Relevant bibliographies by topics / Mobile malware detection

Contents

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

Academic literature on the topic 'Mobile malware detection'

Author: Grafiati
Published: 19 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Mobile malware detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Mobile malware detection"

1

Yildiz, Oktay, and Ibrahim Alper Doğru. "Permission-based Android Malware Detection System Using Feature Selection with Genetic Algorithm." International Journal of Software Engineering and Knowledge Engineering 29, no. 02 (February 2019): 245–62. http://dx.doi.org/10.1142/s0218194019500116.

Full text
Abstract:
As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing Android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.
APA, Harvard, Vancouver, ISO, and other styles
2

Swetha, K., and K. V.D.Kiran. "Survey on Mobile Malware Analysis and Detection." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 279. http://dx.doi.org/10.14419/ijet.v7i2.32.15584.

Full text
Abstract:
The amazing advances of mobile phones enable their wide utilize. Since mobiles are joined with pariah applications, bundles of security and insurance issues are incited. But, current mobile malware analysis and detection advances are as yet flawed, incapable, and incomprehensive. On account of particular qualities of mobiles such as constrained assets, user action and neighborhood correspondence ability, consistent system network, versatile malware detection faces new difficulties, particularly on remarkable runtime malware area. This paper provides overview on malware classification, methodologies of assessment, analysis and on and off device detection methods on android. The work mainly focuses on different classification algorithms which are used as a part of dynamic malware detection on android.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
4

M., Duraipandian, and Vinothkanna R. "MACHINE LEARNING BASED AUTOMATIC PERMISSION GRANTING AND MALWARE IDENTIFICATION." December 2019 01, no. 02 (December 23, 2019): 96–107. http://dx.doi.org/10.36548/jitdw.2019.2.005.

Full text
Abstract:
The mobile device have gained an imperative predominance in the daily routine of our lives, by keeping us connected to the real world seamlessly. Most of the mobile devices are built on android whose security mechanism is totally permission based controlling the applications from accessing the core details of the devices and the users. Even after understanding the permission system often the mobile user are ignorant about the common threat, due to the applications popularity and proceed with the installation process not aware of the targets of the application developer. The aim of the paper is to devise malware detection with the automatic permission granting employing the machine learning techniques. The different machine learning methods are engaged in the malware detection and analyzed. The results are observed to note down the approaches that aids better in enhancing the user awareness and reducing the malware threats, by detecting the malwares of the applications.
APA, Harvard, Vancouver, ISO, and other styles
5

Bibi, Iram, Adnan Akhunzada, Jahanzaib Malik, Muhammad Khurram Khan, and Muhammad Dawood. "Secure Distributed Mobile Volunteer Computing with Android." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–21. http://dx.doi.org/10.1145/3428151.

Full text
Abstract:
Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.
APA, Harvard, Vancouver, ISO, and other styles
6

Du, Yao, Mengtian Cui, and Xiaochun Cheng. "A Mobile Malware Detection Method Based on Malicious Subgraphs Mining." Security and Communication Networks 2021 (April 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/5593178.

Full text
Abstract:
As mobile phone is widely used in social network communication, it attracts numerous malicious attacks, which seriously threaten users’ personal privacy and data security. To improve the resilience to attack technologies, structural information analysis has been widely applied in mobile malware detection. However, the rapid improvement of mobile applications has brought an impressive growth of their internal structure in scale and attack technologies. It makes the timely analysis of structural information and malicious feature generation a heavy burden. In this paper, we propose a new Android malware identification approach based on malicious subgraph mining to improve the detection performance of large-scale graph structure analysis. Firstly, function call graphs (FCGs), sensitive permissions, and application programming interfaces (APIs) are generated from the decompiled files of malware. Secondly, two kinds of malicious subgraphs are generated from malware’s decompiled files and put into the feature set. At last, test applications’ safety can be automatically identified and classified into malware families by matching their FCGs with malicious structural features. To evaluate our approach, a dataset of 11,520 malware and benign applications is established. Experimental results indicate that our approach has better performance than three previous works and Androguard.
APA, Harvard, Vancouver, ISO, and other styles
7

He, Gaofeng, Bingfeng Xu, Lu Zhang, and Haiting Zhu. "On-Device Detection of Repackaged Android Malware via Traffic Clustering." Security and Communication Networks 2020 (May 31, 2020): 1–19. http://dx.doi.org/10.1155/2020/8630748.

Full text
Abstract:
Malware has become a significant problem on the Android platform. To defend against Android malware, researchers have proposed several on-device detection methods. Typically, these on-device detection methods are composed of two steps: (i) extracting the apps’ behavior features from the mobile devices and (ii) sending the extracted features to remote servers (such as a cloud platform) for analysis. By monitoring the behaviors of the apps that are running on mobile devices, available methods can detect suspicious applications (simply, apps) accurately. However, mobile devices are typically resource limited. The feature extraction and massive data transmission might consume substantial power and CPU resources; thus, the performance of mobile devices will be degraded. To address this issue, we propose a novel method for detecting Android malware by clustering apps’ traffic at the edge computing nodes. First, a new integrated architecture of the cloud, edge, and mobile devices for Android malware detection is presented. Then, for repackaged Android malware, the network traffic content and statistics are extracted at the edge as detection features. Finally, in the cloud, similarities between apps are calculated, and the similarity values are automatically clustered to separate the original apps and the malware. The experimental results demonstrate that the proposed method can detect repackaged Android malware with high precision and with a minimal impact on the performance of mobile devices.
APA, Harvard, Vancouver, ISO, and other styles
8

Mazaed Alotaibi, Fahad, and Fawad. "A Multifaceted Deep Generative Adversarial Networks Model for Mobile Malware Detection." Applied Sciences 12, no. 19 (September 20, 2022): 9403. http://dx.doi.org/10.3390/app12199403.

Full text
Abstract:
Malware’s structural transformation to withstand the detection frameworks encourages hackers to steal the public’s confidential content. Researchers are developing a protective shield against the intrusion of malicious malware in mobile devices. The deep learning-based android malware detection frameworks have ensured public safety; however, their dependency on diverse training samples has constrained their utilization. The handcrafted malware detection mechanisms have achieved remarkable performance, but their computational overheads are a major hurdle in their utilization. In this work, Multifaceted Deep Generative Adversarial Networks Model (MDGAN) has been developed to detect malware in mobile devices. The hybrid GoogleNet and LSTM features of the grayscale and API sequence have been processed in a pixel-by-pixel pattern through conditional GAN for the robust representation of APK files. The generator produces syntactic malicious features for differentiation in the discriminator network. Experimental validation on the combined AndroZoo and Drebin database has shown 96.2% classification accuracy and a 94.7% F-score, which remain superior to the recently reported frameworks.
APA, Harvard, Vancouver, ISO, and other styles
9

Rahul Y. Pawar, Mr, and Dr C.Mahesh. "A Survey on Malware Detection Techniques on Linux Powered Smart Phones using Machine Learning Approaches." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 8. http://dx.doi.org/10.14419/ijet.v7i3.34.18706.

Full text
Abstract:
Mobile Phone manufacturers are continuously working to take move on with rapid pace on their new models and to match with the need of customer, they need to customize their system. However the security scenarios of such practice are not that known, due to this various malware and viruses are increasing day by day and causing harm to the devices. Due to the substantial damage caused by malware in last few years certain significant efforts on developing detection and defense mechanism against malwares. For detecting such malicious applications and malwares a security system should be developed which will target such anomaly or outliers in system. In data mining anomaly detection system plays a major role by monitoring the behavior of an application and categorizing them in to normal and abnormal to detect malwares present in the system.
APA, Harvard, Vancouver, ISO, and other styles
10

Guo, Dai Fei, Jian Jun Hu, Ai Fen Sui, Guan Zhou Lin, and Tao Guo. "The Abnormal Mobile Malware Analysis Based on Behavior Categorization." Advanced Materials Research 765-767 (September 2013): 994–97. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.994.

Full text
Abstract:
With the explosive growth of mobile malware in mobile internet, many polymorphic and metamorphic mobile malware appears and causes difficulty of detection. A mobile malware network behavior data mining method based on behavior categorization is proposed to detect the behavior of new or metamorphic mobile malware. The network behavior is divided into different categories after analyzing the behavior character of mobile malware and those different behavior data of known malware and normal action are used to train the Naïve Bayesian classifier respectively. Those Naïve Bayesian classifiers are used to detect the mobile malware network behavior. The experiment result shows that Behavior Categorization based Naïve Bayesian Classifier (BCNBC) can improve the detection accuracy and it can meet the requirement of real time process in mobile internet.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Mobile malware detection"

1

Khoda, Mahbub. "Robust Mobile Malware Detection." Thesis, Federation University Australi, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176412.

Full text
Abstract:
The increasing popularity and use of smartphones and hand-held devices have made them the most popular target for malware attackers. Researchers have proposed machine learning-based models to automatically detect malware attacks on these devices. Since these models learn application behaviors solely from the extracted features, choosing an appropriate and meaningful feature set is one of the most crucial steps for designing an effective mobile malware detection system. There are four categories of features for mobile applications. Previous works have taken arbitrary combinations of these categories to design models, resulting in sub-optimal performance. This thesis systematically investigates the individual impact of these feature categories on mobile malware detection systems. Feature categories that complement each other are investigated and categories that add redundancy to the feature space (thereby degrading the performance) are analyzed. In the process, the combination of feature categories that provides the best detection results is identified. Ensuring reliability and robustness of the above-mentioned malware detection systems is of utmost importance as newer techniques to break down such systems continue to surface. Adversarial attack is one such evasive attack that can bypass a detection system by carefully morphing a malicious sample even though the sample was originally correctly identified by the same system. Self-crafted adversarial samples can be used to retrain a model to defend against such attacks. However, randomly using too many such samples, as is currently done in the literature, can further degrade detection performance. This work proposed two intelligent approaches to retrain a classifier through the intelligent selection of adversarial samples. The first approach adopts a distance-based scheme where the samples are chosen based on their distance from malware and benign cluster centers while the second selects the samples based on a probability measure derived from a kernel-based learning method. The second method achieved a 6% improvement in terms of accuracy. To ensure practical deployment of malware detection systems, it is necessary to keep the real-world data characteristics in mind. For example, the benign applications deployed in the market greatly outnumber malware applications. However, most studies have assumed a balanced data distribution. Also, techniques to handle imbalanced data in other domains cannot be applied directly to mobile malware detection since they generate synthetic samples with broken functionality, making them invalid. In this regard, this thesis introduces a novel synthetic over-sampling technique that ensures valid sample generation. This technique is subsequently combined with a dynamic cost function in the learning scheme that automatically adjusts minority class weight during model training which counters the bias towards the majority class and stabilizes the model. This hybrid method provided a 9% improvement in terms of F1-score. Aiming to design a robust malware detection system, this thesis extensively studies machine learning-based mobile malware detection in terms of best feature category combination, resilience against evasive attacks, and practical deployment of detection models. Given the increasing technological advancements in mobile and hand-held devices, this study will be very useful for designing robust cybersecurity systems to ensure safe usage of these devices.
Doctor of Philosophy
APA, Harvard, Vancouver, ISO, and other styles
2

Kinable, Joris. "Malware Detection Through Call Graphs." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10908.

Full text
Abstract:
Each day, anti-virus companies receive large quantities of potentially harmful executables. Many of the malicious samples among these executables are variations of earlier encountered malware, created by their authors to evade pattern-based detection. Consequently, robust detection approaches are required, capable of recognizing similar samples automatically.In this thesis, malware detection through call graphs is studied. In a call graph, the functions of a binary executable are represented as vertices, and the calls between those functions as edges. By representing malware samples as call graphs, it is possible to derive and detect structural similarities between multiple samples. The latter can be used to implement generic malware detection schemes, which can proactively detect existing versions of the malware, as well as future releases with similar characteristics.To compare call graphs mutually, we compute pairwise graph similarity scores via graphmatchings which minimize an objective function known as the Graph Edit Distance. Finding exact graph matchings is intractable for large call graph instances. Hence we investigate several efficient approximation algorithms. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including variations on k-medoids clustering and DBSCAN clustering algorithms. Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by virus analysts from F-Secure Corporation. Experiments show that it is indeed possible to accurately detect malware families using the DBSCAN clustering algorithm. Based on our results, we anticipate that in the future it is possible to use call graphs to analyse the emergence of new malware families, and ultimately to automate implementinggeneric protection schemes for malware families.
APA, Harvard, Vancouver, ISO, and other styles
3

Al, Sebea Hussain. "Dynamic detection and immunisation of malware using mobile agents." Thesis, Edinburgh Napier University, 2005. http://researchrepository.napier.ac.uk/output/4036/.

Full text
Abstract:
At present, malicious software (mal-ware) is causing many problems on private networks and the Internet. One major cause of this includes outdated or absent security software to countermeasure these anomalies such as Antivirus software and Personal Firewalls. Another cause is that mal-ware can exploit weaknesses in software, notably operating systems. This can be reduced by use of a patch service, which automatically downloads patches to its clients. Unfortunately this can lead to new problems introduced by the patch server itself. The aim of this project is to produce a more flexible approach in which agent programs are dispatched to clients (which in turn run static agent programs), allowing them to communicate locally rather than over the network. Thus, this project uses mobile agents which are software agents which can be given an itinerary and migrate to different hosts, interrogating the static agents therein for any suspicious files. These mobile agents are deployed with a list of known mal-ware signatures and their corresponding cures, which are used as a reference to determine whether a reported suspect is indeed malicious. The overall system is responsible for Dynamic Detection and Immunisation of Mal-ware using Mobile Agents (DIMA) on peer to peer (P2P) systems. DIMA is be categorised under Intrusion Detection Systems (IDS) and deals with the specific branch of malicious software discovery and removal. DIMA was designed using Borland Delphi to implement the static agent due to its seamless integration with the Windows operating system, whereas the mobile agent was implemented in Java, running on the Grasshopper mobile agent environment, due to its compliance with several mobile agent development standards and in-depth documentation. In order to evaluate the characteristics of the DIMA system a number of experiments were carried out. This included measuring the total migration time and host hardware specification and its effect on trip timings. Also, as the mobile agent migrated, its size was measured between hops to see how this varied as more data was collected from hosts. The main results of this project show that the time the mobile agent took to visit all predetermined hosts increased linearly as the number of hosts grew (the average inter-hop interval was approximately 1 second). It was also noted that modifications to hardware specifications in a group of hosts had minimal effect on the total journey time for the mobile agent. Increasing a group of host's processor speeds or RAM capacity made a subtle difference to round trip timings (less than 300 milliseconds faster than a slower group of hosts). Finally, it was proven that as the agent made more hops, it increased in size due to the accumulation of statistical data collected (57 bytes after the first hop, and then a constant increase of 4 bytes per hop thereafter).
APA, Harvard, Vancouver, ISO, and other styles
4

Burguera, Hidalgo Iker. "Behavior-based malware detection system for the Android platform." Thesis, Linköpings universitet, RTSLAB - Laboratoriet för realtidssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-73647.

Full text
Abstract:
Malware in smartphones is growing at a significant rate. There are currently more than 250 million smartphone users in the world and this number is expected to grow in coming years.  In the past few years, smartphones have evolved from simple mobile phones into sophisticated computers. This evolution has enabled smartphone users to access and browse the Internet, to receive and send emails, SMS and MMS messages and to connect devices in order to exchange information. All of these features make the smartphone a useful tool in our daily lives, but at the same time they render it more vulnerable to attacks by malicious applications.  Given that most users store sensitive information on their mobile phones, such as phone numbers, SMS messages, emails, pictures and videos, smartphones are a very appealing target for attackers and malware developers. The need to maintain security and data confidentiality on the Android platform makes the analysis of malware on this platform an urgent issue.  We have based this report on previous approaches to the dynamic analysis of application behavior, and have adapted one approach in order to detect malware on the Android platform. The detector is embedded in a framework to collect traces from a number of real users and is based on crowdsourcing. Our framework has been tested by analyzing data collected at the central server using two types of data sets: data from artificial malware created for test purposes and data from real malware found in the wild. The method used is shown to be an effective means of isolating malware and alerting users of downloaded malware, which suggests that it has great potential for helping to stop the spread of detected malware to a larger community.  This thesis project shows that it is feasible to create an Android malware detection system with satisfactory results.
APA, Harvard, Vancouver, ISO, and other styles
5

Gitzinger, Louison. "Surviving the massive proliferation of mobile malware." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S058.

Full text
Abstract:
De nos jours, nous sommes entourés de périphériques intelligents autonomes qui interagissent avec de nombreux services dans le but d'améliorer notre niveau de vie. Ces périphériques font partie d'écosystèmes plus larges, dans lesquels de nombreuses entreprises collaborent pour faciliter la distribution d'applications entre les développeurs et les utilisateurs. Cependant, des personnes malveillantes en profitent illégalement pour infecter les appareils des utilisateurs avec des application malveillantes. Malgré tous les efforts mis en œuvre pour défendre ces écosystèmes, le taux de périphériques infectés par des malware est toujours en augmentation en 2020.Dans cette thèse, nous explorons trois axes de recherche dans le but d'améliorer globalement la détection de malwares dans l'écosystème Android. Nous démontrons d'abord que la précision des systèmes de détection basés sur le machine learning peuvent être améliorés en automatisant leur évaluation et en ré-utilisant le concept d'AutoML pour affiner les paramètres des algorithmes d'apprentissage. Nous proposons une approche pour créer automatiquement des variantes de malwares à partir de combinaisons de techniques d'évasion complexes pour diversifier les datasets de malwares expérimentaux dans le but de mettre à l'épreuve les systèmes de détection. Enfin, nous proposons des méthodes pour améliorer la qualité des datasets expérimentaux utilisés pour entrainer et tester les systèmes de détection
Nowadays, many of us are surrounded by smart devices that seamlessly operate interactively and autonomously together with multiple services to make our lives more comfortable. These smart devices are part of larger ecosystems, in which various companies collaborate to ease the distribution of applications between developers and users. However malicious attackers take advantage of them illegitimately to infect users' smart devices with malicious applications. Despite all the efforts made to defend these ecosystems, the rate of devices infected with malware is still increasing in 2020. In this thesis, we explore three research axes with the aim of globally improving malware detection in the Android ecosystem. We demonstrate that the accuracy of machine learning-based detection systems can be improved by automating their evaluation and by reusing the concept of AutoML to fine-tune learning algorithms parameters. We propose an approach to automatically create malware variants from combinations of complex evasion techniques to diversify experimental malware datasets in order to challenge existing detection systems. Finally, we propose methods to globally increase the quality of experimental datasets used to train and test detection systems
APA, Harvard, Vancouver, ISO, and other styles
6

Adeel, Muhammad. "Adaptive mobile P2P malware detection using social interactions based digital footprints." Thesis, Queen Mary, University of London, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612575.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.

Full text
Abstract:
This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dissertation critically discusses the advantages of having using the three tier model to combat spamming Botnets.
Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
unrestricted
APA, Harvard, Vancouver, ISO, and other styles
8

Arp, Daniel Christopher Verfasser], Konrad [Akademischer Betreuer] [Rieck, and Lorenzo [Akademischer Betreuer] Cavallaro. "Efficient and Explainable Detection of Mobile Malware with Machine Learning / Daniel Christopher Arp ; Konrad Rieck, Lorenzo Cavallaro." Braunschweig : Technische Universität Braunschweig, 2019. http://d-nb.info/1195705018/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Arp, Daniel Christopher [Verfasser], Konrad [Akademischer Betreuer] Rieck, and Lorenzo [Akademischer Betreuer] Cavallaro. "Efficient and Explainable Detection of Mobile Malware with Machine Learning / Daniel Christopher Arp ; Konrad Rieck, Lorenzo Cavallaro." Braunschweig : Technische Universität Braunschweig, 2019. http://d-nb.info/1195705018/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Irolla, Paul. "Formalization of Neural Network Applications to Secure 3D Mobile Applications." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS585/document.

Full text
Abstract:
Ce travail de thèse fait partie du projet 3D NeuroSecure. C'est un projet d'investissement d'avenir, qui vise à développer une solution de collaboration sécurisée pour l'innovation thérapeutique appliquant les traitements de haute performance (HPC) au monde biomédical. Cette solution donnera la possibilité pour les experts de différents domaines de naviguer intuitivement dans l'imagerie Big Data avec un accès via des terminaux mobile. La protection des données contre les fuites de données est primordiale. En tant que tel, l'environnement client et les communications avec le serveur doivent être sécurisé. Nous avons concentré notre travail sur le développement d'une solution antivirale sur le système d'exploitation Android. Nous avons promu la création de nouveaux algorithmes, méthodes et outils qui apportent des avantages par rapport à état de l'art, mais plus important encore, qui peuvent être utilisés efficacement dans un contexte de production. C'est pourquoi, ce qui est proposé ici est souvent un compromis entre ce qui peut théoriquement être fait et son applicabilité. Les choix algorithmiques et technologiques sont motivés par une relation entre efficacité et performance. Cette thèse contribue à l'état de l'art dans les domaines suivants:Analyse statique et dynamique d'applications Android, web crawling d'application.Tout d'abord, pour rechercher des fonctions malveillantes et des vulnérabilités, il faut concevoir les outils qui extraient des informations pertinentes des applications Android. C'est la base de toute analyse. En outre, tout algorithme de classification est toujours limité par la qualité discriminative des données sous-jacentes. Une partie importante de cette thèse est la la conception d'outils d'analyse statique et dynamique efficientes, telles qu'un module de reverse engineering, un outil d'analyse de communication, un système Android instrumenté.Algorithme d'initialisation, d'apprentissage et d'anti-saturation pour réseau de neurones.Les réseaux de neurones sont initialisés au hasard. Il est possible de contrôler la distribution aléatoire sous-jacente afin de réduire l'effet de saturation, le temps de l'entrainement et la capacité à atteindre le minimum global. Nous avons développé une procédure d’initialisation qui améliore les résultats par rapport à l'état del'art. Nous avons aussi adapté l'algorithme ADAM pour prendre en compte les interdépendances avec des techniques de régularisation, en particulier le Dropout. Enfin, nous utilisons techniques d'anti-saturation et nous montrons qu'elles sont nécessaires pour entraîner correctement un réseau neuronal.Un algorithme pour représenter les sous-séquences communes à un groupe de séquences.Nous proposons un nouvel algorithme pour construire l'AntichaineEnglobante des sous-séquences communes. Il est capable de traiter et de représenter toutes les sous-séquences d'un ensemble de séquences. C'estun outil qui permet de caractériser de manière systématique un groupe de séquence. Cet algorithme est une nouvelle voie de recherche verscréation automatique de règles de détection de famille de virus
This thesis work is part of the 3D NeuroSecure project. It is an investment project, that aims to develop a secure collaborative solution for therapeutic innovation using high performance processing(HPC) technology to the biomedical world. This solution will give the opportunity for experts from different fields to navigate intuitivelyin the Big Data imaging with access via 3D light terminals. Biomedicaldata protection against data leaks is of foremost importance. As such,the client environnement and communications with the server must besecured. We focused our work on the development of antimalware solutionon the Android OS. We emphasizes the creation of new algorithms,methods and tools that carry advantages over the current state-of-the-art, but more importantly that can be used effectively ina production context. It is why, what is proposed here is often acompromise between what theoretically can be done and its applicability. Algorithmic and technological choices are motivated by arelation of efficiency and performance results. This thesis contributes to the state of the art in the following areas:Static and dynamic analysis of Android applications, application web crawling.First, to search for malicious activities and vulnerabilities, oneneeds to design the tools that extract pertinent information from Android applications. It is the basis of any analysis. Furthermore,any classifier or detector is always limited by the informative power of underlying data. An important part of this thesis is the designing of efficient static and dynamic analysis tools forapplications, such as an reverse engineering module, a networkcommunication analysis tool, an instrumented Android system, an application web crawlers etc.Neural Network initialization, training and anti-saturation techniques algorithm.Neural Networks are randomly initialized. It is possible to control the underlying random distribution in order to the reduce the saturation effect, the training time and the capacity to reach theglobal minimum. We developed an initialization procedure that enhances the results compared to the state-of-the-art. We also revisited ADAM algorithm to take into account interdependencies with regularization techniques, in particular Dropout. Last, we use anti-saturation techniques and we show that they are required tocorrectly train a neural network.An algorithm for collecting the common sequences in a sequence group.We propose a new algorithm for building the Embedding Antichain fromthe set of common subsequences. It is able to process and represent allcommon subsequences of a sequence set. It is a tool for solving the Systematic Characterization of Sequence Groups. This algorithm is a newpath of research toward the automatic creation of malware familydetection rules
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Mobile malware detection"

1

Surendran, Roopak, Teenu S. John, Tony Thomas, and Mamoun Alazab. Intelligent Mobile Malware Detection. Taylor & Francis Group, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Surendran, Roopak, Teenu S. John, Tony Thomas, and Mamoun Alazab. Intelligent Mobile Malware Detection. Taylor & Francis Group, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Surendran, Roopak, Teenu S. John, Tony Thomas, and Mamoun Alazab. Intelligent Mobile Malware Detection. Taylor & Francis Group, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Mobile malware detection"

1

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Android Malware." In Intelligent Mobile Malware Detection, 13–22. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Static Malware Detection." In Intelligent Mobile Malware Detection, 23–42. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "System Call Pattern-Based Detection." In Intelligent Mobile Malware Detection, 103–14. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Graph Convolutional Network for Detection." In Intelligent Mobile Malware Detection, 79–90. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Internet and Android OS." In Intelligent Mobile Malware Detection, 1–12. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Graph Signal Processing-Based Detection." In Intelligent Mobile Malware Detection, 91–102. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Dynamic and Hybrid Malware Detection." In Intelligent Mobile Malware Detection, 43–68. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Conclusions and Future Directions." In Intelligent Mobile Malware Detection, 115–18. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Thomas, Tony, Roopak Surendran, Teenu S. John, and Mamoun Alazab. "Detection Using Graph Centrality Measures." In Intelligent Mobile Malware Detection, 69–78. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003121510-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Martin, George, Dona Spencer, Aditya Hair, Deepa K, Sonia Laudanna, Vinod P, and Corrado Aaron Visaggio. "Mobile Malware Detection Using Consortium Blockchain." In Advances in Information Security, 137–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97087-1_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Mobile malware detection"

1

Shabtai, Asaf. "Malware Detection on Mobile Devices." In 2010 Eleventh International Conference on Mobile Data Management. IEEE, 2010. http://dx.doi.org/10.1109/mdm.2010.28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yoon, Seungyong, Jeongnyeo Kim, and Hyunsook Cho. "Detection of SMS mobile malware." In 2014 International Conference on Electronics, Information and Communications (ICEIC). IEEE, 2014. http://dx.doi.org/10.1109/elinfocom.2014.6914392.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shahpasand, Maryam, Len Hamey, Dinusha Vatsalan, and Minhui Xue. "Adversarial Attacks on Mobile Malware Detection." In 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile (AI4Mobile). IEEE, 2019. http://dx.doi.org/10.1109/ai4mobile.2019.8672711.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chen, Sen, Minhui Xue, and Lihua Xu. "Towards adversarial detection of mobile malware." In MobiCom'16: The 22nd Annual International Conference on Mobile Computing and Networking. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2973750.2985246.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jin, Xiang, Xiaofei Xing, Haroon Elahi, Guojun Wang, and Hai Jiang. "A Malware Detection Approach Using Malware Images and Autoencoders." In 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2020. http://dx.doi.org/10.1109/mass50613.2020.00009.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Liu, Peishun, and Xuefang Wang. "Inductive Learning in Malware Detection." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2921.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Skovoroda, Anastasia, and Dennis Gamayunov. "Review of the Mobile Malware Detection Approaches." In 2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2015. http://dx.doi.org/10.1109/pdp.2015.54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dai, Shuaifu, Yaxin Liu, Tielei Wang, Tao Wei, and Wei Zou. "Behavior-Based Malware Detection on Mobile Phone." In 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5601291.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Khatri, Vikramajeet, and Joerg Abendroth. "Mobile Guard Demo: Network Based Malware Detection." In 2015 IEEE Trustcom/BigDataSE/ISPA. IEEE, 2015. http://dx.doi.org/10.1109/trustcom.2015.501.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bulut, Irfan, and A. Gokhan Yavuz. "Mobile malware detection using deep neural network." In 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE, 2017. http://dx.doi.org/10.1109/siu.2017.7960568.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Mobile malware detection' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography