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An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Decision Analytics Journal 2024年 11卷

作者： Younas, Faizan Raza, Ali Thalji, Nisrean Abualigah, Laith Zitar, Raed Abu Jia, Heming Department of Computer Science & Information Technology The University Of Lahore Lahore 54000 Pakistan Department of Software Engineering The University Of Lahore Lahore 54000 Pakistan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi 38044 United Arab Emirates School of Information Engineering Sanming University Sanming 365004 China

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim's browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications. © 2024 The Author(s)

关键词： Artificial intelligence Cross-site scripting attacks Deep learning Feature engineering Feature fusion Machine learning

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NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

arXiv

引用

arXiv 2022年

作者： Meng, Fei Chen, Liangliang Ma, Han Wang, Jiankun Meng, Max Q.-H. The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen518057 China

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-andconquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven flowing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the stateof-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints. Note to Practitioners-This article is motivated by developing an efficient risk-aware path planner that can quickly find risk bounded solutions in uncertain nonconvex environments for practical applications, such as surgical navigation and delivery in crowded squares. Sampling-based planning approaches such as rapidly-exploring random tree (RRT) and its variants are popular for their good performance in exploring the state space. However, it is quite time-consuming to look for risk bounded paths in uncertain environments, especially under no

关键词： Motion planning

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Sparse decomposition of pressure pulse wave signal based on time frequency analysis

Sparse decomposition of pressure pulse wave signal based on ...

引用

International Conference on Intelligent Informatics and Biomedical sciences (ICIIBMS)

作者： Zhixing Jiang Guangming Lu David Zhang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

In traditional Chinese medicine (TCM), wrist pulse is of great significance to help doctors in diagnosis. With the development of sensing technology, the computerized wrist pulse analysis has been attracting more attention in modern medicine for its non-invasive and convenient. Considering the TCM pulse diagnosis theory, it is necessary to develop effective feature extraction methods for computerized diagnosis. In this paper, we decompose the pressure pulse waveform of the radial artery to several components by sparse decomposition with Gabor function. In order to better represent the pulse waveform signal, we use an Gabor function based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with the conventional representation methods, the shape of the Gabor function is more variable, which can better represent both the contour and specific peaks. In addition, due to the limitation of the windowing, the Gabor function can reduce the influence on other positions when representing specific position. The feature vector composed of the decomposed components can be used for the computerized pulse signal analysis and disease diagnosis. The experimental results show that the proposed method can exhibit superior performance in distinguishing between the signals collected from patients and healthy individuals.

关键词： Wrist Time-frequency analysis Dictionaries Shape Physiology Sensors Signal analysis

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On Session Continuation among Slices for Inter-Slice Mobility Support in 3GPP Service-based Architecture

arXiv

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arXiv 2020年

作者： Sajjad, Muhammad Mohtasim Jayalath, Dhammika Tian, Yu-Chu Bernardos, Carlos J. School of Electrical Engineering and Robotics School of Computer Science Queensland University of Technology Brisbane Australia Universidad Carlos III de Madrid Spain

The 3GPP has provided its first standard specifications for network slicing in the recent Release 15. The fundamental principles are specified which constitute the standard network slicing framework. These specifications, however, lack the session continuation mechanisms among slices, which is a fundamental requirement to achieve inter-slice mobility. In this paper, we propose three solutions which enable session continuation among slices in the current 3GPP network slicing framework. These solutions are based on existing, well-established standard mechanisms. The first solution is based on the Return Routability/Binding Update (RR/BU) procedure of the popular Internet standard, Mobile IPv6 (MIPv6). The second solution is based on the 3GPP standard GPRS Tunnelling Protocol User Plane (GTPv1-U), which establishes a GTP tunnel between previous and new slice for session continuation. The third solution is a hybrid solution of both MIPv6-RR/BU and GTPv1-U protocols. We compare the performance of all these solutions through analytical modelling. Results show that the GTPv1-U based and the hybrid MIPv6/GTPv1-U solutions promise lower service disruption latency, however, incur higher resource utilization overhead compared to MIPv6-RR/BU and 3GPP standard PDU Session Establishment process. Copyright © 2020, The Authors. All rights reserved.

关键词： Standardization

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通用最优轨迹规划——基于最小作用量原理实现自动驾驶（英文）

引用

Engineering 2024年第2期 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua University Department of Cognitive Robotics Faculty of Mechanical Maritime and Materials Engineering (3mE) Delft University of Technology Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Xi'an Institute of High-Technology

This study presents a general optimal trajectory planning(GOTP) framework for autonomous vehicles(AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers' behavior and summarizing their manipulation characteristics of "seeking benefits and avoiding losses." Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework's feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multisource interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers' manipulation.

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Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

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Engineering 2020年第7期6卷 789-800页

作者： Gaoqi Liang Guolong Liu Junhua Zhao Yanli Liu Jinjin Gu Guangzhong Sun Zhaoyang Dong School of Science and Engineering Chinese University of Hong KongShenzhenShenzhen 518172China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518172China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSW 2052Australia

The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply *** cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high *** complete system state data are needed to support high-frequency state *** data completeness problem for smart grid state estimation is therefore studied in this *** problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter *** proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data *** studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

关键词： State estimation Low-frequency data High-frequency data Super resolution perception Data completeness

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A Novel Tactile Sensing Skin for Surface Perception

引用

IEEE Sensors Journal 2025年

作者： Das, Subham Thiyagarajan, Karthick Kodagoda, Sarath Krishnan, Athul Bhattacharjee, Mitradip Indian Institute of Science Education and Research Bhopal i-Labs Electrical Engineering and Computer Science Department Madhya Pradesh 462066 India Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment PenrithNSW2751 Australia UTS Robotics Institute University of Technology Sydney Australia SydneyNSW2007 Australia

Humans employ a fusion of pressure and temperature signals to perceive tactile stimuli. While replicating the complexity of human tactile sensing poses challenges, advancements in artificial tactile sensing skins play a crucial role in the progress of robotics and prosthetics. Here, we present a novel tactile sensing skin designed for the perception of diverse surfaces. It utilizes a microstructured porous tactile sensor, exhibiting notable sensitivity at 21.5 kPa-1 under low-pressure conditions and 2.5 kPa-1 in high-pressure scenarios. The sensor also boasts an impressively low detection limit of 2.5 Pa, allowing for discernment of subtle pressure variations. Simultaneously, we utilize a flexible temperature sensor exhibiting a sensitivity of 0.58 % ∘C-1 and swift response attributes to facilitate the detection of radiated heat from diverse surfaces. The incorporation of a multilayer perceptron algorithm into the developed glove-like system, equipped with tactile and temperature sensors, achieves 87% accuracy in classifying eleven distinct surfaces. © 2001-2012 IEEE.

关键词： Tactile sensors

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Grand Challenges in the Verification of Autonomous Systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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Point Cloud Segmentation via Edge-fused Local Graph Learning

Point Cloud Segmentation via Edge-fused Local Graph Learning

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Mengtao Han Yaochen Li Liangyu Zuo Qiao Li Chi Zhang Yuanqi Su Ping Li School of Software Engineering Xi’an Jiaotong University Xi’an China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China China Academy of Railway Sciences Corporation Limited Beijing China

Traditional convolution for capturing local structures and relationships remains a key technical limit in 3D semantic segmentation, which neglects the certain influence of the adjacent points on the central point in the disordered local point clouds. In this paper, we propose a novel joint-edge graph convolution neural network (JEGCN), which can extract the dynamic features of each local area and transfer the edge information between the vertex pairs to the adjacent vertices. In the proposed graph convolution module, the adjacent vertices are selected with high classification confidence which can guide the central vertex, and then reweight these vertices. Considering the lack of texture features in 3D point clouds, we incorporate 2D image features to adjacent feature propagation to effectively extract the local and global features of point clouds. The experimental results based on ScanNet and S3DIS datasets demonstrate the effectiveness of the proposed method.

关键词： Solid modeling Image segmentation Image texture Three-dimensional displays Convolution Image edge detection Semantics

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Covert model poisoning against federated learning: Algorithm design and optimization

arXiv

引用

arXiv 2021年

作者： Wei, Kang Li, Jun Ding, Ming Ma, Chuan Jeon, Yo-Seb Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk634050 Russia Data61 CSIRO Sydney Australia Pohang Korea Republic of Department of Electrical Engineering Princeton University NJ United States

Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP). In this paper, we aim to propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms (e.g., Krum and Trimmed mean) implemented at the server without being noticed, i.e., covert MP (CMP). Specifically, we first formulate the MP as an optimization problem by minimizing the Euclidean distance between the manipulated model and designated one, constrained by a defensive aggregation rule. Then, we develop CMP algorithms against different defensive mechanisms based on the solutions of their corresponding optimization problems. Furthermore, to reduce the optimization complexity, we propose low complexity CMP algorithms with a slight performance degradation. In the case that the attacker does not know the defensive aggregation mechanism, we design a blind CMP algorithm, in which the manipulated model will be adjusted properly according to the aggregated model generated by the unknown defensive aggregation. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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