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Malware algorithm classification method based on big data analysis

INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES 2017年第1期13卷 112-130页

作者： Zhao, Jingling Chen, Shilei Cao, Mengchen Cui, Baojiang Beijing Univ Posts & Telecommun Sch Comp Sci Natl Engn Lab Mobile Network Secur Beijing 100876 Peoples R China

Internet technology has greatly increased the number of malware attacks on networks. Consequently, it has also elevated the importance of automatic malware detection and classification technology based on big data analysis in the field of information security. This paper presents a new method for classifying malware algorithms that exhibits both high accuracy and high coverage. The method combines big data analysis with software security technologies such as feature extraction, machine learning, binary instrumentation and dynamic instruction flow analysis to achieve automated classification of malware algorithms. 20 classification experiments prove the correctness of the method. We also discuss future directions for improving the method.

关键词： malware analysis algorithm classification big data feature extraction machine learning binary instrumentation dynamic instruction flow

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Bilateral Dependency Neural Networks for Cross-Language algorithm classification 26

Bilateral Dependency Neural Networks for Cross-Language Algo...

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26th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Bui, Nghi D. Q. Yu, Yijun Jiang, Lingxiao Singapore Management Univ Sch Informat Syst Singapore Singapore Open Univ Ctr Res Comp Milton Keynes Bucks England

ISBN: (纸本)9781728105918

algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware detection. Code similarity metrics, on the basis of features extracted from syntax and semantics, have been used to classify programs. Such features, however, often need manual selection effort and are specific to individual programming languages, limiting the classifiers to programs in the same language. To recognize the similarities and differences among algorithms implemented in different languages, this paper describes a framework of Bilateral Neural Networks (Bi-NN) that builds a neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language. A whole Bi-NN can be trained with bilateral programs that implement the same algorithms and/or data structures in different languages and then be applied to recognize algorithm classes across languages. We have instantiated the framework with several kinds of token-, tree-and graph-based neural networks that encode and learn various kinds of information in code. We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures. Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages, and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further. In particular, our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated. Our study points to a possible future research direction to tailor bilateral and mu

关键词： cross-language mapping program classification algorithm classification code embedding code dependency neural network bilateral neural network

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Research on intelligent classification and grade regression prediction of metal ores in complex marine environments

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MARINE GEORESOURCES & GEOTECHNOLOGY 2025年

作者： Wang, Jianhua Yin, Tubing Dai, Hao Ma, Jiexin Cent South Univ Sch Resources & Secur Engn Changsha Peoples R China Shenzhen Port Grp Co Ltd Shenzhen Peoples R China

To address the high cost and complexity of marine exploration technology, this study proposes a novel approach that utilizes an underwater robot equipped with a metal detector to identify ore types and grades on the seabed directly. By integrating laboratory physical experiments, computer vision technology, classification algorithms, and mathematical regression analysis, multiple types of metal ores are classified, and the grade of major minerals is predicted under the signal attenuation effects of PVC boards and seawater. The key findings are as follows: improved Random Forest, Logistic Regression, AdaBoost, and SVC models were employed to classify the ore spectral images obtained from laboratory experiments. Among the four models, the Random Forest model had the lowest test accuracy, approximately 0.91, while both the Logistic Regression and AdaBoost models achieved slightly higher accuracies, around 0.94. The SVC model demonstrated the best performance, with the highest test accuracy, close to 0.97. Additionally, as the thickness of the PVC board increased, the grade of the primary elements in the test samples exhibited an exponential decline, with a rapid initial attenuation. However, with further increases in PVC board thickness, the downward trend of mineral grade became more gradual, and the rate of change decreased.

关键词： Marine exploration Mineral grade algorithm classification Support vector machine regression prediction

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Reproducibility of measurements and variability of the classification algorithm of Stratus OCT in normal, hypertensive, and glaucomatous patients

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CLINICAL OPHTHALMOLOGY 2009年第1期3卷 139-145页

作者： Anton, Alfonso Castany, Marta Pazos-Lopez, Marta Cuadrado, Ruben Flores, Ana Castilla, Miguel Hosp del Mar IMAS Hosp Esperanza Barcelona Spain ICR Glaucoma Dept Barcelona Spain Univ Valladolid Inst Univ Oftalmobiol Aplicada IOBA Valladolid Spain

Purpose: To assess the reproducibility of retinal nerve fiber layer (SRNFL) measurements and the variability of the probabilistic classification algorithm in normal, hypertensive and glaucomatous eyes using Stratus optical coherence tomography (OCT). Methods: Forty-nine eyes (13 normal, 17 ocular hypertensive [OHT] and 19 glaucomatous) of 49 subjects were included in this study. RNFL was determined with Stratus OCT using the standard protocol RNFL thickness 3.4. Three different images of each eye were taken consecutively during the same session. To evaluate OCT reproducibility, coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated for average thickness (AvgT), superior average thickness (Savg), and inferior average thickness (Iavg) parameters. The variability of the results of the probabilistic classification algorithm, based on the OCT normative database, was also analyzed. The percentage of eyes with changes in the category assigned was calculated for each group. Results: The 50th percentile of COV was 2.96%, 4.00%, and 4.31% for AvgT, Savg, and Iavg, respectively. Glaucoma group presented the largest COV for all three parameters (3.87%, 5.55%, 7.82%). ICC were greater than 0.75 for almost all measures (except from the inferior thickness parameter in the normal group;ICC = 0.64, 95% CI 0.334-0.857). Regarding the probabilistic classification algorithm for the three parameters (AvgT, Savg, Iavg), the percentage of eyes without color-code category changes among the three images was as follows: normal group, 100%, 84.6% and 92%;OHT group, 89.5%, 52.7%, 79%;and Glaucoma group, 82%, 70.6%, and 76.5%, respectively. A probabilistic category switch from pathologic to normal or vice versa was observed in three eyes (15.8%) of the glaucomatous group for the Savg parameter and in two eyes of the OHT group: one eye (5,9%) for the AvgT and one eye (5.9%) for the Savg parameter. Conclusions: OCT RNFL measurements showed a good reproducibili

关键词： optical coherence tomography glaucoma nerve fiber layer algorithm classification reproducibility

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Extended Species for Code Parallelization Through algorithmic classification 5

Extended Species for Code Parallelization Through Algorithmi...

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IEEE International Advance Computing Conference (IACC 2015)

作者： Mustafa, B. Ahmed, Waseem Bearys Inst Technol Dept Comp Sci & Engn Mangalore India HKBK Coll Engn Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781479980475

Multicore systems along with GPUs enabled to increase the parallelism extensively. Few compilers are enhanced to emerging issues with respect to threading and synchronization. Proper classification of algorithms and programs will benefit largely to the community of programmers to get chances for efficient parallelization. In this work we analyzed the existing species for algorithm classification, where we discuss the classification of related work and compare the amount of problems which are difficult for classification. We have selected set of algorithms which resemble in structure for various problems but perform given specific tasks. These algorithms are tested using existing tools such as Bones compiler and A-Darwin, an automatic species extraction tool. The access patterns are produced for various algorithmic kernels by running against A-Darwin and analysis is done for various code segments. We have identified that all the algorithms cannot be classified using only existing patterns and created new set of access patterns.

关键词： Parallel Programming Bones Access Patterns algorithm classification A-Darwin

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Evaluation and comparison of different segmentation algorithms

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PATTERN RECOGNITION LETTERS 1997年第10期18卷 963-974页

作者： Zhang, YJ Tsing Hua Univ Dept Elect Engn Beijing 100084 Peoples R China

This paper presents an objective and quantitative study of segmentation algorithms. This study is distinguished from other studies by considering both evaluation and comparison, treating algorithms selected from distinct technique groups as well as using carefully designed synthetic images for the test experiments. All these characteristics make this study a general and effective one for revealing the performance of segmentation algorithms. (C) 1997 Elsevier Science B.V.

关键词： image segmentation segmentation evaluation segmentation comparison algorithm classification synthetic image performance assessment

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A Hybrid Machine Learning Model for Code Optimization

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2023年第6期51卷 309-331页

作者： Hakimi, Yacine Baghdadi, Riyadh Challal, Yacine Ecole Natl Super Informat Lab Methodes Concept Syst LMCS BP 68M Oued Smar 16309 Algeria MIT Cambridge MA USA New York Univ Abu Dhabi NYUAD Abu Dhabi U Arab Emirates Univ Doha Sci & Technol UDST Coll Comp & Informat Technol Doha Qatar

The complexity of programming modern heterogeneous systems raises huge challenges. Over the past two decades, researchers have aimed to alleviate these difficulties by employing classical Machine Learning and Deep Learning techniques within compilers to optimize code automatically. This work presents a novel approach to optimize code using at the same time Classical Machine Learning and Deep Learning techniques by maximizing their benefits while mitigating their drawbacks. Our proposed model extracts features from the code using Deep Learning and then applies Classical Machine Learning to map these features to specific outputs for various tasks. The effectiveness of our model is evaluated on three downstream tasks: device mapping, optimal thread coarsening, and algorithm classification. Our experimental results demonstrate that our model outperforms previous models in device mapping with an average accuracy of 91.60% on two datasets and in optimal thread coarsening task where we are the first to achieve a positive speedup on all four platforms while achieving a comparable result of 91.48% in the algorithm classification task. Notably, our approach yields better results even with a small dataset without requiring a pre-training phase or a complex code representation, offering the advantage of reducing training time and data volume requirements.

关键词： Machine learning Deep learning Code optimizations LLVM-IR Heterogeneous platforms Thread coarsening algorithm classification Code mapping

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From driving trajectories to driving paths: a survey on map-matching algorithms

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CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION 2022年第3期4卷 252-267页

作者： Jiang, Linli Chen, Chaoxiong Chen, Chao Huang, Hongyu Guo, Bin Chongqing Univ Coll Comp Sci Chongqing Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

With the widespread deployment of built-in Global Positioning System (GPS) devices, numerous volumes of driving trajectories can be recorded conveniently. Affected by GPS equipment precision and driving environments, raw GPS trajectories will deviate from the paths that vehicles really drove on. Such inaccurate data is not beneficial to the upstream applications. Therefore, map matching is applied to identify the true driving paths in the road network from the GPS trajectories data. A lot of studies in the filed of map matching have been proposed, but there still exist three problems: (1) there lacks a comprehensive review on recent map matching algorithms with new techniques;(2) the existing map-matching algorithms still fail to meet the requirements of both high precision and high efficiency simultaneously;(3) there is a lack of comparison between various types of matching algorithms on a unified experimental environment. In this paper, we review the existing matching algorithms and propose a new categorisation based on their methodologies. The proposed categorisation can better reveal their properties and facilitate the future utilization. In addition, we conduct an experimental comparison among four representative algorithms to give a deep insight to the properties of different categories. Experimental results reveal the importance of some solutions to improve matching accuracy and efficiency.

关键词： Map matching algorithm classification GPS trajectory Survey

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Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and classification

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SENSORS 2021年第5期21卷 1832页

作者： Mijuskovic, Adriana Chiumento, Alessandro Bemthuis, Rob Aldea, Adina Havinga, Paul Univ Twente Dept Pervas Syst NL-7522 NB Enschede Netherlands Univ Twente Dept Ind Engn & Business Informat Syst NL-7522 NB Enschede Netherlands

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

关键词： resource management cloud computing fog computing edge computing algorithm classification evaluation framework

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algorithmic Species: A classification of Affine Loop Nests for Parallel Programming

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2013年第4期9卷 40-40页

作者： Nugteren, Cedric Custers, Pieter Corporaal, Henk Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands

Code generation and programming have become ever more challenging over the last decade due to the shift towards parallel processing. Emerging processor architectures such as multi-cores and GPUs exploit increasingly parallelism, requiring programmers and compilers to deal with aspects such as threading, concurrency, synchronization, and complex memory partitioning. We advocate that programmers and compilers can greatly benefit from a structured classification of program code. Such a classification can help programmers to find opportunities for parallelization, reason about their code, and interact with other programmers. Similarly, parallelising compilers and source-to-source compilers can take threading and optimization decisions based on the same classification. In this work, we introduce algorithmic species, a classification of affine loop nests based on the polyhedral model and targeted for both automatic and manual use. Individual classes capture information such as the structure of parallelism and the data reuse. To make the classification applicable for manual use, a basic vocabulary forms the base for the creation of a set of intuitive classes. To demonstrate the use of algorithmic species, we identify 115 classes in a benchmark set. Additionally, we demonstrate the suitability of algorithmic species for automated uses by showing a tool to automatically extract species from program code, a species-based source-to-source compiler, and a species-based performance prediction model.

关键词： Performance Languages algorithm classification parallel programming polyhedral model

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