检索结果-南通市图书馆

CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

science China(Information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA school of cyber Engineering Xidian University school of Computer science and technology Xidian University Department of Computer and Information science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance.MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Towards the universal defense for query-based audio adversarial attacks on speech recognition system

引用

cybersecurity 2024年第1期7卷 53-70页

作者： Feng Guo Zheng Sun Yuxuan Chen Lei Ju school of cyber science and technology Shandong UniversityQingdaoChina Quancheng Laboratory QCLJinanChina

Recently,studies show that deep learning-based automatic speech recognition(ASR)systems are vulnerable to adversarial examples(AEs),which add a small amount of noise to the original audio examples.These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices.The existing defense methods are either limited in application or only defend on results,but not on process.In this work,we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.The insight of this method is based on the observation:many existing audio AE attacks utilize query-based methods,which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process.Inspired by this observation,We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query.Thus,we can identify when a sequence of queries appears to be suspectable to generate audio AEs.Through extensive evaluation on four state-of-the-art audio AE attacks,we demonstrate that on average our defense identify the adversary’s intent with over 90%accuracy.With careful regard for robustness evaluations,we also analyze our proposed defense and its strength to withstand two adaptive attacks.Finally,our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.

关键词： Adversarial attacks Defense Memory mechanism Query-based

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

引用

Chinese Journal of Aeronautics 2024年第3期37卷 208-218页

作者： Youmin GONG Yanning GUO Yueyong LYU Dongyu LI Guangfu MA Department of Control science and Engineering Harbin Institute of TechnologyHarbin 150001China school of cyber science and technology Beihang UniversityBeijing 100191China

Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this paper.Firstly,a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance,whose estimation error can converge to zero after a time independent of the initial state.Then,a sliding surface ensuring fixed-time convergence is presented.This sliding surface can guarantee that the vehicle achieves a non-concave trajectory,which is better for avoiding collision and maintaining the visibility of the landing site or docking port.Next,the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed,which is continuous and chatter free.At last,three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

关键词： Three-dimensional Terminal guidance Non-concave trajectory constraint Fast fixed-time control Sliding mode control

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

CoDetect: cooperative anomaly detection with privacy protection towards UAV swarm

引用

science China(Information sciences) 2024年第5期67卷 325-326页

作者： Teng LI Weiguo LIN Ruichen MA Zhuo MA Yulong SHEN Jianfeng MA school of cyber Engineering Xidian University school of Computer science and technology Xidian University

As mission scenarios grow more complex, the role of a single unmanned aerial vehicle(UAV) is limited. The UAV swarm,surpassing the single UAV, dynamically establishes temporary communication networks, ensuring high transmission rates and expansive communication coverage [1]. Unlike a single UAV, a swarm faces increased security risks, requiring specific capabilities.(1) Anomalies detection.

关键词： Unmanned aerial vehicles (UAV)

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves

引用

Computers, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashekar K.P.Vijayakumar K.Pradeep A.Balasundaram school of Computer science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing *** employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

关键词： Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks

引用

Chinese Journal of Electronics 2024年第1期33卷 90-100页

作者： Wenbin YANG Xueluan GONG Yanjiao CHEN Qian WANG Jianshuo DONG school of cyber science and Engineering Wuhan University school of Computer science Wuhan University College of Electrical Engineering Zhejiang University

With the rise of artificial intelligence and cloud computing, machine-learning-as-a-service platforms,such as Google, Amazon, and IBM, have emerged to provide sophisticated tasks for cloud applications. These proprietary models are vulnerable to model extraction attacks due to their commercial value. In this paper, we propose a time-efficient model extraction attack framework called Swift Theft that aims to steal the functionality of cloud-based deep neural network models. We distinguish Swift Theft from the existing works with a novel distribution estimation algorithm and reference model settings, finding the most informative query samples without querying the victim model. The selected query samples can be applied to various cloud models with a one-time selection. We evaluate our proposed method through extensive experiments on three victim models and six datasets, with up to 16 models for each dataset. Compared to the existing attacks, Swift Theft increases agreement(i.e., similarity) by 8% while consuming 98% less selecting time.

关键词： Artificial intelligence security Model extraction attacks Deep neural networks

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

引用

Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan school of Computer science and Engineering Vellore Institute of TechnologyChennai600127India Center for cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Secure Fine-grained Multi-keyword Ciphertext Search Supporting Cloud-edge-end Collaboration in IoT

引用

Chinese Journal of Electronics 2024年

作者： Kaifa ZHENG Ziyu ZHOU Jianwei LIU Beiyuan YU school of cyber science and technology Beihang University

The massive terminals access the Internet of things(IoT) through edge nodes, bringing forth new security and privacy challenges in ciphertext search and data sharing. Meanwhile, existing ciphertext search schemes often overlook lightweight computing paradigms and pay little attention to the search requirements of multiple data owners(DOs)/data users(DUs). To address these issues, we propose a secure fine-grained multi-keyword ciphertext search scheme with cloud-edge-end collaboration computing(SFMS-CC). This SFMS-CC scheme focuses on the efficiency of end users and employs a cloud-edge-end collaborative computing paradigm, effectively offloading the incremental overhead from terminals and achieving low-cost constant overhead for the first time on the DO/DU side. Furthermore, based on public-key cryptography, a ciphertext search framework supporting multi-keyword is presented systematically. Each user is assigned an exclusive search Tok to enhance the user experience. Additionally, by integrating attribute-based encryption(ABE), a multi-DOs/multi-DUs model is constructed, seamlessly embedding entity private keys and public keys into encryption, search, decryption, and other steps, ensuring high privacy and security of this scheme. Security analysis demonstrates that the SFMS-CC scheme withstands choose plaintext attack(CPA), providing privacy-preserving for outsourced data and user information. Simulation results indicate that the SFMS-CC scheme is efficient and feasible in practice.

关键词：

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Channel modeling for MI-based wireless underground sensor networks with conductive objects

引用

science China(Information sciences) 2024年第7期67卷 333-334页

作者： Shuhan DENG Guanghua LIU Ziwei CHEN Huaijin ZHANG Tao JIANG Research Center of 6G Mobile Communications School of Cyber Science and EngineeringHuazhong University of Science and Technology school of Electronic Information and Communications Huazhong University of Science and Technology

Wireless underground sensor networks (WUSNs) are an emerging research field with promising applications. However, WUSNs links suffer from significant attenuation as the propagation of the signal needs to pass through soil, rock,and water. Magnetic induction (MI)-based transmission is proposed to overcome these issues as MI signal can penetrate most substances similarly without experiencing high path loss, which ensures the robustness of WUSNs links [1].

关键词： Wireless sensor networks

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

引用

Computers, Materials & Continua 2024年第2期78卷 2469-2489页

作者： Yan Li Qiyuan Wang Kaidi Jia school of cyber Security Gansu University of Political Science and LawLanzhou730070China

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

关键词： Image description deep reinforcement learning attention mechanism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：