Relevant bibliographies by topics / Malware family

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Malware family'

Author: Grafiati
Published: 28 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Malware family"

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Yan, Jinpei, Yong Qi, and Qifan Rao. "Detecting Malware with an Ensemble Method Based on Deep Neural Network." Security and Communication Networks 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/7247095.

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Malware detection plays a crucial role in computer security. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset.
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Jiao, Jian, Qiyuan Liu, Xin Chen, and Hongsheng Cao. "Behavior Intention Derivation of Android Malware Using Ontology Inference." Journal of Electrical and Computer Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9250297.

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Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.
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Prima, B., and M. Bouhorma. "USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 343–49. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-343-2020.

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Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.
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Jang, Jae-wook, and Huy Kang Kim. "Function-Oriented Mobile Malware Analysis as First Aid." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6707524.

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Recently, highly well-crafted mobile malware has arisen as mobile devices manage highly valuable and sensitive information. Currently, it is impossible to detect and prevent all malware because the amount of new malware continues to increase exponentially; malware detection methods need to improve in order to respond quickly and effectively to malware. For the quick response, revealing the main purpose or functions of captured malware is important; however, only few recent works have attempted to find malware’s main purpose. Our approach is designed to help with efficient and effective incident responses or countermeasure development by analyzing the main functions of malicious behavior. In this paper, we propose a novel method for function-oriented malware analysis approach based on analysis of suspicious API call patterns. Instead of extracting API call patterns for malware in each family, we focus on extracting such patterns for certain malicious functionalities. Our proposed method dumps memory sections where an application is allocated and extracts suspicious API sequences from bytecode by comparing with predefined suspicious API lists. By matching API call patterns with our functionality database, our method determines whether they are malicious. The experiment results demonstrate that our method performs well in detecting malware with high accuracy.
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Wang, Changguang, Ziqiu Zhao, Fangwei Wang, and Qingru Li. "A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet." Security and Communication Networks 2021 (January 5, 2021): 1–16. http://dx.doi.org/10.1155/2021/6658842.

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With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families.
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Abuthawabeh, Mohammad, and Khaled Mahmoud. "Enhanced Android Malware Detection and Family Classification, using Conversation-level Network Traffic Features." International Arab Journal of Information Technology 17, no. 4A (July 31, 2020): 607–14. http://dx.doi.org/10.34028/iajit/17/4a/4.

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Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised-based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14‬% for precision and recall, respectively
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Cheng, Binlin, Jinjun Liu, Jiejie Chen, Shudong Shi, Xufu Peng, Xingwen Zhang, and Haiqing Hai. "MoG: Behavior-Obfuscation Resistance Malware Detection." Computer Journal 62, no. 12 (June 4, 2019): 1734–47. http://dx.doi.org/10.1093/comjnl/bxz033.

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Abstract Malware brings a big security threat on the Internet today. With the great increasing malware attacks. Behavior-based detection approaches are one of the major method to detect zero-day malware. Such approaches often use API calls to represent the behavior of malware. Unfortunately, behavior-based approaches suffer from behavior obfuscation attacks. In this paper, we propose a novel malware detection approach that is both effective and efficient. First, we abstract the API call to object operation. And then we generate the object operation dependency graph based on these object operations. Finally, we construct the family dependency graph for a malware family. Our approach use family dependency graph to represent the behavior of malware family. The evaluation results show that our approach can provide a complete resistance to all types of behavior obfuscation attacks, and outperforms existing behavior-based approaches in terms of better effectiveness and efficiency.
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Shao, Ke, Qiang Xiong, and Zhiming Cai. "FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection." Security and Communication Networks 2021 (June 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/6642252.

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As the number of Android malware applications continues to grow at a high rate, detecting malware to protect the system security and user privacy is becoming increasingly urgent. Each malware application belongs to a specific family, and there is a gap in the number of malware families. The accuracy of detection can be improved if malware family information is well utilized and certain strategies are adopted to balance the variability among samples. In addition, the performance of a base classifier is limited. If an ensemble classifier or an ensemble method can be adopted, the detection effect can be further improved. Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection. First, five features are extracted from the Android application package. Then, the relief feature selection algorithm is used for feature selection. Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset. Combined with the two sampling strategies, the traditional bagging algorithm is improved to integrate the classifier. In the experiment, several classifiers were used to evaluate the proposed scheme. The experimental results show that the proposed sampling strategy and the improved bagging algorithm can effectively improve the detection accuracy of these classifiers.
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Alswaina, Fahad, and Khaled Elleithy. "Android Malware Family Classification and Analysis: Current Status and Future Directions." Electronics 9, no. 6 (June 5, 2020): 942. http://dx.doi.org/10.3390/electronics9060942.

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Android receives major attention from security practitioners and researchers due to the influx number of malicious applications. For the past twelve years, Android malicious applications have been grouped into families. In the research community, detecting new malware families is a challenge. As we investigate, most of the literature reviews focus on surveying malware detection. Characterizing the malware families can improve the detection process and understand the malware patterns. For this reason, we conduct a comprehensive survey on the state-of-the-art Android malware familial detection, identification, and categorization techniques. We categorize the literature based on three dimensions: type of analysis, features, and methodologies and techniques. Furthermore, we report the datasets that are commonly used. Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.
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Cheng, Binlin, Qiang Tong, Jianhong Wang, and Wenhui Tian. "Malware Clustering Using Family Dependency Graph." IEEE Access 7 (2019): 72267–72. http://dx.doi.org/10.1109/access.2019.2914031.

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Dissertations / Theses on the topic "Malware family"

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Varga, Adam. "Identifikace a charakterizace škodlivého chování v grafech chování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442388.

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Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.
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Liu, Chi-Feng, and 劉其峰. "Malware Family Characterization." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4m43xu.

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碩士
國立政治大學
資訊管理學系
106
Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attracts hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, we adopt a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset.
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Kuo, Wen-Han, and 郭文翰. "Artificial Intelligence Technology for Malware Family Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3q8ee3.

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碩士
國立臺灣科技大學
電機工程系
107
The rapid development of Internet of Things (IoT) devices and communication technologies have greatly expanded the application of the internet. In response to people’s pursuit of high quality of life, the number of IoT devices and related services have increased annually. However, the importance of information security has been overlooked by majority of people, promoting hackers and those with ulterior motives to use malware to attack security holes in Internet applications. With the number of attack incidents increasing, detection system of malware has become imperative. This study proposed an integrative system framework that combines machine learning, deep learning, data balancing, and feature evaluation mechanism to detect malware, and a family-based approach was used to present classification results. The proposed framework can serve as a reference for antivirus companies and related service providers to develop adequate strategies for defending against malware attacks. This study acquired data from the CTU-13 open dataset, which was compiled through capturing the traffic from the network of a university. The dataset includes normal, malware, and background traffic. In order to reduce the noise in the dataset and improve the overall model efficiency, this study performed data analysis using feature evaluation methods including ANOVA, Chi-Square and AutoEncoder. Features that reduce the model accuracy were removed to reduce the model computation time and improve model stability. Because imbalanced data existed among various classes of malware and benign software in the original dataset, a data balancing mechanism was introduced to resolve this problem. The SMOTEENN algorithm was used to generate data for minority classes, thereby alleviating model deviations and enhancing the overall model credibility. This study also considered that malware receives updates and grows in number over time. Therefore, the neural networks architecture adopted in this study employ an activation function mechanism to detect malware. When an unknown malware program be found that does not belong to any family derived from the previous neural networks architecture, this mechanism incorporates the program in the model training for the subsequent model update. Analysis on the efficiency of the proposed framework revealed that the detection models with XGBoost and Back Propagation reached an accuracy rate of 99.98% and 98.88%, respectively.
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Zhou, Jun-Da, and 周俊達. "DMFF: Detection Malware by its Family Features." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/53626941465322630802.

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碩士
國立交通大學
電機工程學系
104
The population of mobile users grows rapidly and people get used to storing information on a mobile device, hence the possibility under attack raises. Among the mobile attacks, malware is the most common attack and cause large damage for mobile users. For example, A victim may suffer from the information leakage or money lost causing by Short Message Service (SMS) attacks. To improve the security of a mobile device, experts have proposed many methods for malware detection. The website, Datasets, defines four malware families to simplify the detection of malware. In this thesis, we design DMFF (Detecting Malware by its Family Features) to provide an automation tool for categorizing them. DMFF comprises four stages, \textit{Extracting Stage}, \textit{Training Stage}, \textit{Testing Stage} and \textit{Update \& Retraining}. \textit{Extracting Stage} extracts Permission and Service from an application configuration file. \textit{Training Stage} applies matrix computation to generate system training model \textbf{k} for each malware family. The value \textbf{k} is used to detect a malware in DMFF to indicate its malicious behavior. The result then are forwarded to update the system model. To evaluate DMFF, four experiments with 179 malware and 200 normal samples involving are designed to test the accuracy on applying only Permission, only Service and the combination of both Permission and Service. The last experiment tests the accuracy on distinguishing benign application from malware. With 97.42\% accuracy on distinguishing benign application from malware and 82\% accuracy on categorizing malwares, DMFF is proved its ability to detect a malware and categorize the malware by its behavior.
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Chiang, Li-Yuan, and 姜立垣. "Malware Family Motif API Sequence Analysis on Windows Platform." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/71906851778430095106.

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碩士
國立臺灣大學
資訊管理學研究所
104
This thesis aims to focus on malware on Windows platform, extracting common characteristic behaviors in a malware family, identifying differentiated characteristic behavior among malware family variants. First, we define a malware process execution to be a Windows API call sequence and winnow parameters in these sequences. Then, in order to compare these sequences, we apply sequence alignment techniques to align similar parts in execution sequences, insert gaps or align mismatch parts in different parts. Thus, we develop a system for multiple sequence alignment based on Needleman-Wunsch algorithm. This system produces a data structure, stageMatrix, to describe all segment alignment information among a family variants. Next, we extract common execution stages. We define APIs that may cause system state changes (StateChange_API, SC_API) and track the resources these APIs access and visualize the full access flow. At last, we plan to extend characteristic comparison to multiple families in future work.
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Chao, Wei-Chieh, and 趙偉傑. "Base on RFpS of Ensemble learning in Malware Family Classification." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4pavv7.

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碩士
淡江大學
資訊管理學系碩士在職專班
105
As we know some fundamental issues of data mining applications are much more critical and severe once it refers to malware analysis, and unfortunately, they are still not well-addressed. In this paper, the proposed a function, as well as uses supervised feature projection for redundant feature reduction and noise filtering. Combining Random Forest with SVM for named RFPS (Random Forest Predicated Svm), Method of reducing feature and fast classification. The results that the learning time about 4.5 times compared with the SVM , predicted speed increases by about 2.5 times ,and the accuracy is about 20% to 98.4%.
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Chiu, Wei-Jhih, and 邱偉志. "Automated Malware Family Signature Generation based on Runtime API Call Sequence." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/etw684.

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碩士
國立臺灣大學
資訊管理學研究所
106
Recent years, the threats from malware are increasing in the world. It is important if we analyze the malwares and extract their signatures. The malware threat detection and defense will benefit from that.This research collected the malware family labels from anti-virus vendors and analyzed the behavior intents of malware family. We designed a API Call Sequence-based clustering algorithm – RasMMA, which could extract the common signature of a group of malwares. If we input some malware profiles, RasMMA algorithm could cluster the malware samples and output the common behavior of each cluster. The cluster common behavior is semantic-based which human experts could analyze the intent that malwares done. We could see the common behavior as the signature of malware family. Besides, we also found that malware family is pluralistic. The behavior clusters might different to each other in one family. Even though some clusters are cross-family clusters which behavior is similar to other families’ behavior.In the research, we also apply the behavior cluster to family sample detection. We found that our method had a better performance than other traditional data mining method in the time series malware data classification.
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Chen, Ting-Yi, and 陳廷易. "Malware Family Classification System based on Attention-based Characteristic Execution Sequence." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6jj2mm.

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碩士
國立臺灣大學
資訊管理學研究所
107
In recent years, the number of malicious software (malware) has increased rapidly, which has caused a lot of losses for individuals and businesses around the world. Understanding the intention of malware and extracting key execution behaviors will considerably help detect and defend against malware. This research proposes an automated important execution sequence behavior identification system. The recurrent neural network and self-attention mechanism are used as the basis of the architecture. It is used to analyze Windows API call invocations sequence recording at runtime, and capture the relationship between API call invocations. To automatically identify malware whether each API call invocation is a characteristic API call in malicious behavioral activity, and can respond to its malicious intentions. The proposed system contains three functional modules, namely Embedder which vectorizes API call invocations, Encoder which calculates the importance of each API call invocation in the execution profiles, and Filter which extracts important API call invocations from the malware. Through these three modules, we can establish a pipeline for malware analysis and family classification. The important API call invocations of the system output allow the security analysts to quickly know the semantic interpretation of the characteristic execution pattern and classify or cluster malware by calculating the similarity score. Compared with other methods our experiments not only prove the effectiveness of the proposed functional modules in our system but also demonstrate the system''s behavioral feature recognition ability, which can classify unseen malware correctly into their family. Additionally, we visualize the important API call invocations of the malware and analyze the relationship between different behavioral patterns and family characteristic execution patterns. We found that the malware family is pluralistic, and the same behavioral patterns can exist in many different families.
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Hsueh, Chu-Yun, and 薛筑允. "Automated Generation and Semantic Analysis of System-state-change Activity Lifecycle of Malware Family." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5676wn.

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碩士
國立臺灣大學
資訊管理學研究所
106
In this work, we aim to visualize the common behavior of malware family that cause system state changes. First of all, we conduct a malware classification based on proposed family classification algorithm. Secondly, we use the high-level semantics profiling system to profile different variants of malware family, generating the time-ordered sequences of each variant, called execution traces. Then, in order to differentiate behavior diversity between different variants in same malware family, we input execution trace of each variant to Runtime API call sequence-based motif mining algorithm to conduct behavior sequence clustering, producing behavior forest of a malware family. For each behavior tree in behavior forest, we collect execution trace belong to behavior tree and input to Global Sequence Alignment module to gather longest alignment result. For each behavior tree in behavior forest, we input all execution traces belong to the behavior tree to Global Sequence Alignment module to acquire longest alignment combination. Finally, we obtain the 100% common behavior sequence from GSA result, then extract sequence that will causing system state change from 100% common behavior sequence, visualize the behavior using trajectory graph, called system-state-change resource manipulation trajectory We also make semantic explanation toward produced trajectory graph, expound malicious intent of malware family, provide in-depth and clear malicious activity illustration, and verify behavior of malware family with illustration of antivirus software company.
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Book chapters on the topic "Malware family"

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Basole, Samanvitha, and Mark Stamp. "Cluster Analysis of Malware Family Relationships." In Malware Analysis Using Artificial Intelligence and Deep Learning, 361–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62582-5_14.

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Aman, Naqqash, Yasir Saleem, Fahim H. Abbasi, and Farrukh Shahzad. "A Hybrid Approach for Malware Family Classification." In Applications and Techniques in Information Security, 169–80. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5421-1_14.

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Islam, Rafiqul, and Irfan Altas. "A Comparative Study of Malware Family Classification." In Information and Communications Security, 488–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34129-8_48.

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Shrestha, Prasha, Suraj Maharjan, Gabriela Ramírez de la Rosa, Alan Sprague, Thamar Solorio, and Gary Warner. "Using String Information for Malware Family Identification." In Advances in Artificial Intelligence -- IBERAMIA 2014, 686–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12027-0_55.

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Choudhary, Chhaya, Raaghavi Sivaguru, Mayana Pereira, Bin Yu, Anderson C. Nascimento, and Martine De Cock. "Algorithmically Generated Domain Detection and Malware Family Classification." In Communications in Computer and Information Science, 640–55. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5826-5_50.

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Gayathri, T., and M. S. Vijaya. "Malware Family Classification Model Using Convolutional Neural Network." In Advances in Intelligent Systems and Computing, 27–35. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0171-2_3.

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Davis, Shreya, C. N. Sminesh, K. S. Akshay, T. R. Akshay, and Anjali Ranjith. "An Evaluation of Convolutional Neural Networks for Malware Family Classification." In Communications in Computer and Information Science, 51–60. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9700-8_5.

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Chen, Yihang, Fudong Liu, Zheng Shan, and Guanghui Liang. "MalCommunity: A Graph-Based Evaluation Model for Malware Family Clustering." In Communications in Computer and Information Science, 279–97. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2203-7_21.

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Sun, Yeali S., Chien-Chun Chen, Shun-Wen Hsiao, and Meng Chang Chen. "ANTSdroid: Automatic Malware Family Behaviour Generation and Analysis for Android Apps." In Information Security and Privacy, 796–804. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93638-3_48.

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Gayathri, T., and M. S. Vijaya. "Malware Family Classification Model Using User Defined Features and Representation Learning." In IFIP Advances in Information and Communication Technology, 185–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63467-4_14.

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Conference papers on the topic "Malware family"

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Kumar, Nitish, and Toshanlal Meenpal. "Texture-Based Malware Family Classification." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944659.

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Hsiao, Shun-Wen, Yeali S. Sun, and Meng Chang Chen. "Behavior grouping of Android malware family." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511424.

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Pitolli, Gregorio, Leonardo Aniello, Giuseppe Laurenza, Leonardo Querzoni, and Roberto Baldoni. "Malware family identification with BIRCH clustering." In 2017 International Carnahan Conference on Security Technology (ICCST). IEEE, 2017. http://dx.doi.org/10.1109/ccst.2017.8167802.

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Turker, Sercan, and Ahmet Burak Can. "AndMFC: Android Malware Family Classification Framework." In 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops). IEEE, 2019. http://dx.doi.org/10.1109/pimrcw.2019.8880840.

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Walker, Aaron, and Shamik Sengupta. "Malware Family Fingerprinting Through Behavioral Analysis." In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2020. http://dx.doi.org/10.1109/isi49825.2020.9280529.

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Xie, Qi, Yongjun Wang, and Zhiquan Qin. "Malware Family Classification using LSTM with Attention." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263499.

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Nomura, Kazuya, Daiki Chiba, Mitsuaki Akiyama, and Masato Uchida. "Auto-creation of Android Malware Family Tree." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500876.

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Chang, Shun-Chieh, Yeali S. Sun, Wu-Long Chuang, Meng-Chang Chen, Bo Sun, and Takeshi Takahashi. "ANTSdroid: Using RasMMA Algorithm to Generate Malware Behavior Characteristics of Android Malware Family." In 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2018. http://dx.doi.org/10.1109/prdc.2018.00047.

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Boukhtouta, Amine, Nour-Eddine Lakhdari, and Mourad Debbabi. "Inferring Malware Family through Application Protocol Sequences Signature." In 2014 6th International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2014. http://dx.doi.org/10.1109/ntms.2014.6814026.

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Chen, Chin-Wei, Ching-Hung Su, Kun-Wei Lee, and Ping-Hao Bair. "Malware Family Classification using Active Learning by Learning." In 2020 22nd International Conference on Advanced Communication Technology (ICACT). IEEE, 2020. http://dx.doi.org/10.23919/icact48636.2020.9061419.

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