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Articles 4441 - 4470 of 8495
Full-Text Articles in Computer Sciences
Automatic Loop-Invariant Generation And Refinement Through Selective Sampling, Jiaying Li, Jun Sun, Li Li, Quang Loc Le, Shang-Wei Lin
Automatic Loop-Invariant Generation And Refinement Through Selective Sampling, Jiaying Li, Jun Sun, Li Li, Quang Loc Le, Shang-Wei Lin
Research Collection School Of Computing and Information Systems
Automatic loop-invariant generation is important in program analysis and verification. In this paper, we propose to generate loop-invariants automatically through learning and verification. Given a Hoare triple of a program containing a loop, we start with randomly testing the program, collect program states at run-time and categorize them based on whether they satisfy the invariant to be discovered. Next, classification techniques are employed to generate a candidate loop-invariant automatically. Afterwards, we refine the candidate through selective sampling so as to overcome the lack of sufficient test cases. Only after a candidate invariant cannot be improved further through selective sampling, we …
Fib: Squeezing Loop Invariants By Interpolation Between Forward/Backward Predicate Transformers, Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sana, Henri Hansen
Fib: Squeezing Loop Invariants By Interpolation Between Forward/Backward Predicate Transformers, Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sana, Henri Hansen
Research Collection School Of Computing and Information Systems
Loop invariant generation is a fundamental problem in program analysis and verification. In this work, we propose a new approach to automatically constructing inductive loop invariants. The key idea is to aggressively squeeze an inductive invariant based on Craig interpolants between forward and backward reachability analysis. We have evaluated our approach by a set of loop benchmarks, and experimental results show that our approach is promising.
Mining Implicit Design Templates For Actionable Code Reuse, Yun Lin, Guozhu Meng, Yinxing Yue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, Jin Song Dong
Mining Implicit Design Templates For Actionable Code Reuse, Yun Lin, Guozhu Meng, Yinxing Yue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, Jin Song Dong
Research Collection School Of Computing and Information Systems
In this paper, we propose an approach to detecting project-specific recurring designs in code base and abstracting them into design templates as reuse opportunities. The mined templates allow programmers to make further customization for generating new code. The generated code involves the code skeleton of recurring design as well as the semi-implemented code bodies annotated with comments to remind programmers of necessary modification. We implemented our approach as an Eclipse plugin called MICoDe. We evaluated our approach with a reuse simulation experiment and a user study involving 16 participants. The results of our simulation experiment on 10 open source Java …
Improving Probability Estimation Through Active Probabilistic Model Learning, Jingyi Wang, Xiaohong Chen, Jun Sun, Shengchao Qin
Improving Probability Estimation Through Active Probabilistic Model Learning, Jingyi Wang, Xiaohong Chen, Jun Sun, Shengchao Qin
Research Collection School Of Computing and Information Systems
It is often necessary to estimate the probability of certain events occurring in a system. For instance, knowing the probability of events triggering a shutdown sequence allows us to estimate the availability of the system. One approach is to run the system multiple times and then construct a probabilistic model to estimate the probability. When the probability of the event to be estimated is low, many system runs are necessary in order to generate an accurate estimation. For complex cyber-physical systems, each system run is costly and time-consuming, and thus it is important to reduce the number of system runs …
Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw
Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via …
Indexable Bayesian Personalized Ranking For Efficient Top-K Recommendation, Dung D. Le, Hady W. Lauw
Indexable Bayesian Personalized Ranking For Efficient Top-K Recommendation, Dung D. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Top-k recommendation seeks to deliver a personalized recommendation list of k items to a user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer, and (2) efficiency in constructing the recommendation list in real time. One direction towards retrieval efficiency is to formulate retrieval as approximate k nearest neighbor (kNN) search aided by indexing schemes, such as locality-sensitive hashing, spatial trees, and inverted index. These schemes, applied on the output representations of recommendation algorithms, speed up the retrieval process by automatically discarding a large number of potentially irrelevant items when given a user …
Guest Editor's Introduction To The Special Issue On Source Code Analysis And Manipulation (Scam 2015), Foutse Khomh, David Lo, Michael W. Godfrey
Guest Editor's Introduction To The Special Issue On Source Code Analysis And Manipulation (Scam 2015), Foutse Khomh, David Lo, Michael W. Godfrey
Research Collection School Of Computing and Information Systems
We are happy to introduce you to this special issue that presents selected papers from the 15th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2015). SCAM is a leading conference that brings together researchers and practitioners working on theory, techniques, and applications that concern analysis and/or manipulation of the source code of computer systems. While much attention in the wider software engineering community is properly directed towards other aspects of systems development and evolution, such as specification, design, and requirements engineering, it is the source code that contains the only precise description of the behavior of …
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto
Research Collection School Of Computing and Information Systems
This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure …
Eeg-Based Emotion Recognition Via Fast And Robust Feature Smoothing, Cheng Tang, Di Wang, Ah-Hwee Tan, Chunyan Miao
Eeg-Based Emotion Recognition Via Fast And Robust Feature Smoothing, Cheng Tang, Di Wang, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Electroencephalograph (EEG) signals reveal much of our brain states and have been widely used in emotion recognition. However, the recognition accuracy is hardly ideal mainly due to the following reasons: (i) the features extracted from EEG signals may not solely reflect one’s emotional patterns and their quality is easily affected by noise; and (ii) increasing feature dimension may enhance the recognition accuracy, but it often requires extra computation time. In this paper, we propose a feature smoothing method to alleviate the aforementioned problems. Specifically, we extract six statistical features from raw EEG signals and apply a simple yet cost-effective feature …
Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo
Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In this paper, we describe the systems developed for Video-to-Text (VTT), Ad-hoc Video Search (AVS) and Video Hyper-linking (LNK) tasks at TRECVID 2017 [1] and the achieved results.
Leveraging Social Analytics Data For Identifying Customer Segments For Online News Media, Jansen, Bernard J, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Haewoon Kwak
Leveraging Social Analytics Data For Identifying Customer Segments For Online News Media, Jansen, Bernard J, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Haewoon Kwak
Research Collection School Of Computing and Information Systems
In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, presenting a holistic picture of the customer base of an organization. We validate our methodology via the implementation of a working system that rapidly and in near real-time processes tens of millions of online customer interactions with content posted on …
Color-Sketch Simulator: A Guide For Color-Based Visual Known-Item Search, Jakub Lokoč, Anh Nguyen Phuong, Marta Vomlelová, Chong-Wah Ngo
Color-Sketch Simulator: A Guide For Color-Based Visual Known-Item Search, Jakub Lokoč, Anh Nguyen Phuong, Marta Vomlelová, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In order to evaluate the effectiveness of a color-sketch retrieval system for a given multimedia database, tedious evaluations involving real users are required as users are in the center of query sketch formulation. However, without any prior knowledge about the bottlenecks of the underlying sketch-based retrieval model, the evaluations may focus on wrong settings and thus miss the desired effect. Furthermore, users have usually no clues or recommendations to draw color-sketches effectively. In this paper, we aim at a preliminary analysis to identify potential bottlenecks of a flexible color-sketch retrieval model. We present a formal framework based on position-color feature …
Detecting Semantic Uncertainty By Learning Hedge Cues In Sentences Using An Hmm, Xiujun Li, Wei Gao, Jude Shavlik
Detecting Semantic Uncertainty By Learning Hedge Cues In Sentences Using An Hmm, Xiujun Li, Wei Gao, Jude Shavlik
Research Collection School Of Computing and Information Systems
Detecting speculative assertions is essential to distinguish semantically uncertain information from the factual ones in text. This is critical to the trustworthiness of many intelligent systems that are based on information retrieval and natural language processing techniques, such as question answering or information extraction. We empirically explore three fundamental issues of uncertainty detection: (1) the predictive ability of different learning methods on this task; (2) whether using unlabeled data can lead to a more accurate model; and (3) whether closed-domain training or crossdomain training is better. For these purposes, we adopt two statistical learning approaches to this problem: the commonly …
A Verification Framework For Stateful Security Protocols, Li Li, Naipeng Dong, Jun Pang, Jun Sun, Guangdong Bai, Yang Liu, Jin Song Dong
A Verification Framework For Stateful Security Protocols, Li Li, Naipeng Dong, Jun Pang, Jun Sun, Guangdong Bai, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
A long-standing research problem is how to efficiently verify security protocols with tamper-resistant global states, especially when the global states evolve unboundedly. We propose a protocol specification framework, which facilitates explicit modeling of states and state transformations. On the basis of that, we develop an algorithm for verifying security properties of protocols with unbounded state-evolving, by tracking state transformation and checking the validity of the state-evolving traces. We prove the correctness of the verification algorithm, implement both of the specification framework and the algorithm, and evaluate our implementation using a number of stateful security protocols. The experimental results show that …
A Semantics Comparison Workbench For A Concurrent, Asynchronous, Distributed Programming Language, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt
A Semantics Comparison Workbench For A Concurrent, Asynchronous, Distributed Programming Language, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
A number of high-level languages and libraries have been proposed that offer novel and simple to use abstractions for concurrent, asynchronous, and distributed programming. The execution models that realise them, however, often change over time---whether to improve performance, or to extend them to new language features---potentially affecting behavioural and safety properties of existing programs. This is exemplified by SCOOP, a message-passing approach to concurrent object-oriented programming that has seen multiple changes proposed and implemented, with demonstrable consequences for an idiomatic usage of its core abstraction. We propose a semantics comparison workbench for SCOOP with fully and semi-automatic tools for analysing …
Enabling Phased Array Signal Processing For Mobile Wifi Devices, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu
Enabling Phased Array Signal Processing For Mobile Wifi Devices, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu
Research Collection School Of Computing and Information Systems
Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate …
Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng
Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng
Research Collection School Of Computing and Information Systems
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by …
Understanding Inactive Yet Available Assignees In Github, Jing Jiang, David Lo, Xinyu Ma, Fuli Feng, Li Zhang
Understanding Inactive Yet Available Assignees In Github, Jing Jiang, David Lo, Xinyu Ma, Fuli Feng, Li Zhang
Research Collection School Of Computing and Information Systems
Context In GitHub, an issue or a pull request can be assigned to a specific assignee who is responsible for working on this issue or pull request. Due to the principle of voluntary participation, available assignees may remain inactive in projects. If assignees ever participate in projects, they are active assignees; otherwise, they are inactive yet available assignees (inactive assignees for short). Objective Our objective in this paper is to provide a comprehensive analysis of inactive yet available assignees in GitHub. Method We collect 2,374,474 records of activities in 37 popular projects, and 797,756 records of activities in 687 projects …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these graphs and …
Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang
Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang
Research Collection School Of Computing and Information Systems
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided …
Collaborative Topic Regression With Denoising Autoencoder For Content And Community Co-Representation, Trong T. Nguyen, Hady W. Lauw
Collaborative Topic Regression With Denoising Autoencoder For Content And Community Co-Representation, Trong T. Nguyen, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through similarity in social relations. We seek to integrate both types of information, in addition to the adoption information, within a single integrated model. Our proposed method models item content via a topic model, and user communities via an autoencoder model, while bridging a user's community-based preference to her topic-based preference. Experiments on public real-life data showcase the …
Answerbot: Automated Generation Of Answer Summary To Developers’ Technical Questions, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo
Answerbot: Automated Generation Of Answer Summary To Developers’ Technical Questions, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
The prevalence of questions and answers on domain-specific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details …
Apibot: Question Answering Bot For Api Documentation, Yuan Tian, Ferdian Thung, Abhishek Sharma, David Lo
Apibot: Question Answering Bot For Api Documentation, Yuan Tian, Ferdian Thung, Abhishek Sharma, David Lo
Research Collection School Of Computing and Information Systems
As the carrier of Application Programming Interfaces (APIs) knowledge, API documentation plays a crucial role in how developers learn and use an API. It is also a valuable information resource for answering API-related questions, especially when developers cannot find reliable answers to their questions online/offline. However, finding answers to API-related questions from API documentation might not be easy because one may have to manually go through multiple pages before reaching the relevant page, and then read and understand the information inside the relevant page to figure out the answers. To deal with this challenge, we develop APIBot, a bot that …
Modeling Check-In Behavior With Geographical Neighborhood Influence Of Venues, Thanh Nam Doan, Ee Peng Lim
Modeling Check-In Behavior With Geographical Neighborhood Influence Of Venues, Thanh Nam Doan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4,6,12,17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby …
Large Scale Kernel Methods For Online Auc Maximization, Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi
Large Scale Kernel Methods For Online Auc Maximization, Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datasets. To solve this issue, some kernel-based learning methods are proposed for non-linearly separable datasets. However, such kernel approaches have been shown to be inefficient and failed to scale well on large scale datasets in practice. Taking this cue, in this work, we explore the use of scalable kernel-based learning techniques as surrogates to existing approaches: …
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance givenlimited query budget. Secondly, it is also challenging to decide how to update the predictive models effectively whenever the true labelof an instance is queried. Most existing approaches for online active learning are often based on a family of first-order online …
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness …
Capsense: Capacitor-Based Activity Sensing For Kinetic Energy Harvesting Powered Wearable Devices, Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Capsense: Capacitor-Based Activity Sensing For Kinetic Energy Harvesting Powered Wearable Devices, Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion …
The Impact Of Coverage On Bug Density In A Large Industrial Software Project, Thomas Bach, Artur Andrzejak, Ralf Pannemans, David Lo
The Impact Of Coverage On Bug Density In A Large Industrial Software Project, Thomas Bach, Artur Andrzejak, Ralf Pannemans, David Lo
Research Collection School Of Computing and Information Systems
Measuring quality of test suites is one of the major challenges of software testing. Code coverage identifies tested and untested parts of code and is frequently used to approximate test suite quality. Multiple previous studies have investigated the relationship between coverage ratio and test suite quality, without a clear consent in the results. In this work we study whether covered code contains a smaller number of future bugs than uncovered code (assuming appropriate scaling). If this correlation holds and bug density is lower in covered code, coverage can be regarded as a meaningful metric to estimate the adequacy of testing. …
On Locating Malicious Code In Piggybacked Android Apps, Li Li, Daoyuan Li, Tegawende F. Bissyande, Jacques Klein, Haipeng Cai, David Lo, Yves Le Traon
On Locating Malicious Code In Piggybacked Android Apps, Li Li, Daoyuan Li, Tegawende F. Bissyande, Jacques Klein, Haipeng Cai, David Lo, Yves Le Traon
Research Collection School Of Computing and Information Systems
To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically …