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Enhancing Security through Modularization: A Counterfactual Analysis of Vulnerability Propagation and Detection Precision

Enhancing Security through Modularization: A Counterfactual ...

引用

IEEE International Workshop on Source Code Analysis and Manipulation

作者： Mohammad Mahdi Abdollahpour Jens Dietrich Patrick Lam University of Waterloo Victoria University of Wellington

ISBN: (数字)9798331528508

ISBN: (纸本)9798331528515

In today's software development landscape, the use of third-party libraries is near-ubiquitous; leveraging third-party libraries can significantly accelerate development, allowing teams to implement complex functionalities without reinventing the wheel. However, one significant cost of reusing code is security vulnerabilities. Vulnerabilities in third-party libraries have allowed attackers to breach databases, conduct identity theft, steal sensitive user data, and launch mass phishing campaigns. Notorious examples of vulnerabilities in libraries from the past few years include log4shell, solarwinds, event-stream, lodash, and equifax. Existing software composition analysis (SCA) tools track the propagation of vulnerabilities from libraries through dependencies to downstream clients and alert those clients. Due to their design, many existing tools are highly imprecise―they create alerts for clients even when the flagged vulnerabilities are not exploitable. Library developers occasionally release new versions of their software with refactorings that improve modularity. In this work, we explore the impacts of modularity improvements on vulnerability detection. In addition to generally improving the nonfunctional properties of the code, refactoring also has several security-related beneficial side effects: (1) it improves the precision of existing (fast and stable) SCAs; and (2) it protects from vulnerabilities that are exploitable when the vulnerable code is present and not even reachable, as in gadget chain attacks. Our primary contribution is thus to quantify, using a novel simulation-based counterfactual vulnerability analysis, two main ways that improved modularity can boost security. We propose a modularization method using a DAG partitioning algorithm, and statically measure properties of systems that we (synthetically) modularize. In our experiments, we find that modularization can improve precision of Software Composition Analysis (SCA) tools to 71%, up from 35%

关键词： Codes Source coding Phishing Software algorithms Wheels Libraries Software partitioning algorithms Security Software development management

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Distributed Reactive Power Optimization for Active Distribution Network Based on Network Partition

Distributed Reactive Power Optimization for Active Distribut...

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Electrical Power Systems and Intelligent Control (EPSIC), International Conference on

作者： Jinzhou Zhu Qingren Jin Xinlei Cai Lei Yu Zijie Meng Tong Liu Zhenfan Yu Power Dispatching and Controlling Center of Guangdong Power Grid Company Limited Guangdong Power Grid Company Limited Guangzhou China Electrical Power Research Institute China Southern Power Grid (CSG) Guangzhou China

ISBN: (数字)9798350366075

ISBN: (纸本)9798350366082

Active distribution networks (ADNs) serve as a linchpin in contemporary power systems, tasked with accommo-dating the rising penetration of renewable energy sources while ensuring efficient and reliable operation. This paper proposes a network partition-based framework for distributed reactive power optimization in active distribution networks, with the minimization of network losses during the operation of active distribution networks as the optimization objective. This paper constructs a distributed reactive power optimization model that includes both continuous and discrete devices, such as on-load tap changer (OLTC) and capacitor banks (CB). Moreover, a solution method based on the alternating optimization process (AOP) and alternating direction method of multipliers (ADMM) is introduced. Case study simulations validate the effectiveness of the proposed reactive power optimization algorithm.

关键词： Reactive power Renewable energy sources Capacitors Convex functions partitioning algorithms Power system reliability Active distribution networks Reliability On load tap changers Optimization

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Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization

Audio Spotforming Using Nonnegative Tensor Factorization wit...

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European Signal Processing Conference (EUSIPCO)

作者： Shoma Ayano Li Li Shogo Seki Daichi Kitamura National Institute of Technology Kagawa Japan CyberAgent Inc. Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use.

关键词： Tensors Array signal processing Signal processing algorithms Europe Clustering algorithms Interference Microphone arrays Vectors partitioning algorithms

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Learners’ Privacy-Preserving Scheme for Ranking Data Sensitivity in Mobile Learning System

Learners’ Privacy-Preserving Scheme for Ranking Data Sensit...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Muhammad Kudu Muhammad Olayemi Mikail Olaniyi Francis Osang Ishaq Oyebisi Oyefolahan Ojeniyi Joseph Adebayo Idris Mohammed Kolo Department of Computer Science Federal University Technology Minna Nigeria Department of Cyber Security National Open University of Nigeria Abuja Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Cyber Security Federal University Technology Minna Nigeria

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Mobile Learning System (MLS) is facing new challenges in terms of privacy, such as data collection, storage, and sharing because of the core infrastructure and network that enables cloud computing services. Most of the data exchange in Mobile Learning System (MLS) require mandatory authorisation to allow access to the learners’ information in the MLS. Therefore, this article attempts to rank learners' sensitive attributes stored in MLS. Thus, concerns about privacy breaches motivated this paper to adopt an attributes partitioning strategy into the sensitive attributes to enforce privacy during learners’ profile information access. The article adopted the informed consent phenomenon to determine and formulate learners’ data privacy attributes sensitivity using the Fuzzy Analytic Hierarchy Process (FAHP) Algorithm. Results from the implemented Learners’ Privacy Preserving (LPP) Algorithm determined normalized weights of top-five rank-selected learners’ sensitive data to include: Browsing History (1 ST , Ranked), Geolocation Data (2 ND , Ranked), IP Address (3 RD , Ranked), web Browser (4 TH , Ranked), Medical Records (5 TH , Ranked) and CGPA (10 TH , Ranked) respectively. This indicates that these five most sensitive features are at risk and require protection to prevent privacy breaches, thus ensuring privacy preservation that prevents unauthorised access to learners’ sensitive data in the mobile learning system environment. The ranking of sensitive data in this paper could serve as inspiration for future research work on mobile learning security to improve the privacy of sensitive attributes in MLS environment

关键词： Authorization Data privacy Sensitivity Geology Privacy breach Mobile learning partitioning algorithms Browsers IP networks History

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hualei Zhang Jun Du Xiangwang Hou Chunxiao Jiang Jintao Wang Dusit Niyato Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

关键词： Training Wireless communication Analytical models Accuracy Federated learning Computational modeling Computer architecture partitioning algorithms Internet of Things Convergence

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A Novel Approach for Traveling Salesman Problem Via Probe Machine

A Novel Approach for Traveling Salesman Problem Via Probe Ma...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Changfeng Duan Jing Liu Jin Xu Dongdong An Yu Bao Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High Confidence Software Technologies Peking University Beijing China Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

The Traveling Salesman Problem is a combinatorial optimization problem that seeks to find the shortest path visiting a set of locations, where each location is visited exactly once, and the path returns to the starting point. Using traditional computing model to solve Traveling Salesman Problem would face the issue of state explosion. This paper proposes a solving method based on the probe machine model, greatly accelerating the solving speed. By iteratively adding probes and performing probe operations in sequence, both optimal and feasible solutions for this problem can be obtained. We developed a solver PROBE4TSP and presented its framework and execution process. Through comparative experiments, we demonstrate that this method is faster than some classical solvers for small-scale Traveling Salesman Problems, especially fewer than thirty nodes.

关键词： Computational modeling Urban areas Transforms Traveling salesman problems Parallel processing Libraries partitioning algorithms

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Distribution Systems Clustering Through Voltage Unbalance Sensitivity to Photovoltaic Presence

Distribution Systems Clustering Through Voltage Unbalance Se...

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Annual Conference of Industrial Electronics Society

作者： Rafael M Leite Mário Oleskovicz Daniel Barbosa Flavio B. Costa Department of Electrical and Computer Engineering University of São Paulo Brazil Department of Electrical and Computer Engineering Federal University of Bahia Brazil Electrical and Computer Engineering Department Michigan Technological Univesity Houghton U.S.A

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Distributed photovoltaic micro and mini-generators are gaining prominence as they become more accessible to end consumers. These systems promise to reduce expenses related to electrical energy and offer potential financial returns. One way to enhance the application and planning of the distribution network and its hosting capacity is by partitioning these systems based on the impact of distributed generation on power quality. This study aims to develop a generic network partitioning method using the Spectral Clustering algorithm, enhanced with a balancing algorithm to ensure similar cluster sizes. The input data for this method come from modeling actual standardized electrical systems and simulating the individual impact of photovoltaic generation on each low-voltage feeder connected to the primary grid. The unbalance sensitivity coefficient, which quantifies the change in the voltage unbalance factor at one node due to the variation of active power at another, is used as a metric. Considering all simulation states, the ratios of these coefficients per node form the elements of the Sensitivity matrix. By applying Balanced Spectral Clustering to this Sensitivity matrix, the method can identify continuous electrical regions that highlight the optimal partitioning of the tested distribution system. This proposed method may serve as an initial framework for defining the hosting capacity of smaller regions, taking into account the characteristics of the entire electrical system while managing its restrictive factors locally.

关键词： Photovoltaic systems Low voltage Sensitivity Limiting Power quality Clustering algorithms Voltage Planning partitioning algorithms Capacity planning

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Research on Improved Adaptive Spectral Clustering HRM-ISC Algorithm

Research on Improved Adaptive Spectral Clustering HRM-ISC Al...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Hongxia Wang Weijian Tong School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

As a crucial component in the realm of data mining, In the past few years, the focus of researchers in the field of data mining has significantly shifted towards the exploration of clustering analysis, garnering widespread interest and recognition, and a considerable multitude of theories and methods have been produced. Based on the conventional clustering algorithm, spectral clustering converts the partition of the data set into the partition of the graph, and can identify the clusters composed of arbitrary shape sample data. The traditional spectral clustering algorithm NJW has problems such as difficult parameter selection in the clustering process and poor clustering effect on non-convex or non-uniform density data sets. In this paper, a hot region mining algorithm built upon improved spectral clustering (HRM-ISC) is suggested. In the first place, The enhancement of the local scale parameter in the self-tuning algorithm is achieved by incorporating the standard deviation of the sample neighborhood, which avoids the interference of the local scale parameter of the sample by noise points and the parameter selection depends on the empirical value to the maximum extent, so that the similarity matrix of the sample reflects the distribution information of the sample more accurately. Secondly, The conventional spectral clustering approach can not automatically estimate the optimal number of clusters and the eigenvector selection is difficult. According to the matrix perturbation theory, an adaptive spectral clustering algorithm utilizing eigengap is used to solve the problem. Finally, to tackle challenge of sensitivity to the initial clustering center encountered in the K-Means algorithm, the K-Medoids algorithm with variance maximization of initial clustering center was employed as a substitute.

关键词： Sensitivity Shape Perturbation methods Noise Clustering algorithms Signal processing algorithms Matrices partitioning algorithms Data mining Standards

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GALA-GNN: Optimization for Training GNNs of Large-scale Graphs on Heterogeneous Platforms

GALA-GNN: Optimization for Training GNNs of Large-scale Grap...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Junfeng Hu Yi Zou Xianfeng Song Yiqiu Liu School of Microelectronics South China University of Technology Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Graph Neural Networks (GNNs) demonstrate remarkable learning efficacy on graph-structured data across various real-world domains. However, due to large-scale graph datasets, it is impractical to train the complete graph on a single GPU. Subgraph-level parallel training has proven to be an effective approach for GNN training on large-scale graphs. Nonetheless, this approach presents certain issues: 1. Due to the absence of neighboring vertices at the boundaries, gradient estimation during the training process may exhibit systemic bias, potentially compromising training accuracy; 2. Existing training frameworks often neglect to consider computational capability differences among trainers when allocating the workloads. We propose GALA-GNN to alleviate the aforementioned issues: 1. We introduce a heuristic edge partitioning algorithm, Neighbor Expansion Simulated Annealing, i.e., NESA, which minimizes the number of vertices with missing neighborhoods in subgraphs. Additionally, it allows for the division of the original graph into subgraphs with uneven workloads by setting a threshold manually; 2. We present Computation-Aware Subgraph Enlarge, which not only prevents significant loss in training accuracy but also reduces idle time for high-computation trainers during the training process, thereby enhancing overall computational resource utilization. We compare our proposed GALA-GNN with the current state-of-the-art GNN training framework, DGL. On the medium-scale dataset Flickr, GALA-GNN achieves up to 7.63x speedup, and on the large-scale dataset ogbn-products, it achieves up to 4.84x speedup, without causing significant loss in training accuracy.

关键词： Training Accuracy Computer aided software engineering Simulated annealing Graph neural networks partitioning algorithms Web sites Synchronization Resource management Optimization

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DeepSample: DNN Sampling-Based Testing for Operational Accuracy Assessment

DeepSample: DNN Sampling-Based Testing for Operational Accur...

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International Conference on Software Engineering (ICSE)

作者： Antonio Guerriero Roberto Pietrantuono Stefano Russo DIETI Università degli Studi di Napoli Federico II Napoli Italy

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Deep Neural Networks (DNN) are core components for classification and regression tasks of many software systems. Companies incur in high costs for testing DNN with datasets representative of the inputs expected in operation, as these need to be manually labelled. The challenge is to select a representative set of test inputs as small as possible to reduce the labelling cost, while sufficing to yield unbiased high-confidence estimates of the expected DNN accuracy. At the same time, testers are interested in exposing as many DNN mispredictions as possible to improve the DNN, ending up in the need for techniques pursuing a threefold aim: small dataset size, trustworthy estimates, mispredictions exposure. This study presents DeepSample, a family of DNN testing techniques for cost-effective accuracy assessment based on probabilistic sampling. We investigate whether, to what extent, and under which conditions probabilistic sampling can help to tackle the outlined challenge. We implement five new sampling-based testing techniques, and perform a comprehensive comparison of such techniques and of three further state-of-the-art techniques for both DNN classification and regression tasks. Results serve as guidance for best use of sampling-based testing for faithful and high-confidence estimates of DNN accuracy in operation at low cost.

关键词： Costs Estimation Artificial neural networks Probabilistic logic Software systems partitioning algorithms Labeling

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