Relevant bibliographies by topics / Mobile botnet

Contents

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

Academic literature on the topic 'Mobile botnet'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Mobile botnet"

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Wang, Yichuan, Yefei Zhang, Wenjiang Ji, Lei Zhu, and Yanxiao Liu. "Gleer: A Novel Gini-Based Energy Balancing Scheme for Mobile Botnet Retopology." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7805408.

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Mobile botnet has recently evolved due to the rapid growth of smartphone technologies. Unlike legacy botnets, mobile devices are characterized by limited power capacity, calculation capabilities, and wide communication methods. As such, the logical topology structure and communication mode have to be redesigned for mobile botnets to narrow energy gap and lower the reduction speed of nodes. In this paper, we try to design a novel Gini-based energy balancing scheme (Gleer) for the atomic network, which is a fundamental component of the heterogeneous multilayer mobile botnet. Firstly, for each operation cycle, we utilize the dynamic energy threshold to categorize atomic network into two groups. Then, the Gini coefficient is introduced to estimate botnet energy gap and to regulate the probability for each node to be picked as a region C&C server. Experimental results indicate that our proposed method can effectively prolong the botnet lifetime and prevent the reduction of network size. Meanwhile, the stealthiness of botnet with Gleer scheme is analyzed from users’ perspective, and results show that the proposed scheme works well in the reduction of user’ detection awareness.
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Karim, Ahmad, Victor Chang, and Ahmad Firdaus. "Android Botnets." Journal of Organizational and End User Computing 32, no. 3 (July 2020): 50–67. http://dx.doi.org/10.4018/joeuc.2020070105.

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Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually.
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Wu, Min-Hao, Chia-Hao Lee, Fu-Hau Hsu, Kai-Wei Chang, Tsung-Huang Huang, Ting-Cheng Chang, and Li-Min Yi. "Simple and Ingenious Mobile Botnet Covert Network Based on Adjustable Unit (SIMBAIDU)." Mathematical Problems in Engineering 2021 (August 3, 2021): 1–6. http://dx.doi.org/10.1155/2021/9920883.

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Various services through smartphones or personal computers have become common nowadays. Accordingly, embedded malware is rapidly increasing. The malware is infiltrated by using short message service (SMS), wireless networks, and random calling and makes smartphones bots in botnets. Therefore, in a system without an appropriate deterrent, smartphones are infiltrated easily. In the security threats by malware, random calling has become serious nowadays. To develop the defensive system against random calling and prevent the infiltration of the malware through random calling, it is required to understand the exact process of how to make bots in the botnet. Thus, this research develops a simple and ingenious mobile botnet covert network based on adjustable ID units (SIMBAIDU) to investigate how a botnet network is established by using phone numbers. Perfect octave coding (P8 coding) turns out to be effective in infiltrating smartphones and executing commands, which is used for botnets. The results provide the basic process of P8 coding which is useful for developing defensive systems of smartphones.
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Yerima, Suleiman Y., Mohammed K. Alzaylaee, Annette Shajan, and Vinod P. "Deep Learning Techniques for Android Botnet Detection." Electronics 10, no. 4 (February 23, 2021): 519. http://dx.doi.org/10.3390/electronics10040519.

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Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers.
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Li, Na, Yan Hui Du, and Guang Xun Chen. "CPbot: The Construction of Mobile Botnet Using GCM." Applied Mechanics and Materials 635-637 (September 2014): 1526–29. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1526.

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In order to detect hacker attacks and take early countermeasures, this paper introduced a novel mobile botnet called CPbot which uses Google Cloud Messaging (GCM) to spread Trojan on Android devices. First, we presented the network model of this GCM based mobile botnet as well as its command and control (C&C) mechanism. Secondly, we illustrated the different roles that this botnet can play. Finally, we setup a simulation model to discuss the topology of this mobile botnet. The MATLAB simulation result shows that CPbot is robust against single point of failures and has good resiliency to shutdown attempts, its command dissemination is highly efficient and the bot App has very low battery consumption. This analysis indicates that mobile botnet is a leading threat to mobile network security; therefore we must deploy defense strategies against this botnet.
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Sun, Fenggang, Lidong Zhai, Yuejin Du, Peng Wang, and Jun Li. "Design of Mobile Botnet Based on Open Service." International Journal of Digital Crime and Forensics 8, no. 3 (July 2016): 1–10. http://dx.doi.org/10.4018/ijdcf.2016070101.

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In recent years, botnet has become one of the most serious security threats to Internet. With the rapid development of mobile network and the popularity of smartphones, botnet began to spread to mobile platform. In order to counter mobile botnet, it is meaningful to study its constructive mechanism and reproduce it. In the past studies, researchers have designed several kinds of mobile botnet model based on various communication channels, such as SMS, Bluetooth, etc. This paper proposed a general mobile botnet model based on open service, and verified its feasibility by implementing it on Android platform. This paper also analyzed this model, and then proposed potential defense strategy in terms of its characteristic, which may provide reliable theoretical and technical support for future prevention and privacy protection.
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Yusof, Muhammad, Madihah Mohd Saudi, and Farida Ridzuan. "Mobile Botnet Classification by using Hybrid Analysis." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.15.21429.

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The popularity and adoption of Android smartphones has attracted malware authors to spread the malware to smartphone users. The malware on smartphone comes in various forms such as Trojans, viruses, worms and mobile botnet. However, mobile botnet or Android botnet are more dangerous since they pose serious threats by stealing user credential information, distributing spam and sending distributed denial of service (DDoS) attacks. Mobile botnet is defined as a collection of compromised mobile smartphones and controlled by a botmaster through a command and control (C&C) channel to serve a malicious purpose. Current research is still lacking in terms of their low detection rate due to their selected features. It is expected that a hybrid analysis could improve the detection rate. Therefore, machine learning methods and hybrid analysis which combines static and dynamic analyses were used to analyse and classify system calls, permission and API calls. The objective of this paper is to leverage machine learning techniques to classify the Android applications (apps) as botnet or benign. The experiment used malware dataset from the Drebin for the training and mobile applications from Google Play Store for testing. The results showed that Random Forest Algorithm achieved the highest accuracy rate of 97.9%. In future, more significant approach by using different feature selection such as intent, string and system component will be further explored for a better detection and accuracy rate.
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Lin, Kuan-Cheng, Sih-Yang Chen, and Jason C. Hung. "Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/986428.

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Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.
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Bernardeschi, Cinzia, Francesco Mercaldo, Vittoria Nardone, and Antonella Santone. "Exploiting Model Checking for Mobile Botnet Detection." Procedia Computer Science 159 (2019): 963–72. http://dx.doi.org/10.1016/j.procs.2019.09.263.

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Jiang, Ruei Min, Jia Sian Jhang, Fu Hau Hsu, Yan Ling Hwang, Pei Wen Huang, and Yung Hoh Sheu. "JokerBot – An Android-Based Botnet." Applied Mechanics and Materials 284-287 (January 2013): 3454–58. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3454.

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Due to the trend that mobile devices are getting more and more popular, smart phone security becomes an important issue nowadays. This paper proposes an Android-based botnet, called JokerBot, to show the possible security problems in mobile devices. This paper describes JokerBot framework. JokerBot designs its own communication mechanism to allow different bots to communicate with each other. An attacker can use JokerBot to trigger many kinds of potential attacks, such as monitoring the SMS messages and location disclosure. Moreover, after a bot is created in a compromised smartphone, it is difficult to locate the botmaster and detect whether the smartphone is infected or not. Finally, this paper proposes some defense mechanisms to protect a smartphone against JokerBot attacks.
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Dissertations / Theses on the topic "Mobile botnet"

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

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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
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Meng, Xim. "An integrated network-based mobile botnet detection system." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/19840/.

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The increase in the use of mobile devices has made them target for attackers, through the use of sophisticated malware. One of the most significant types of such malware is mobile botnets. Due to their continually evolving nature, botnets are difficult to tackle through signature and traditional anomaly based detection methods. Machine learning techniques have also been used for this purpose. However, the study of their effectiveness has shown methodological weaknesses that have prevented the emergence of conclusive and thorough evidence about their merit. To address this problem, in this thesis we propose a mobile botnet detection system, called MBotCS and report the outcomes of a comprehensive experimental study of mobile botnet detection using supervised machine learning techniques to analyse network traffic and system calls on Android mobile devices. The research covers a range of botnet detection scenarios that is wider from what explored so far, explores atomic and box learning algorithms, and investigates thoroughly the sensitivity of the algorithm performance on different factors (algorithms, features of network traffic, system call data aggregation periods, and botnets vs normal applications and so on). These experiments have been evaluated using real mobile device traffic, and system call captured from Android mobile devices, running normal apps and mobile botnets. The experiments study has several superiorities comparing with existing research. Firstly, experiments use not only atomic but also box ML classifiers. Secondly, a comprehensive set of Android mobile botnets, which had not been considered previously, without relying on any form of synthetic training data. Thirdly, experiments contain a wider set of detection scenarios including unknown botnets and normal applications. Finally, experiments include the statistical significance of differences in detection performance measures with respect to different factors. The study resulted in positive evidence about the effectiveness of the supervised learning approach, as a solution to the mobile botnet detection problem.
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Jensen, David. "Ddos Defense Against Botnets in the Mobile Cloud." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500027/.

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Mobile phone advancements and ubiquitous internet connectivity are resulting in ever expanding possibilities in the application of smart phones. Users of mobile phones are now capable of hosting server applications from their personal devices. Whether providing services individually or in an ad hoc network setting the devices are currently not configured for defending against distributed denial of service (DDoS) attacks. These attacks, often launched from a botnet, have existed in the space of personal computing for decades but recently have begun showing up on mobile devices. Research is done first into the required steps to develop a potential botnet on the Android platform. This includes testing for the amount of malicious traffic an Android phone would be capable of generating for a DDoS attack. On the other end of the spectrum is the need of mobile devices running networked applications to develop security against DDoS attacks. For this mobile, phones are setup, with web servers running Apache to simulate users running internet connected applications for either local ad hoc networks or serving to the internet. Testing is done for the viability of using commonly available modules developed for Apache and intended for servers as well as finding baseline capabilities of mobiles to handle higher traffic volumes. Given the unique challenge of the limited resources a mobile phone can dedicate to Apache when compared to a dedicated hosting server a new method was needed. A proposed defense algorithm is developed for mitigating DDoS attacks against the mobile server that takes into account the limited resources available on the mobile device. The algorithm is tested against TCP socket flooding for effectiveness and shown to perform better than the common Apache module installations on a mobile device.
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Liu, En-Bang, and 劉恩榜. "Mobile Botnet Detection on Android." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/72436850018843213186.

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碩士
國立交通大學
資訊科學與工程研究所
100
Botnets are now a serious threat to the internet . The infected computers will become a puppet (zombie computer), and controlled by attacker unconsciously . This impact not only resulted in leakage of information, system damage , but also make the computers become a springboard for a major network attacks .With the high development of smart phones , the phone is not just for calling or sending messages like before , also contains the ability of surfing the internet and basic processing data ; hence many personal data , passwords , private pictures/videos are stored in the phone. The smart phone has become a mini-PC. So in recent years , many hackers continue to develop viruses , Trojan Horses , bot virus and other malicious software on mobile phones to steal private information , send advertising messages and spam e-mails. Therefore in this paper , we provide a mobile Botnet detection system on Android. Based on the group activities model and abnormal connections metric , installing the Snort IDS to detect real time traffic and the Botnet packet filter to collect abnormal traffic in the frontend. Then upload the abnormal traffic to the detection center . After collecting traffic data from many mobile phones , the center uses similarity algorithms to determine which phone is infected with the bot virus and controlled by attacker.
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Pieterse, Heloise. "Design of a hybrid command and control mobile botnet." Diss., 2014. http://hdl.handle.net/2263/41816.

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Mobile devices have excelled in the 21st century due to the increasing popularity and continuous improvement of mobile technology. Today mobile devices have become all-in-one portable devices, providing inter-connectivity, device-to-device communication and the capability to compete with personal computers. The improved capabilities and popularity of mobile devices have, however, caught the attention of botnet developers, allowing the threat of botnets to move into the mobile environment. A mobile botnet is de fined as a collection of compromised mobile devices, controlled by a botmaster through a command and control (C&C) network to serve a malicious purpose. Previous studies of mobile botnet designs focused mostly on the C&C structure, investigating other mechanisms as potential C&C channels. None of these studies dealt with the use of a hybrid C&C structure within a mobile botnet design. This research consequently examines the problem of designing a new mobile botnet that uses a hybrid C&C structure. A model of this new hybrid design is proposed, describing the propagation vectors, C&C channels, and the topology. This hybrid design, called the Hybrid Mobile Botnet, explores the efficiency of multiple C&C channels against the following characteristics: no single point of failure must exist in the topology, low cost for command dissemination, limited network activities and low battery consumption per bot. The objectives were measured by using a prototype built according to the Hybrid Mobile Botnet model. The prototype was deployed on a small collection of mobile devices running the Android operating system. In addition, the prototype allowed for the design of a physical Bluetooth C&C channel, showing that such a channel is feasible, able to bypass security and capable of establishing a stealthy C&C channel. The successful execution of the prototype shows that a hybrid C&C structure is possible, allowing for a stealthy and cost-eff ective design. It also revels that current mobile technology is capable of supporting the development and execution of hybrid mobile botnets. Finally, this dissertation concludes with an exploration of the future of mobile botnets and the identifi cation of security steps users of mobile devices can follow to protect against their attacks.
Dissertation (MSc)--University of Pretoria, Pretoria 2014
Computer Science
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Kitana, Asem. "Impact of mobile botnet on long term evolution networks: a distributed denial of service attack perspective." Thesis, 2021. http://hdl.handle.net/1828/12817.

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In recent years, the advent of Long Term Evolution (LTE) technology as a prominent component of 4G networks and future 5G networks, has paved the way for fast and new mobile web access and application services. With these advantages come some security concerns in terms of attacks that can be launched on such networks. This thesis focuses on the impact of the mobile botnet on LTE networks by implementing a mobile botnet architecture that initiates a Distributed Denial of Service (DDoS) attack. First, in the quest of understanding the mobile botnet behavior, a correlation between the mobile botnet impact and different mobile device mobility models, is established, leading to the study of the impact of the random patterns versus the uniform patterns of movements on the mobile botnet’s behavior under a DDoS attack. Second, the impact of two base transceiver station selection mechanisms on a mobile botnet behavior launching a DDoS attack on a LTE network is studied, the goal being to derive the effect of the attack severity of the mobile botnet. Third, an epidemic SMS-based cellular botnet that uses an epidemic command and control mechanism to initiate a short message services (SMS) phishing attack, is proposed and its threat impact is studied and simulated using three random graphs models. The simulation results obtained reveal that (1) in terms of users’ mobility patterns, the impact of the mobile botnet behavior under a DDoS attack on a victim web server is more pronounced when an asymmetric mobility model is considered compared to a symmetric mobility model; (2) in terms of base transceiver station selection mechanisms, the Distance-Based Model mechanism yields a higher threat impact on the victim server compared to the Signal Power Based Model mechanism; and (3) under the Erdos-and-Reyni Topology, the proposed epidemic SMS-based cellular botnet is shown to be resistant and resilient to random and selective cellular device failures.
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Chia-Wei, Kao, and 高家緯. "An Effective Unknown Botnet Malware Detection Mechanism for Android-based Mobile Devices." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/11971069157005073291.

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碩士
大葉大學
資訊管理學系碩士班
99
At present, the smart phone system is developing vigorously, in which Android occupies most of the current market share, using the open operating system to provide overall effective applications (APPs) for the users to install. However, while it provides protection, it also brings harms just like a double-edged sword. Some malware may hide in the various Android APPs. This study mainly discusses one of the Android botnets, which abuses the powerful connection function of Android. Its distributed denial of service (DDoS)attacks have the features of the large-scaled botnet, plus the high mobility of the Android mobile device, so it will cause greater harm to the targets than the conventional DDoS attacks, and it is hard to track the attack source. This malware makes the Android connection slower, so that users cannot normally use the network service. What worse, the greater threat is that it blocks the operation of servers; as a result, the uninfected Android smart phones can’t normally access the network services. 　　Nowadays, most of the conventional DDoS detection mechanisms are in the server-end, which can only temporarily relieve the DDoS attacks to stabilize the normal service, but don’t provide effective solution to the Android botnet problems. Furthermore, the conventional detectors are not designed for mobile devices, so its design mechanism is not suitable for the mobile devices with low performance, limited powers and less storage space. Therefore, in order to design an effective detection mechanism to unknown botnet malware, this study first develops a kind of Android botnet malware based on the HTTP Flood attack, which is the most inundant DDoS attack and is hard to detect; meanwhile, it cannot be detected by the well-known anti-virus software tools. Afterward, we further develop a mechanism that cannot only effectively detect a wide variety of unknown botnet malware, but also detect the botnet malware developed in this study. The performance evaluation and analysis reveal the proposed detection mechanism indeed has high detection accuracy, and is superior to the related studies in terms of performance requirements and practical applications. Thus, we affirm the proposed detection mechanism has extremely high practical application value.
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LEE, CHIA-HAO, and 李家豪. "CIDP Treatment: An Innovative Mobile Botnet Covert Channel based on Caller IDs with P8 Treatment." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/17943920410701052238.

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碩士
國立中央大學
資訊工程研究所
99
Nowadays we use a variety of applications on mobile phones or personal computers, and the probability of malware embedding is growing high. If there is not any robust prevention in the future, botnet will penetrate, and then manipulate the user’s mobile phones or computers and seize the authority of control. Mobile phones brought us much convenience, but also the safety of the use on it has been received more attention. In real world, because of the difference of application scenarios, the security mechanism on a personal computer in the past, although some may be directly applied, most likely seems to be no avail in smart phones, for the purpose of use as well as on different architecture. Smart phones (broadly speaking, mobile smart devices) in modern society play an important role. With the applications on the network, smart phones bring the convenience, but also led to many related security issues. This paper presents a possible way, CIDP Treatment, to achieve the control of a mobile botnet by using caller ID numbers as an innovative covert channel. We design an innovative lossless data compression treatment -- Perfect Octave Coding (P8 Coding) for this new covert channel to enhance the efficiency of the data transmission.
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Book chapters on the topic "Mobile botnet"

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Karim, Ahmad, Syed Adeel Ali Shah, and Rosli Salleh. "Mobile Botnet Attacks: A Thematic Taxonomy." In Advances in Intelligent Systems and Computing, 153–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05948-8_15.

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Vural, Ickin, and Hein Venter. "Mobile Botnet Detection Using Network Forensics." In Future Internet - FIS 2010, 57–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15877-3_7.

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Bhatia, J. S., R. K. Sehgal, and Sanjeev Kumar. "Honeynet Based Botnet Detection Using Command Signatures." In Advances in Wireless, Mobile Networks and Applications, 69–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21153-9_7.

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Porras, Phillip, Hassen Saïdi, and Vinod Yegneswaran. "An Analysis of the iKee.B iPhone Botnet." In Security and Privacy in Mobile Information and Communication Systems, 141–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17502-2_12.

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Šimon, Marek, Ladislav Huraj, and Marián Hosťovecký. "A Mobile Botnet Model Based on P2P Grid." In Communications in Computer and Information Science, 604–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65551-2_44.

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Hua, Jingyu, and Kouichi Sakurai. "A SMS-Based Mobile Botnet Using Flooding Algorithm." In Information Security Theory and Practice. Security and Privacy of Mobile Devices in Wireless Communication, 264–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21040-2_19.

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Dong, Yulong, Jun Dai, and Xiaoyan Sun. "A Mobile Botnet That Meets Up at Twitter." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 3–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01704-0_1.

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Li, Na, Yanhui Du, and Guangxuan Chen. "Mobile Botnet Propagation Modeling in Wi-Fi Networks." In Proceedings of the 4th International Conference on Computer Engineering and Networks, 1147–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_132.

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Wang, Peng, Chengwei Zhang, Xuanya Li, and Can Zhang. "A Mobile Botnet Model Based on Android System." In Trustworthy Computing and Services, 54–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43908-1_7.

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Li, Yue, Lidong Zhai, Zhilei Wang, and Yunlong Ren. "Control Method of Twitter- and SMS-Based Mobile Botnet." In Trustworthy Computing and Services, 644–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35795-4_81.

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Conference papers on the topic "Mobile botnet"

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Choi, Byungha, Sung-Kyo Choi, and Kyungsan Cho. "Detection of Mobile Botnet Using VPN." In 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2013. http://dx.doi.org/10.1109/imis.2013.32.

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Alzahrani, Abdullah J., and Ali A. Ghorbani. "SMS-Based Mobile Botnet Detection Module." In 2016 6th International Conference on IT Convergence and Security (ICITCS). IEEE, 2016. http://dx.doi.org/10.1109/icitcs.2016.7740371.

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Abdullah, Zubaile, Madihah Mohd Saudi, and Nor Badrul Anuar. "Mobile botnet detection: Proof of concept." In 2014 IEEE 5th Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2014. http://dx.doi.org/10.1109/icsgrc.2014.6908733.

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Anwar, Shahid, Jasni Mohamad Zain, Zakira Inayat, Riaz Ul Haq, Ahmad Karim, and Aws Naser Jabir. "A static approach towards mobile botnet detection." In 2016 3rd International Conference on Electronic Design (ICED). IEEE, 2016. http://dx.doi.org/10.1109/iced.2016.7804708.

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Guining Geng, Guoai Xu, Miao Zhang, Yixian Yang, and Guang Yang. "An improved SMS based heterogeneous mobile botnet model." In 2011 International Conference on Information and Automation (ICIA). IEEE, 2011. http://dx.doi.org/10.1109/icinfa.2011.5948987.

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Liu, Tao, and Kai Zhu. "The Research of Control Mechanism in Mobile Botnet." In 2015 3rd International Conference on Machinery, Materials and Information Technology Applications. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icmmita-15.2015.279.

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Liao, Ming-Yi, Jynu-Hao Li, Chu-Sing Yang, Min Chen, Chun-Wei Tsai, and Ming-Cho Chang. "Botnet Topology Reconstruction: A Case Study." In 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2012. http://dx.doi.org/10.1109/imis.2012.114.

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8

Cai, Tao, and Futai Zou. "Detecting HTTP Botnet with Clustering Network Traffic." In 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2012. http://dx.doi.org/10.1109/wicom.2012.6478491.

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9

Alzahrani, Abdullah J., and Ali A. Ghorbani. "SMS mobile botnet detection using a multi-agent system." In the 1st International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2602945.2602950.

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10

Zhang, YeFei, Yi Chuan, Wang LeiWang, XinHong Hei, and Guo Xie. "Fairness-power consumption re-topology strategies for mobile botnet." In 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA). IEEE, 2017. http://dx.doi.org/10.1109/iceaa.2017.8065370.

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