In an era where the Internet of Things (IoT) intersects increasingly with
generative Artificial Intelligence (AI), this article scrutinizes the emergent
security risks inherent in this integration. We explore how generative AI
drives innovation in IoT and we analyze the potential for data breaches when
using generative AI and the misuse of generative AI technologies in IoT
ecosystems. These risks not only threaten the privacy and efficiency of IoT
systems but also pose broader implications for trust and safety in AI-driven
environments. The discussion in this article extends to strategic approaches
for mitigating these risks, including the development of robust security
protocols, the multi-layered security approaches, and the adoption of AI
technological solutions. Through a comprehensive analysis, this article aims to
shed light on the critical balance between embracing AI advancements and
ensuring stringent security in IoT, providing insights into the future
direction of these intertwined technologies.

本文研究了物联网和生成式人工智能（AI）交叉融合的时代中，这种整合中所固有的新兴安全风险。我们探讨了生成式人工智能在物联网中推动创新的方式，并分析了在使用生成式人工智能时可能发生数据泄露以及在物联网生态系统中滥用生成式人工智能技术的潜力。这些风险不仅威胁到物联网系统的隐私和效率，而且对 AI 驱动环境中的信任和安全性产生广泛的影响。本文讨论还延伸到缓解这些风险的战略方法，包括开发健壮的安全协议、多层次安全方法和采用 AI 技术解决方案。通过全面分析，本文旨在阐明在物联网中拥抱 AI 进步和确保严格安全之间的关键平衡，并提供对这些交织技术未来方向的洞察。

物联网中生成式人工智能的安全风险关切

Security Risks Concerns of Generative AI in the IoT

In today's digitally interconnected world, cybersecurity threats have reached
unprecedented levels, presenting a pressing concern for individuals,
organizations, and governments. This study employs a qualitative research
approach to comprehensively examine the diverse threats of cybersecurity and
their impacts across various sectors. Four primary categories of threats are
identified and analyzed, encompassing malware attacks, social engineering
attacks, network vulnerabilities, and data breaches. The research delves into
the consequences of these threats on individuals, organizations, and society at
large. The findings reveal a range of key emerging threats in cybersecurity,
including advanced persistent threats, ransomware attacks, Internet of Things
(IoT) vulnerabilities, and social engineering exploits. Consequently, it is
evident that emerging cybersecurity threats pose substantial risks to both
organizations and individuals. The sophistication and diversity of these
emerging threats necessitate a multi-layered approach to cybersecurity. This
approach should include robust security measures, comprehensive employee
training, and regular security audits. The implications of these emerging
threats are extensive, with potential consequences such as financial loss,
reputational damage, and compromised personal information. This study
emphasizes the importance of implementing effective measures to mitigate these
threats. It highlights the significance of using strong passwords, encryption
methods, and regularly updating software to bolster cyber defenses.

通过定性研究方法全面审查了网络安全的多种威胁及其对各个领域的影响，发现包括恶意软件攻击、社交工程攻击、网络漏洞和数据泄露在内的四类主要威胁；重要的新兴网络安全威胁还包括高级持续性威胁、勒索软件攻击、物联网漏洞和社交工程利用，对组织和个人造成重大风险；应采取多层次的安全措施，包括强大的安全措施、全面的员工培训和定期的安全审计来应对这些威胁，以减轻潜在的财务损失、声誉损害和个人信息泄露等后果。

网络安全的新前沿：新兴威胁与创新

The New Frontier of Cybersecurity: Emerging Threats and Innovations

Application security is an essential part of developing modern software, as
lots of attacks depend on vulnerabilities in software. The number of attacks is
increasing globally due to technological advancements. Companies must include
security in every stage of developing, testing, and deploying their software in
order to prevent data breaches. There are several methods to detect software
vulnerability Non-AI-based such as Static Application Security Testing (SAST)
and Dynamic Application Security Testing (DAST). However, these approaches have
substantial false-positive and false-negative rates. On the other side,
researchers have been interested in developing an AI-based vulnerability
detection system employing deep learning models like BERT, BLSTM, etc. In this
paper, we proposed a two-stage solution, two deep learning models were proposed
for vulnerability detection in C/C++ source codes, the first stage is CNN which
detects if the source code contains any vulnerability (binary classification
model) and the second stage is CNN-LTSM that classifies this vulnerability into
a class of 50 different types of vulnerabilities (multiclass classification
model). Experiments were done on SySeVR dataset. Results show an accuracy of
99% for the first and 98% for the second stage.

本文提出了一个两阶段解决方案，第一阶段采用 CNN 检测源代码是否存在漏洞（二元分类模型），第二阶段采用 CNN-LTSM 将漏洞分类为 50 种不同的漏洞类型（多类分类模型），并针对 SySeVR 数据集进行实验，结果表明第一和第二阶段的准确性分别达到 99% 和 98%。