Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Intelligent Immunity Based Security Defense System for Multi-Access Edge Computing Network

In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system.In the proposed security defense system,the security is protected by the intelligent immunity through three functions,identification function,learning function,and regulation function,respectively.Meanwhile,a three process-based intelligent algorithm is proposed for the intelligent immunity system.Numerical simulations are given to prove the effeteness of the proposed approach.

Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.

Linux Kali for Social Media User Location:A Target-Oriented Social Media Software Vulnerability Detection

Technology is expanding like a mushroom,there are various benefits of technology,in contrary users are facing serious losses by this technology.Furthermore,people lost their lives,their loved ones,brain-related diseases,etc.The industry is eager to get one technology that can secure their finance-related matters,personal videos or pictures,precious contact numbers,and their current location.Things are going worst because every software has some sort of legacy,deficiency,and shortcomings through which exploiters gain access to any software.There are various ways to get illegitimate access but on the top is Linux Kali with QRLjacker by user grabber command.This study recapitulates the impacts of the said technology and related avoidance.Detail contemplation depicts social media users like WhatsApp users can take a long sigh of relief when they will adopt the recommended methods.The problem is breaching of legitimate social media real-time location by an illegitimate user through Linux Kali,for this reason,end-user has no knowledge to spoof their IP to protect their real-time location.This paper will address the solution to the said problem.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

探讨企业财务管理中的大数据与人工智能技术

在企业财务管理的实践中,大数据与人工智能技术已成为不可或缺的工具。应用这些技术,使处理复杂的财务信息变得更加高效,同时,也提升决策的准确性。通过深入分析和预测,企业能在竞争激烈的市场环境中更好地防范风险,优化资源配置。文章探讨这些技术在改善数据处理速度和精确度方面的作用,以及它们如何帮助企业降低运营成本,提高工作效率。随着技术的进步,企业的挑战不仅限于如何收集和存储大量数据,更关键的是如何从这些数据中提炼出有用的信息,支持快速、有效的决策。文章关注大数据与人工智能在企业财务管理中的具体应用,强调这些技术为企业带来的直接效益和潜在的正面影响。

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Bibliometrics-based analysis on competitive intelligence in China

By applying to the theories and methodologies on informetrics,the authors collected the statistical data of relevant published articles,and then made some analysis including the prolific authors community,the long tail distribution of authors,and the disciplinary distribution of the published articles.To conclude,some development trends and suggestions were put forward for the reference.

针对自动驾驶智能模型的攻击与防御

近年来,以深度学习算法为代表的人工智能技术为人类生产生活的方方面面带来了巨大的革新,尤其是在自动驾驶领域,部署着自动驾驶系统的智能汽车已经走进入们的生活,成为了重要的生产力工具.然而,自动驾驶系统中的人工智能模型面临着潜在的安全隐患和风险,这给人民群众生命财产安全带来了严重威胁.本文通过回顾自动驾驶智能模型攻击和防御的相关研究工作,揭示自动驾驶系统在物理世界下面临的安全风险并归纳总结了相应的防御对策.具体来说,本文首先介绍了包含攻击面、攻击能力和攻击目标的自动驾驶系统安全风险模型.其次,面向自动驾驶系统的三个关键功能层——传感器层、感知层和决策层,本文依据受攻击的智能模型和攻击手段归纳、分析了对应的攻击方法以及防御对策,并探讨了现有方法的局限性.最后,本文讨论和展望了自动驾驶智能模型攻击与防御技术面临的难题与挑战,并指出了未来潜在的研究方向和发展趋势.

城市内涝预警预报系统研发及应用

城市内涝问题具有成因多元、影响面广的特点,是基础性社会问题。分析了城市内涝的成因,提出通过信息化手段提升城市内涝灾害应对能力。构建基于内涝实时物联感知、数据汇聚、数学模型预报、大数据分析、水利数字孪生的城市内涝预警预报系统,并在宁波市进行了应用。通过搭建“天基”监测预警、“落地雨”监测预警、内涝预报预警、内涝成因分析、内涝影响评估等功能模块,实现了城区内涝的精准模拟、风险图的在线绘制以及内涝损失的高效评估,有力支撑了城市暴雨内涝灾害预报预警,提升了城市内涝风险管控能力。

人工智能时代“深度伪造辩护”对刑事诉讼的挑战及其应对

深度伪造辩护是在深度伪造技术及生成式人工智能发展基础之上出现的一种刑事辩护方法。深度伪造辩护的出现,可能消解刑事诉讼中对证据的信任基础,同时可能导致在刑事诉讼中出现“说谎者红利”的情形,甚至可能影响对案件事实的认定。面临人工智能时代“深度伪造辩护”对刑事诉讼的冲击,必须适时对刑事诉讼辩护制度进行改革。在程序法方面,需要由证据真实性审查,转向程序规则之限制;在实体法方面,应增设相关“深度伪造辩护”的罚则;在律师执业纪律规范方面,应将行政性监管罚则进行具体化。

融合大语言模型的国防科技情报智能感知系统构建及应用研究

[目的/意义]在全球地缘冲突风险下,搭建自主可控的国防科技情报智能感知系统,对国家安全和军队建设的战略决策支撑作用重大。开展融合大语言模型的国防科技情报智能感知系统建设研究,能提高情报获取效率和智能分析能力。[方法/过程]该系统通过自动采集分类更新,可以快速识别技术情报及专家情报;构建了庞大知识库和信息模型,融合大语言模型智能挖掘创新点和前沿技术。[结果/结论]该系统总体上实现了根据任务及时监测重要情报线索、深度挖掘支撑可信情报的证据及证据链、智能感知领域技术关键内容等功能,为国防科技情报工作在人工智能时代提质增效提供参考借鉴。

美国国防人工智能战略的主题挖掘与演化特征分析

面对美国国防部启动的一系列人工智能(AI)战略举措,识别美国国防AI战略主题和演化特征,认清美国国防AI战略的实质意图,能为我国国防AI战略规划提供参考。本文选取2018—2022年美国国防部正式发布的AI战略文本作为研究样本,采用非结构化文本挖掘工具,通过提炼高频词汇,挖掘出美国国防AI战略主题。同时,绘制主题词共现网络图谱,由点及面观察美国国防AI主题演变特征。研究表明,美国国防部的AI发展战略可分为战略主体、技术创新、伦理安全、战略资源、多元合作五大主题。在演化进程中,战略主体方面,形成了以AI联合中心为核心主体,联合多部门共建AI的国家防御体系。技术创新方面,从各军种自建AI作战系统演化为建立陆、海、空一体化的作战指挥控制网络。战略资源方面,数据是关键资产、人才是关键要素、资金是关键助力。伦理安全方面,从“AI伦理准则”到“负责任的人工智能(RAI)”的背后是从“体现民主价值观”到“为美国军事霸权扫清舆论障碍”。多元合作方面,对内,美国公私主体共建AI技术生态系统;对外,国防部与盟友伙伴国家联合共建全球RAI生态体系。

数智技术时代的中国档案学学科发展

数智技术,直接导致我国千行百业的原始数据形成,其管理正发生前所未有的变化。为适应从人人相连走向万物智联的大数据安全管理,需要对我国高等教育相关学科专业进行系统研究。梳理数字技术对我国组织的业务活动及其原始记录形成和管理的作用影响之后,提出传统的“中国档案学”学科将演进为“档案大数据工程”学科。并且指出,档案大数据工程学科高等教育包括“管理文秘”“数据资源管理”“大数据技术”等专业,尝试提出以上几个专业建设的发展方向。

一种半监督学习潜艇规避防御智能决策方法

潜艇水下作战行动中,受到水下弱可观测环境影响,获取的目标信息呈现稀疏特性。机动规避是潜艇水下防御的重要战术方法,现有机动规避参数仿真与优化方法在建模时不可避免引入观测误差,缺乏对态势演变的应对手段,且由于军事专家的稀缺性,获取军事专家标签的战术对抗样本代价十分昂贵。针对上述困难提出一种基于自编码与主动Q学习策略结合的半监督学习智能决策方法。通过引入对比预测编码自编码器,最大化时序输入与上下文间互信息熵,提高对稀疏时序输入的表征能力。将表征输入与主动强化学习任务相结合,降低智能体的标签需求率,提高规避决策时对环境反馈的能力。基于3 a采集的指挥员战法研练复盘数据构建上帝视角、红方视角数据集。实验结果表明:所提算法与不采用稀疏时序自编码器的算法消融实验,在完全信息、红方视角条件下决策精度分别达到98%、78%,而标签需求率仅为4%、44%;相比于经典的时序分类算法决策精度提高了14%、9%,与有监督算法相比在标签需求率降低为原来的24%~44%条件下,决策精度误差与有监督算法仅差1%,说明所提算法在保证决策精度的同时可大幅降低标签需求量,从而为少量样本条件下的军事智能决策提供一种通用的技术框架。

人工智能后门防御评估方法及其架构研究

为了应对人工智能系统可能面临的后门攻击风险,研究人员已经开发了一系列后门防御策略。现有防御方法评估标准的多样性,使得跨方法比较成为一大挑战,因此提出了一种人工智能后门防御统一评估框架。该框架旨在为不同层面(包括数据集级别和模型级别等)的防御策略,提供一个公共的评价标准。在数据集级别,主要通过准确率来评估后门检测的有效性;而在模型级别,则主要关注攻击成功率等指标。人工智能后门防御统一评估框架,能够在相同的评价标准下,对比和分析不同后门防御方法的性能。这不仅有助于识别各方法的优势和不足,还能够提出针对性改进建议。结果表明,人工智能后门防御统一评估框架能有效地评估不同防御策略的性能,为进一步提升人工智能系统的安全性提供重要的参考依据。

智能化对弈:军用人工智能对现代战争攻防平衡的重塑

当前,人工智能发展取得了重大进步并加速向军事领域延伸,军用人工智能已成为各国发展自身军事力量的重要动力。攻防理论以攻防平衡为核心概念、以技术和军事任务执行能力为关注焦点,是结构现实主义的重要分支理论。军用人工智能通过提高军事能力、影响军事战略制定和军事组织形态、推动军队作战方式和指挥方式改变、强化军队综合保障能力,引发了攻防能力的深刻变化。大国之间军用人工智能水平和能力的对比变化远高于常规军事力量,这使得强弱方的更替明显加速、攻防平衡的稳定性减弱。在这一情况下,智能赋能为国家创造了更多打破均衡、突破传统攻防作战极限的可能性与优势,丰富了攻防理论的研究论域。

人工智能时代我国刑事司法改革的双重使命

人工智能时代,我国刑事司法改革面临确保司法规律遵循和科学技术运用的双重使命。需要强化检察机关对侦查行为的监督,平衡侦查机关使用大数据技术与保护公民权利之间的关系;坚持控辩平等,并通过建立公共技术服务平台,实现控辩双方在数据获取上的权利平衡;在审判领域,应建立以审判为中心的诉讼制度,坚守法官中立性和决策核心地位,确保人工智能技术辅助的正确应用,同时注重算法的透明性和可解释性,并尊重被告人的选择权,合理限制人工智能辅助系统的案件适用范围。