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ICAIS 2022: Artificial Intelligence and Security pp 583–598 Cite as

A Survey on IPv6 Security Threats and Defense Mechanisms

  • Ning Liu 11 ,
  • Jing Xia 11 ,
  • Zhiping Cai 11 ,
  • Tao Yang 11 ,
  • Bingnan Hou 11 &
  • Zhilin Wang 12  
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  • First Online: 04 July 2022

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13338))

Compared with IPv4 network, IPv6 network has some new characteristics, such as larger address space and more simplified message header structure. These new characteristics in IPv6 network may introduce both security enhancement opportunities and new security challenges. At present, there are mainly two threats in IPv6 network during its wide deployment. On one hand, during the transition from IPv4 to IPv6, “dual stack”, “traffic tunneling” and “translation” are three building blocks, all of which may introduce new security threats. On the other hand, IPv6 networking internal caused by new characteristics of IPv6 can also incur vulnerability. In view of these new forms of threats, network managers will generally adopt some defense mechanisms based on the characteristics of IPv6. This paper introduces and classifies the security threats in IPv6 network, then analyzed the defense mechanisms against different security threats in detail. Finally, we present some thoughts and future research directions in the field of IPv6 security.

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Liu, N., Xia, J., Cai, Z., Yang, T., Hou, B., Wang, Z. (2022). A Survey on IPv6 Security Threats and Defense Mechanisms. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_47

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Abstract: This paper presents the features and benefits of legacy IPv4 network migration towards major two latest networking paradigms viz. Internet protocol version 6 (IPv6) and the software-defined networking (SDN). These latest networking paradigms are the enabler of future generation networking so that the standards and requirements of fifth generation (5G) wireless networking can be achieved. Features and migration approaches of IPv6 and SDN will be separately discussed, then a joint migration approach of SDN and IPv6 network termed as SoDIP6 network migration will be presented, and the integration of SoDIP6 network as a backbone of 5G network will be introduced.

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More than one-quarter of scholarly articles are not being properly archived and preserved, a study of more than seven million digital publications suggests. The findings, published in the Journal of Librarianship and Scholarly Communication on 24 January 1 , indicate that systems to preserve papers online have failed to keep pace with the growth of research output.

“Our entire epistemology of science and research relies on the chain of footnotes,” explains author Martin Eve, a researcher in literature, technology and publishing at Birkbeck, University of London. “If you can’t verify what someone else has said at some other point, you’re just trusting to blind faith for artefacts that you can no longer read yourself.”

Eve, who is also involved in research and development at digital-infrastructure organization Crossref, checked whether 7,438,037 works labelled with digital object identifiers (DOIs) are held in archives. DOIs — which consist of a string of numbers, letters and symbols — are unique fingerprints used to identify and link to specific publications, such as scholarly articles and official reports. Crossref is the largest DOI registration agency, allocating the identifiers to about 20,000 members, including publishers, museums and other institutions.

The sample of DOIs included in the study was made up of a random selection of up to 1,000 registered to each member organization. Twenty-eight per cent of these works — more than two million articles — did not appear in a major digital archive, despite having an active DOI. Only 58% of the DOIs referenced works that had been stored in at least one archive. The other 14% were excluded from the study because they were published too recently, were not journal articles or did not have an identifiable source.

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Eve notes that the study has limitations: namely that it tracked only articles with DOIs, and that it did not search every digital repository for articles (he did not check whether items with a DOI were stored in institutional repositories, for example).

Nevertheless, preservation specialists have welcomed the analysis. “It’s been hard to know the real extent of the digital preservation challenge faced by e-journals,” says William Kilbride, managing director of the Digital Preservation Coalition, headquartered in York, UK. The coalition publishes a handbook detailing good preservation practice.

“Many people have the blind assumption that if you have a DOI, it’s there forever,” says Mikael Laakso, who studies scholarly publishing at the Hanken School of Economics in Helsinki. “But that doesn’t mean that the link will always work.” In 2021, Laakso and his colleagues reported 2 that more than 170 open-access journals had disappeared from the Internet between 2000 and 2019.

Kate Wittenberg, managing director of the digital archiving service Portico in New York City, warns that small publishers are at higher risk of failing to preserve articles than are large ones. “It costs money to preserve content,” she says, adding that archiving involves infrastructure, technology and expertise that many smaller organizations do not have access to.

Eve’s study suggests some measures that could improve digital preservation, including stronger requirements at DOI registration agencies and better education and awareness of the issue among publishers and researchers.

“Everybody thinks of the immediate gains they might get from having a paper out somewhere, but we really should be thinking about the long-term sustainability of the research ecosystem,” Eve says. “After you’ve been dead for 100 years, are people going to be able to get access to the things you’ve worked on?”

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Eve, M. P. J. Libr. Sch. Commun. 12 , eP16288 (2024).

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"We decide when and how to use AI tools in our work." —

Producing more but understanding less: the risks of ai for scientific research, a psychologist and an anthropologist ponder the epistemic risks ai could pose for science..

Jennifer Ouellette - Mar 6, 2024 6:08 pm UTC

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Last month, we witnessed the viral sensation of several egregiously bad AI-generated figures published in a peer-reviewed article in Frontiers, a reputable scientific journal. Scientists on social media expressed equal parts shock and ridicule at the images, one of which featured a rat with grotesquely large and bizarre genitals.

As Ars Senior Health Reporter Beth Mole reported , looking closer only revealed more flaws, including the labels "dissilced," "Stemm cells," "iollotte sserotgomar," and "dck." Figure 2 was less graphic but equally mangled, rife with nonsense text and baffling images. Ditto for Figure 3, a collage of small circular images densely annotated with gibberish.

The paper has since been retracted, but that eye-popping rat penis image will remain indelibly imprinted on our collective consciousness. The incident reinforces a growing concern that the increasing use of AI will make published scientific research less trustworthy, even as it increases productivity. While the proliferation of errors is a valid concern, especially in the early days of AI tools like ChatGPT, two researchers argue in a new perspective published in the journal Nature that AI also poses potential long-term epistemic risks to the practice of science.

Molly Crockett is a psychologist at Princeton University who routinely collaborates with researchers from other disciplines in her research into how people learn and make decisions in social situations. Her co-author, Lisa Messeri , is an anthropologist at Yale University whose research focuses on science and technology studies (STS), analyzing the norms and consequences of scientific and technological communities as they forge new fields of knowledge and invention—like AI.

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The original impetus for their new paper was a 2019 study published in the Proceedings of the National Academy of Sciences claiming that researchers could use machine learning to predict the replicability of studies based only on an analysis of their texts. Crockett and Messeri co-wrote a letter to the editor disputing that claim, but shortly thereafter, several more studies appeared, claiming that large language models could replace humans in psychological research. The pair realized this was a much bigger issue and decided to work together on an in-depth analysis of how scientists propose to use AI tools throughout the academic pipeline.

They came up with four categories of visions for AI in science. The first is AI as Oracle, in which such tools can help researchers search, evaluate, and summarize the vast scientific literature, as well as generate novel hypotheses. The second is AI as Surrogate, in which AI tools generate surrogate data points, perhaps even replacing human subjects. The third is AI as Quant. In the age of big data, AI tools can overcome the limits of human intellect by analyzing vast and complex datasets. Finally, there is AI as Arbiter, relying on such tools to more efficiently evaluate the scientific merit and replicability of submitted papers, as well as assess funding proposals.

Each category brings undeniable benefits in the form of increased productivity—but also certain risks. Crockett and Messeri particularly caution against three distinct "illusions of understanding" that may arise from over-reliance on AI tools, which can exploit our cognitive limitations. For instance, a scientist may use an AI tool to model a given phenomenon and believe they, therefore, understand that phenomenon more than they actually do (an illusion of explanatory depth). Or a team might think they are exploring all testable hypotheses when they are only really exploring those hypotheses that are testable using AI (an illusion of exploratory breadth). Finally, there is the illusion of objectivity: the belief that AI tools are truly objective and do not have biases or a point of view, unlike humans.

This error-ridden AI-generated image, published in the journal Frontiers, is supposed to show spermatogonial stem cells, isolated, purified, and cultured from rat testes.

The paper's tagline is "producing more while understanding less," and that is the central message the pair hopes to convey. "The goal of scientific knowledge is to understand the world and all of its complexity, diversity, and expansiveness," Messeri told Ars. "Our concern is that even though we might be writing more and more papers, because they are constrained by what AI can and can't do, in the end, we're really only asking questions and producing a lot of papers that are within AI's capabilities."

Neither Crockett nor Messeri are opposed to any use of AI tools by scientists. "It's genuinely useful in my research, and I expect to continue using it in my research," Crockett told Ars. Rather, they take a more agnostic approach. "It's not for me and Molly to say, 'This is what AI ought or ought not to be,'" Messeri said. "Instead, we're making observations of how AI is currently being positioned and then considering the realm of conversation we ought to have about the associated risks."

Ars spoke at length with Crockett and Messeri to learn more.

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Department of Defense Announces Fiscal Year 2024 University Research Funding Awards

The Department of Defense today announced $221 million in awards for basic defense-related research projects as part of the Multidisciplinary University Research Initiative (MURI) program. At an average award amount of $7.5 million over five years, these competitive grants will support 30 teams located at 73 U.S. academic institutions, subject to satisfactory research progress and the availability of funds.

"The science and engineering challenges we face today are highly complex and cross disciplinary," said Dr. Bindu Nair, director of the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering. "The MURI program acknowledges these complexities by supporting teams whose members have diverse sets of expertise as well as creative scientific approaches to tackling problems. 

"This cross-fertilization of ideas can accelerate research progress to enable more rapid scientific breakthroughs and hasten the transition of basic research funding to practical applications. The program is a cornerstone of DoD's basic research portfolio and a strong contributor to its legacy of scientific impact." 

Since its inception in 1985, the Department's MURI program has allowed teams of investigators from multiple disciplines to generate collective insights, facilitating the growth of cutting-edge technologies to address the Department's unique challenges. 

The highly competitive program, which complements the Department's single-investigator basic research grants, has made immense contributions to current and future military capabilities and produced numerous commercial sector applications. 

Notable MURI achievements include breakthroughs in cold-atom quantum methods with potential applications in quantum sensing and communication, as well as advances in pulsed magnetic field propagation and Doppler radar detection leading to new detection physics for landmines.

The Fiscal Year 2024 competition identified six topics that received an additional $1.5 million each over the five-year award term specifically to support the participation of historically Black colleges and universities and minority-serving institutions (HBCU/MIs). Seven proposals selected across the six topics will receive support for HBCU/MI participation on the MURI projects.

The Army Research Office, Air Force Office of Scientific Research, and Office of Naval Research solicited Fiscal Year 2024 proposals in 25 topic areas of strategic importance to the Department. After a merit-based review of 276 white papers, a panel of experts narrowed the pool to a subset of 102 full proposals, from which the 30 final awards were selected. The list of winning teams can be downloaded here .

About USD(R&E) 

The Under Secretary of Defense for Research and Engineering (USD(R&E)) is the Chief Technology Officer of the Department of Defense. The USD(R&E) champions research, science, technology, engineering, and innovation to maintain the U.S. military's technological advantage. Learn more at www.cto.mil, follow us on Twitter @DoDCTO, or visit us on LinkedIn at https://www.linkedin.com/company/ousdre .

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Doing more, but learning less: the risks of ai in research.

Abstract illustration of data

(© stock.adobe.com)

Artificial intelligence (AI) is widely heralded for its potential to enhance productivity in scientific research. But with that promise come risks that could narrow scientists’ ability to better understand the world, according to a new paper co-authored by a Yale anthropologist.

Some future AI approaches, the authors argue, could constrict the questions researchers ask, the experiments they perform, and the perspectives that come to bear on scientific data and theories.

All told, these factors could leave people vulnerable to “illusions of understanding” in which they believe they comprehend the world better than they do.

The paper published March 7 in Nature .

“ There is a risk that scientists will use AI to produce more while understanding less,” said co-author Lisa Messeri, an anthropologist in Yale’s Faculty of Arts and Sciences. “We’re not arguing that scientists shouldn’t use AI tools, but we’re advocating for a conversation about how scientists will use them and suggesting that we shouldn’t automatically assume that all uses of the technology, or the ubiquitous use of it, will benefit science.”

The paper, co-authored by Princeton cognitive scientist M. J. Crockett, sets a framework for discussing the risks involved in using AI tools throughout the scientific research process, from study design through peer review.

“ We hope this paper offers a vocabulary for talking about AI’s potential epistemic risks,” Messeri said.

Added Crockett: “To understand these risks, scientists can benefit from work in the humanities and qualitative social sciences.”

Messeri and Crockett classified proposed visions of AI spanning the scientific process that are currently creating buzz among researchers into four archetypes:

  • In study design, they argue, “AI as Oracle” tools are imagined as being able to objectively and efficiently search, evaluate, and summarize massive scientific literatures, helping researchers to formulate questions in their project’s design stage.
  • In data collection, “AI as Surrogate” applications, it is hoped, allow scientists to generate accurate stand-in data points, including as a replacement for human study participants, when data is otherwise too difficult or expensive to obtain.
  • In data analysis, “AI as Quant” tools seek to surpass the human intellect’s ability to analyze vast and complex datasets.
  • And “AI as Arbiter” applications aim to objectively evaluate scientific studies for merit and replicability, thereby replacing humans in the peer-review process.   

The authors warn against treating AI applications from these four archetypes as trusted partners, rather than simply tools , in the production of scientific knowledge. Doing so, they say, could make scientists susceptible to illusions of understanding, which can crimp their perspectives and convince them that they know more than they do.

The efficiencies and insights that AI tools promise can weaken the production of scientific knowledge by creating “monocultures of knowing,” in which researchers prioritize the questions and methods best suited to AI over other modes of inquiry, Messeri and Crockett state. A scholarly environment of that kind leaves researchers vulnerable to what they call “illusions of exploratory breadth,” where scientists wrongly believe that they are exploring all testable hypotheses, when they are only examining the narrower range of questions that can be tested through AI.

For example, “Surrogate” AI tools that seem to accurately mimic human survey responses could make experiments that require measurements of physical behavior or face-to-face interactions increasingly unpopular because they are slower and more expensive to conduct, Crockett said.

The authors also describe the possibility that AI tools become viewed as more objective and reliable than human scientists, creating a “monoculture of knowers” in which AI systems are treated as a singular, authoritative, and objective knower in place of a diverse scientific community of scientists with varied backgrounds, training, and expertise. A monoculture, they say, invites “illusions of objectivity” where scientists falsely believe that AI tools have no perspective or represent all perspectives when, in truth, they represent the standpoints of the computer scientists who developed and trained them.

“ There is a belief around science that the objective observer is the ideal creator of knowledge about the world,” Messeri said. “But this is a myth. There has never been an objective ‘knower,’ there can never be one, and continuing to pursue this myth only weakens science.”  

There is substantial evidence that human diversity makes science more robust and creative, the authors add.

“ Acknowledging that science is a social practice that benefits from including diverse standpoints will help us realize its full potential,” Crockett said. “Replacing diverse standpoints with AI tools will set back the clock on the progress we’ve made toward including more perspectives in scientific work.”

It is important to remember AI’s social implications, which extend far beyond the laboratories where it is being used in research, Messeri said.

“ We train scientists to think about technical aspects of new technology,” she said. “We don’t train them nearly as well to consider the social aspects, which is vital to future work in this domain.”

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'all of us' research project diversifies the storehouse of genetic knowledge.

Rob Stein, photographed for NPR, 22 January 2020, in Washington DC.

Results from a DNA sequencer used in the Human Genome Project. National Human Genome Research Institute hide caption

Results from a DNA sequencer used in the Human Genome Project.

A big federal research project aimed at reducing racial disparities in genetic research has unveiled the program's first major trove of results.

"This is a huge deal," says Dr. Joshua Denny , who runs the All of Us program at the National Institutes of Health. "The sheer quantify of genetic data in a really diverse population for the first time creates a powerful foundation for researchers to make discoveries that will be relevant to everyone."

The goal of the $3.1 billion program is to solve a long-standing problem in genetic research: Most of the people who donate their DNA to help find better genetic tests and precision drugs are white.

"Most research has not been representative of our country or the world," Denny says. "Most research has focused on people of European genetic ancestry or would be self-identified as white. And that means there's a real inequity in past research."

For example, researchers "don't understand how drugs work well in certain populations. We don't understand the causes of disease for many people," Denny says. "Our project is to really correct some of those past inequities so we can really understand how we can improve health for everyone."

But the project has also stirred up debate about whether the program is perpetuating misconceptions about the importance of genetics in health and the validity of race as a biological category.

New genetic variations discovered

Ultimately, the project aims to collect detailed health information from more than 1 million people in the U.S., including samples of their DNA.

In a series of papers published in February in the journals Nature , Nature Medicine , and Communications Biology , the program released the genetic sequences from 245,000 volunteers and some analysis of those data.

"What's really exciting about this is that nearly half of those participants are of diverse race or ethnicity," Denny says, adding that researchers found a wealth of genetic diversity.

"We found more than a billion genetic points of variation in those genomes; 275 million variants that we found have never been seen before," Denny says.

"Most of that variation won't have an impact on health. But some of it will. And we will have the power to start uncovering those differences about health that will be relevant really maybe for the first time to all populations," he says, including new genetic variations that play a role in the risk for diabetes .

Researchers Gather Health Data For 'All Of Us'

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Researchers gather health data for 'all of us'.

But one concern is that this kind of research may contribute to a misleading idea that genetics is a major factor — maybe even the most important factor — in health, critics say.

"Any effort to combat inequality and health disparities in society, I think, is a good one," says James Tabery , a bioethicist at the University of Utah. "But when we're talking about health disparities — whether it's black babies at two or more times the risk of infant mortality than white babies, or sky-high rates of diabetes in indigenous communities, higher rates of asthma in Hispanic communities — we know where the causes of those problem are. And those are in our environment, not in our genomes."

Race is a social construct, not a genetic one

Some also worry that instead of helping alleviate racial and ethnic disparities, the project could backfire — by inadvertently reinforcing the false idea that racial differences are based on genetics. In fact, race is a social category, not a biological one.

"If you put forward the idea that different racial groups need their own genetics projects in order to understand their biology you've basically accepted one of the tenants of scientific racism — that races are sufficiently genetically distinct from each other as to be distinct biological entities," says Michael Eisen , a professor of molecular and cell biology at the University of California, Berkeley. "The project itself is, I think, unintentionally but nonetheless really bolstering one of the false tenants of scientific racism."

While Nathaniel Comfort, a medical historian at Johns Hopkins, supports the All of Us program, he also worries it could give misconceptions about genetic differences between races "the cultural authority of science."

Denny disputes those criticisms. He notes the program is collecting detailed non-genetic data too.

"It really is about lifestyle, the environment, and behaviors, as well as genetics," Denny says. "It's about ZIP code and genetic code — and all the factors that go in between."

And while genes don't explain all health problems, genetic variations associated with a person's race can play an important role worth exploring equally, he says.

"Having diverse population is really important because genetic variations do differ by population," Denny says. "If we don't look at everyone, we won't understand how to treat well any individual in front of us."

  • diversity in medicine
  • human genome
  • genetic research

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