging Cyber-Physical system, applications and security issues in sustaining development in various economic sectors; outlining a set of strategic Research and Development opportunities that should be accosted, so as to allow upgraded CPS to attain their potential and provide a wide range of societal advantages in the future.


Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>


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