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A Novel Approach Using Machine Learning for Accurate Detection of DDoS Attack in software Defined Network 2nd

A Novel Approach Using Machine Learning for Accurate Detecti...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Sharma, Hitesh Kumar Choudhury, Tanupriya Kotecha, Ketan Mahdi, Hussain Falih Dutta, Soumi Bhattacharya, Abhishek Sar, Ayan Energy Acres Bidholi Uttarakhand Bidholi India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Pune India Pune India Department of Computer and Software Engineering University of Diyala Baquba Iraq Sister Nivedita University Chakpachuria India

ISBN: (纸本)9789819776023

A denial Of Service (DoS) attack is termed an attack when the request-receiving server or network shuts down or becomes inaccessible for service. This kind of attack is caused by flooding the target server or network with huge traffic or sending information which can trigger a vulnerability to the server and lead to server crash. Different kinds of DoS attacks can occur on a network including a classic DoS attack which is carried out by a single client machine sending tons of requests and data to a server by itself to increase its processing to the extent that no new genuine requests are answered by the server and network is ultimately unreachable. Another kind of DoS attack is termed a DDoS (Distributed Denial Of Service) attack which includes a master host machine that leverages the network of machines around it to send multiple requests to the server from different machines which act as zombie machines for the attack as the machine won’t even have an idea itself that they are being used for an attack on the server. As multiple requests and data hits the server from different locations, the server becomes unavailable for processing and leads to network failure. These zombie machines are termed botnets which are connected online and are used to overwhelm a target website with fake traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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Multiple Purchase Chains with Negative Transfer Elimination for Multi-Behavior Recommendation 39

Multiple Purchase Chains with Negative Transfer Elimination ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Shuwei Liu, Yuting Dang, Yizhou Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

Multi-behavior recommendation exploits auxiliary behaviors (e.g., view, cart) to help predict users' potential target behavior (e.g., purchase) on a given item. However, existing works suffer from two issues: (1) They generally consider only a single chain from auxiliary behaviors to the target behavior, referred to as a purchase chain (e.g., view →cart→ purchase), ignoring other valuable purchase chains (e.g, view→purchase) that are beneficial for recommendation performance. (2) Most studies presume that interacted items in auxiliary behaviors are good for recommendations, and pay little attention to the negative transfer problem. That is, some auxiliary behaviors may negatively transfer the influence to the modeling of target ones (e.g., items viewed but not purchased). To alleviate these issues, we propose a novel Multiple Purchase Chains (MPC) model with negative transfer elimination for multi-behavior recommendation. Specifically, we construct multiple purchase chains from auxiliary to target behaviors according to users' historical interactions, while the representations of a previous behavior will be fed to initialize the next behavior on the chain. Then, we construct a negative graph for the latter behavior and learn the negative representations of users and items which will be filtered out to eliminate negative transfer. Experimental results on two real datasets outperform the best baseline by 40.97% and 47.26% on average in terms of Recall@10 and NDCG@10 respectively, demonstrating the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sales

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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Augmenting Sequential Recommendation with Balanced Relevance and Diversity 39

Augmenting Sequential Recommendation with Balanced Relevance...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dang, Yizhou Zhang, Jiahui Liu, Yuting Yang, Enneng Liang, Yuliang Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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EPT: Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion 39

EPT: Efficient Prompt Tuning by Multi-Space Projection and P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lan, Pengxiang Yang, Enneng Liu, Yuting Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better (worse) accuracy but at the cost of more (less) training time. (ii) The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease 4

An Efficient Deep Learning Model based Diagnosis System for ...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Ikram Ullah, Muhammad Ullah, Inam Ihtesham, Muhammad Junaid, Hazrat Yousafzai, Spogmay Sardar, Fouzia Department of Computer Software Engineering Uet Mardan Pakistan China University of Geosciences School of Automation Wuhan430074 China Dalian Jiaotong University School of Software Dalian116028 China Abdul Wali Khan University Department of Computer Science Maradan Pakistan University of Malakand Department of Computer Science and Information Technology Dir Lower Pakistan University of Malakand Department of Zoology Pakistan

ISBN: (纸本)9798350335316

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable. © 2023 IEEE.

关键词： Convolutional neural networks

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Smart Health Records Sharing Scheme based on Partially Policy-Hidden CP-ABE with Leakage Resilience 24

Smart Health Records Sharing Scheme based on Partially Polic...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Acheampong, Edward Mensah Zhou, Shijie Liao, Yongjian Antwi-Boasiako, Emmanuel Obiri, Isaac Amankona School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

With the rapid innovation of cloud computing technologies, which has enhanced the application of the Internet of Things (IoT), smart health (s-health) is expected to enhance the quality of the healthcare system. However, s-health records (SHRs) outsourcing, storage, and sharing via a cloud server must be protected and users attribute privacy issues from the public domain. Ciphertext policy attribute-based encryption (CP-ABE) is the cryptographic primitive which is promising to provide fine-grained access control in the cloud environment. However, the direct application of traditional CP-ABE has brought a lot of security issues like attributes' privacy violations and vulnerability in the future by potential powerful attackers like side-channel and cold-bot attacks. To solve these problems, a lot of CP-ABE schemes have been proposed but none of them concurrently support partially policy-hidden and leakage resilience. Hence, we propose a new Smart Health Records Sharing Scheme that will be based on Partially Policy-Hidden CP-ABE with Leakage Resilience which is resilient to bound leakage from each of many secret keys per user, as well as many master keys, and ensure attribute privacy. Our scheme hides attribute values of users in both secret key and ciphertext which contain sensitive information in the cloud environment and are fully secure in the standard model under the static assumptions. © 2022 IEEE.

关键词： Access control

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Label-Free Backdoor Attacks in Vertical Federated Learning 39

Label-Free Backdoor Attacks in Vertical Federated Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shen, Wei Huang, Wenke Wan, Guancheng Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)157735897X

Vertical Federated Learning (VFL) involves multiple clients collaborating to train a global model, with distributed features of shared samples. While it becomes a critical privacy-preserving learning paradigm, its security can be significantly compromised by backdoor attacks, where a malicious client injects a target backdoor by manipulating local data. Existing attack methods in VFL rely on the assumption that the malicious client can obtain additional knowledge about task labels, which is not applicable in VFL. In this work, we investigate a new backdoor attack paradigm in VFL, Label-Free Backdoor Attacks (LFBA), which does not require any additional task label information and is feasible in VFL settings. Specifically, while existing methods assume access to task labels or target-class samples, we demonstrate that the gradients of local embeddings reflect the semantic information of labels. It can be utilized to construct the target poison sample set. Besides, we uncover that backdoor triggers tend to be ignored and under-fitted due to the learning of original features, which hinders backdoor task optimization. To address this, we propose selectively switching poison samples to disrupt feature learning, promoting backdoor task learning while maintaining accuracy on clean data. Extensive experiments demonstrate the effectiveness of our method in various settings. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Federated learning

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Design of Vehicle-cloud Collaborative Framework for 5G Cross-border Multi-PLAN Scenarios 8

Design of Vehicle-cloud Collaborative Framework for 5G Cross...

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8th Annual International Conference on Network and Information Systems for computers, ICNISC 2022

作者： Du, Guotao Chu, Jinlong Ma, Jing Ning, Shiduo School of Mechanical Engineering Dalian University of Technology Dut Dalian China School of Engineering Computer & Mathematical Sciences Auckland University of Technology Department of Computer Science & Software Engineering Auckland New Zealand

ISBN: (纸本)9781665453516

The 5G-MOBIX project applies 5G network communication technology and defines five user stories and specific user scenarios in order to solve the cross-border problem of 5G driverless vehicles. Combined with the remote control user scenario in the remote driving user case, this paper explains the interconnect delay problem between PLMNS in this scenario. From the perspective of infrastructure, that is, the cooperation between vehicle infrastructure and cloud infrastructure, the CCAM architecture of remote control user story simulation test scenario is designed. The proposed solution of direct interconnection is verified and tested, and the cross-border test is simulated at the test site in China. The final test results show that the solution is feasible. © 2022 IEEE.

关键词： Remote control

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ScaleMatch: Multi-scale Consistency Enhancement for Semi-supervised Semantic Segmentation 39

ScaleMatch: Multi-scale Consistency Enhancement for Semi-sup...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lv, Liang Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)157735897X

Semi-supervised learning improves semantic segmentation performance by leveraging unlabeled data, thereby significantly reducing labeling costs. Previous semi-supervised semantic segmentation (S4) methods explored perturbations at the image level but neglected to adequately utilize multi-scale information. When labeled information is insufficient, the scale variation between different objects makes learning instances with extreme scales even more difficult. To address this issue, we propose ScaleMatch, which aims to learn scale-invariant features by obtaining a mixed dual-scale pseudo-label and scale consistency learning. Specifically, the cross-scale interaction fusion (CIF) module enforces interactive information across different scaled-views, allowing for more reliable pseudo-label generation. More importantly, ScaleMatch introduces variable scale branches to utilize scale-invariant supervision. It consists of image-level scale variation consistency (ISVC) and feature-level scale variation consistency (FSVC). Consequently, our ScaleMatch enhances the model’s generalization under scale variation, outperforming existing state-of-the-art methods on both the Pascal VOC and Cityscapes datasets under various partition protocols. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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