Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

[1]  A. Alamer.  A privacy-preserving federated learning with a secure collaborative for malware detection models using Internet of Things resources , 2024, Internet Things.

[2]  Wanlei Zhou,et al.  Supplement Data in Federated Learning with a Generator Transparent to Clients , 2024, Information Sciences.

[3]  Liang Zhang,et al.  A novel Android malware detection method with API semantics extraction , 2024, Comput. Secur..

[4]  Noor Afiza Mohd Ariffin,et al.  Android Malware Detection using Permission Based Static Analysis , 2023, Journal of Advanced Research in Applied Sciences and Engineering Technology.

[5]  Halit Bakır,et al.  DroidEncoder: Malware detection using auto-encoder based feature extractor and machine learning algorithms , 2023, Comput. Electr. Eng..

[6]  Ekta Gandotra,et al.  MalDetect: A classifier fusion approach for detection of android malware , 2023, Expert Syst. Appl..

[7]  Heitor Murilo Gomes,et al.  SeGDroid: An Android malware detection method based on sensitive function call graph learning , 2023, Expert Syst. Appl..

[8]  Zakaria Abou El Houda,et al.  MiTFed: A Privacy Preserving Collaborative Network Attack Mitigation Framework Based on Federated Learning Using SDN and Blockchain , 2023, IEEE Transactions on Network Science and Engineering.

[9]  A. Merlo,et al.  Android, Notify Me When It Is Time To Go Phishing , 2023, 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P).

[10]  O. Alfandi,et al.  DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection , 2023, Applied Sciences.

[11]  Zixuan Li,et al.  Universal backdoor attack on deep neural networks for malware detection , 2023, Appl. Soft Comput..

[12]  Kamlesh Dutta,et al.  A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learning , 2023, Comput. Secur..

[13]  Yuzhen Liu,et al.  BFG: privacy protection framework for internet of medical things based on blockchain and federated learning , 2023, Connect. Sci..

[14]  Artemis C. Voulkidis,et al.  GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems , 2023, Electronics.

[15]  Caio C. Moreira,et al.  Improving ransomware detection based on portable executable header using xception convolutional neural network , 2023, Comput. Secur..

[16]  A. Masood,et al.  RealMalSol: real-time optimized model for Android malware detection using efficient neural networks and model quantization , 2023, Neural Computing and Applications.

[17]  T. Pham,et al.  A multi-view feature fusion approach for effective malware classification using Deep Learning , 2023, J. Inf. Secur. Appl..

[18]  Cam Nguyen Tan,et al.  Android Malware Category and Family Classification Using Static Analysis , 2023, 2023 International Conference on Information Networking (ICOIN).

[19]  B. Pradhan,et al.  A Dynamic Weighted Federated Learning for Android Malware Classification , 2022, ArXiv.

[20]  Ammar Yahya Daeef,et al.  Features Engineering for Malware Family Classification Based API Call , 2022, Comput..

[21]  Sudipan Saha,et al.  TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications , 2022, Applied Sciences.

[22]  Agnius Liutkevičius,et al.  Framing Network Flow for Anomaly Detection Using Image Recognition and Federated Learning , 2022, Electronics.

[23]  Hui Zhu,et al.  Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges , 2022, Security and Communication Networks.

[24]  Mouhammd Alkasassbeh,et al.  Android Spyware Detection Using Machine Learning: A Novel Dataset , 2022, Sensors.

[25]  Gang Li.  Research on Smartphone Trojan Detection Based on the Wireless Sensor Network , 2022, Security and Communication Networks.

[26]  Chunhe Xia,et al.  FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification , 2022, Entropy.

[27]  Şerif Bahtiyar,et al.  Data poisoning attacks against machine learning algorithms , 2022, Expert Syst. Appl..

[28]  P. Nand,et al.  A Review of Android and iOS Operating System Security , 2022, 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS).

[29]  Esra Calik Bayazit,et al.  A Deep Learning Based Android Malware Detection System with Static Analysis , 2022, 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA).

[30]  Xiapu Luo,et al.  MalRadar , 2022, SIGMETRICS.

[31]  Weizhe Zhang,et al.  You are what the permissions told me! Android malware detection based on hybrid tactics , 2022, J. Inf. Secur. Appl..

[32]  F. Roli,et al.  Machine Learning Security Against Data Poisoning: Are We There Yet? , 2022, Computer.

[33]  A. W. Wahab,et al.  Permissions-Based Detection of Android Malware Using Machine Learning , 2022, Symmetry.

[34]  M. Alani,et al.  AdStop: Efficient flow-based mobile adware detection using machine learning , 2022, Comput. Secur..

[35]  Ning Li,et al.  A novel deep framework for dynamic malware detection based on API sequence intrinsic features , 2022, Comput. Secur..

[36]  Tuo Zhang,et al.  Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities , 2022, IEEE Internet of Things Magazine.

[37]  Y. Challal,et al.  Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges , 2022, Revue d'Intelligence Artificielle.

[38]  Wonsuk Kim,et al.  Privacy-preserving collaborative machine learning in biomedical applications , 2022, 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).

[39]  Gang Sun,et al.  PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks , 2022, IEEE Internet of Things Journal.

[40]  S. Yerima,et al.  A Novel Android Botnet Detection System Using Image-Based and Manifest File Features , 2022, Electronics.

[41]  Anit Kumar Sahu,et al.  Federated Learning Challenges and Opportunities: An Outlook , 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[42]  Shaker El-Sappagh,et al.  Robust deep learning early alarm prediction model based on the behavioural smell for android malware , 2022, Comput. Secur..

[43]  S. Bagui,et al.  Machine Learning for Android Scareware Detection , 2022, J. Inf. Technol. Res..

[44]  Shengshan Hu,et al.  Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning , 2021, 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom).

[45]  Chen Zhang,et al.  A survey on federated learning in data mining , 2021, WIREs Data Mining Knowl. Discov..

[46]  D. Rawat,et al.  Mitigating Poisoning Attack in Federated Learning , 2021, 2021 IEEE Symposium Series on Computational Intelligence (SSCI).

[47]  Frank Breitinger,et al.  Android application forensics: A survey of obfuscation, obfuscation detection and deobfuscation techniques and their impact on investigations , 2021, Digit. Investig..

[48]  Kai Rannenberg,et al.  Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing , 2021, CSCS.

[49]  Iman M. Almomani,et al.  Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems , 2021, Applied Sciences.

[50]  Jaiteg Singh,et al.  Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware , 2021, Security and Communication Networks.

[51]  Dima Alhadidi,et al.  Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder , 2021, Journal of Network and Systems Management.

[52]  Sven Nomm,et al.  KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization , 2021, Comput. Secur..

[53]  Pin-Yu Chen,et al.  CAFE: Catastrophic Data Leakage in Vertical Federated Learning , 2021, ArXiv.

[54]  Gregory Epiphaniou,et al.  Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things , 2021, Comput. Secur..

[55]  Ahmad Firdaus,et al.  A Bayesian probability model for Android malware detection , 2021, ICT Express.

[56]  Muhammad Tariq,et al.  Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges , 2021, Comput. Secur..

[57]  Ali Hamzeh,et al.  Ransomware Detection Based on PE Header Using Convolutional Neural Networks , 2021, ISC Int. J. Inf. Secur..

[58]  Muhammad Ikram,et al.  Analyzing security issues of android mobile health and medical applications , 2021, J. Am. Medical Informatics Assoc..

[59]  Gourav Garg,et al.  RPNDroid: Android Malware Detection using Ranked Permissions and Network Traffic , 2021, 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4).

[60]  Muhammad Ejaz Ahmed,et al.  Backdoor Attack on Machine Learning Based Android Malware Detectors , 2021, IEEE Transactions on Dependable and Secure Computing.

[61]  Yijin Chen,et al.  A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles , 2021, IEEE Transactions on Intelligent Transportation Systems.

[62]  Sikha Bagui,et al.  Android Adware Detection Using Machine Learning , 2021, International Journal of Cyber Research and Education.

[63]  Mohammad Zulkernine,et al.  RansomCare: Data-centric detection and mitigation against smartphone crypto-ransomware , 2021, Comput. Networks.

[64]  Niyati Baliyan,et al.  Comparative analysis of Android and iOS from security viewpoint , 2021, Comput. Sci. Rev..

[65]  Hossam Faris,et al.  Android botnet detection using machine learning models based on a comprehensive static analysis approach , 2021, J. Inf. Secur. Appl..

[66]  Pedro Miguel S'anchez S'anchez,et al.  Federated Learning for Malware Detection in IoT Devices , 2021, Comput. Networks.

[67]  Aslam Muhammad,et al.  CEEP-FL: A comprehensive approach for communication efficiency and enhanced privacy in federated learning , 2021, Appl. Soft Comput..

[68]  Zeyi Tao,et al.  A survey of federated learning for edge computing: Research problems and solutions , 2021, High-Confidence Computing.

[69]  Suleiman Y. Yerima,et al.  Deep Learning Techniques for Android Botnet Detection , 2021, Electronics.

[70]  Kashif Ahmad,et al.  Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge , 2021, IEEE Open Journal of the Computer Society.

[71]  Danda B. Rawat,et al.  Mitigating Data Poisoning Attacks On a Federated Learning-Edge Computing Network , 2021, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC).

[72]  Pawel Weichbroth,et al.  Mobile Security: Threats and Best Practices , 2020, Mob. Inf. Syst..

[73]  Raden Budiarto Hadiprakoso,et al.  Hybrid-Based Malware Analysis for Effective and Efficiency Android Malware Detection , 2020, 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS).

[74]  Li Li,et al.  A review of applications in federated learning , 2020, Comput. Ind. Eng..

[75]  Daojing He,et al.  Security Analysis of Cryptocurrency Wallets in Android-Based Applications , 2020, IEEE Network.

[76]  Diyana Tehrany Dehkordy,et al.  DroidTKM: Detection of Trojan Families using the KNN Classifier Based on Manhattan Distance Metric , 2020, 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE).

[77]  Sabu Emmanuel,et al.  A TAN based hybrid model for android malware detection , 2020, J. Inf. Secur. Appl..

[78]  Yanhui Du,et al.  Android Malware Detection Based on a Hybrid Deep Learning Model , 2020, Secur. Commun. Networks.

[79]  Takeshi Takahashi,et al.  A Privacy-Preserving Federated Learning System for Android Malware Detection Based on Edge Computing , 2020, 2020 15th Asia Joint Conference on Information Security (AsiaJCIS).

[80]  Dima Alhadidi,et al.  Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning , 2020, 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

[81]  Mehmet Emre Gursoy,et al.  Data Poisoning Attacks Against Federated Learning Systems , 2020, ESORICS.

[82]  Xiao Zhang,et al.  Android malware detection method based on bytecode image , 2020, Journal of Ambient Intelligence and Humanized Computing.

[83]  Iman M. Almomani,et al.  Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection , 2020, Applied Sciences.

[84]  Marco Evangelisti,et al.  A modular framework for mobile security analysis , 2020, Inf. Secur. J. A Glob. Perspect..

[85]  Mingfu Xue,et al.  Machine Learning Security: Threats, Countermeasures, and Evaluations , 2020, IEEE Access.

[86]  V. S. Subrahmanian,et al.  A Data-driven Characterization of Modern Android Spyware , 2020, ACM Trans. Manag. Inf. Syst..

[87]  Huda M. Salih,et al.  Spyware Injection in Android using Fake Application , 2020, 2020 International Conference on Computer Science and Software Engineering (CSASE).

[88]  Jemal H. Abawajy,et al.  An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches , 2020, Appl. Soft Comput..

[89]  Weixin Xie,et al.  Private Machine Learning Classification Based on Fully Homomorphic Encryption , 2020, IEEE Transactions on Emerging Topics in Computing.

[90]  Micah J. Sheller,et al.  The future of digital health with federated learning , 2020, npj Digital Medicine.

[91]  Ahmed A. Hashim,et al.  Android Botnet Detection Using Machine Learning , 2020, Ingénierie des Systèmes d Inf..

[92]  Yan Cai,et al.  Resource Race Attacks on Android , 2020, 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[93]  John K. Alhassan,et al.  Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis , 2019, 2019 15th International Conference on Electronics, Computer and Computation (ICECCO).

[94]  Sina Hojjatinia,et al.  Android Botnet Detection using Convolutional Neural Networks , 2019, 2020 28th Iranian Conference on Electrical Engineering (ICEE).

[95]  Peter B. Walker,et al.  Federated Learning for Healthcare Informatics , 2019, Journal of Healthcare Informatics Research.

[96]  Robert Nikolai Reith,et al.  Efficiently Stealing your Machine Learning Models , 2019, WPES@CCS.

[97]  Arash Habibi Lashkari,et al.  Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls , 2019, 2019 International Carnahan Conference on Security Technology (ICCST).

[98]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[99]  Iman Almomani,et al.  Ransomware Detection System for Android Applications , 2019, Electronics.

[100]  C. D. Jaidhar,et al.  Experimental analysis of Android malware detection based on combinations of permissions and API-calls , 2019, Journal of Computer Virology and Hacking Techniques.

[101]  Sandeep HR,et al.  Static Analysis of Android Malware Detection using Deep Learning , 2019, 2019 International Conference on Intelligent Computing and Control Systems (ICCS).

[102]  Naima Kaabouch,et al.  Social Engineering Attacks: A Survey , 2019, Future Internet.

[103]  Z. Li,et al.  Automatic fine-grained access control in SCADA by machine learning , 2019, Future Gener. Comput. Syst..

[104]  Zubaile Abdullah,et al.  Android Trojan Detection Based on Dynamic Analysis , 2019 .

[105]  Eul Gyu Im,et al.  A Multimodal Deep Learning Method for Android Malware Detection Using Various Features , 2019, IEEE Transactions on Information Forensics and Security.

[106]  Hongwei Li,et al.  Secure Multi-Party Computation: Theory, practice and applications , 2019, Inf. Sci..

[107]  Mark Stamp,et al.  An analysis of Android adware , 2018, Journal of Computer Virology and Hacking Techniques.

[108]  Prateek Mittal,et al.  Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.

[109]  Ali A. Ghorbani,et al.  Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification , 2018, 2018 International Carnahan Conference on Security Technology (ICCST).

[110]  Hajime Shimada,et al.  Detecting Privacy Information Abuse by Android Apps from API Call Logs , 2018, IWSEC.

[111]  Yidong Li,et al.  DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features , 2018, IEEE Access.

[112]  He Wang,et al.  Real-time Detection of Passive Backdoor Behaviors on Android System , 2018, 2018 IEEE Conference on Communications and Network Security (CNS).

[113]  Hyunwoo Choi,et al.  Large-Scale Analysis of Remote Code Injection Attacks in Android Apps , 2018, Secur. Commun. Networks.

[114]  Mohammad Al-Rubaie,et al.  Privacy-Preserving Machine Learning: Threats and Solutions , 2018, IEEE Security & Privacy.

[115]  Robert H. Deng,et al.  SCLib: A Practical and Lightweight Defense against Component Hijacking in Android Applications , 2018, CODASPY.

[116]  Hongnian Yu,et al.  SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System , 2018, IEEE Access.

[117]  Heiko Ludwig,et al.  Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach , 2017, AISec@CCS.

[118]  Iman Almomani,et al.  Abusing Android permissions: A security perspective , 2017, 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[119]  Ali A. Ghorbani,et al.  DNA-Droid: A Real-Time Android Ransomware Detection Framework , 2017, NSS.

[120]  Chuah Chai Wen,et al.  Android Malware Classification Using K-Means Clustering Algorithm , 2017 .

[121]  Ali Dehghantanha,et al.  Machine learning aided Android malware classification , 2017, Comput. Electr. Eng..

[122]  Ping Yan,et al.  A survey on dynamic mobile malware detection , 2017, Software Quality Journal.

[123]  Mansour Ahmadi,et al.  DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware , 2017, CODASPY.

[124]  Ali Feizollah,et al.  AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection , 2017, Comput. Secur..

[125]  Arvind Mahindru,et al.  Dynamic Permissions based Android Malware Detection using Machine Learning Techniques , 2017, ISEC.

[126]  Olga Gadyatskaya,et al.  Evaluation of Resource-Based App Repackaging Detection in Android , 2016, NordSec.

[127]  Ali A. Ghorbani,et al.  Android Botnets: What URLs are Telling Us , 2015, NSS.

[128]  Somesh Jha,et al.  Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.

[129]  Jing Xu,et al.  MalDetector-using permission combinations to evaluate malicious features of Android App , 2015, 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[130]  Dhiren Patel,et al.  Android vs iOS Security: A Comparative Study , 2015, 2015 12th International Conference on Information Technology - New Generations.

[131]  Ali A. Ghorbani,et al.  DroidKin: Lightweight Detection of Android Apps Similarity , 2014, SecureComm.

[132]  Julian Schütte,et al.  Native code execution control for attack mitigation on android , 2013, SPSM '13.

[133]  Win Zaw,et al.  Permission-Based Android Malware Detection , 2013 .

[134]  M. Eslahi,et al.  MoBots: A new generation of botnets on mobile devices and networks , 2012, 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).

[135]  Niklas Lavesson,et al.  Accurate Adware Detection Using Opcode Sequence Extraction , 2011, 2011 Sixth International Conference on Availability, Reliability and Security.

[136]  David A. Wagner,et al.  Analyzing inter-application communication in Android , 2011, MobiSys '11.

[137]  S. Vijayarani,et al.  Analysis of privacy preserving K-anonymity methods and techniques , 2010, 2010 International Conference on Communication and Computational Intelligence (INCOCCI).

[138]  R. Wolff,et al.  Providing k-anonymity in data mining , 2008, The VLDB Journal.

[139]  Mingxing Duan,et al.  DynamicNet: Efficient Federated Learning for Mobile Edge Computing With Dynamic Privacy Budget and Aggregation Weights , 2024, IEEE Transactions on Consumer Electronics.

[140]  Giacomo Iadarola,et al.  StegWare: A Novel Malware Model Exploiting Payload Steganography and Dynamic Compilation , 2023, ICISSP.

[141]  Matthias Nickles,et al.  Federated Learning as a Privacy Solution - An Overview , 2023, ISM.

[142]  Qussai M. Yaseen,et al.  A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features , 2023, IEEE Access.

[143]  Giacomo Iadarola,et al.  A Model Checking-based Approach to Malicious Family Detection in iOS Environment , 2022, KES.

[144]  Muhammed Basheer Jasser,et al.  A Method for Automatic Android Malware Detection Based on Static Analysis and Deep Learning , 2022, IEEE Access.

[145]  Gaurav Choudhary,et al.  De-LADY: Deep learning based Android malware detection using Dynamic features , 2021, J. Internet Serv. Inf. Secur..

[146]  Dimitris Geneiatakis,et al.  On machine learning effectiveness for malware detection in Android OS using static analysis data , 2021, J. Inf. Secur. Appl..

[147]  Tamim Ahmed Khan,et al.  A Dynamic Robust DL-Based Model for Android Malware Detection , 2021, IEEE Access.

[148]  Bo Liu,et al.  A Survey of Android Malware Static Detection Technology Based on Machine Learning , 2021, Mob. Inf. Syst..

[149]  Ning Zheng,et al.  Deep Model Poisoning Attack on Federated Learning , 2021, Future Internet.

[150]  Shweta Sharma,et al.  RansomAnalysis: The Evolution and Investigation of Android Ransomware , 2020 .

[151]  Markus Miettinen,et al.  Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System , 2020, Proceedings 2020 Workshop on Decentralized IoT Systems and Security.

[152]  Jiqiang Liu,et al.  Detecting Android Locker-Ransomware on Chinese Social Networks , 2019, IEEE Access.

[153]  Amir Jalaly Bidgoly,et al.  BENCHMARKING MACHINE LEARNING ALGORITHMS FOR ANDROID MALWARE DETECTION , 2019 .

[154]  Jianfeng Ma,et al.  A Novel Dynamic Android Malware Detection System With Ensemble Learning , 2018, IEEE Access.

[155]  Hyoungshick Kim,et al.  Open Sesame! Design and Implementation of Backdoor to Secretly Unlock Android Devices , 2017, J. Internet Serv. Inf. Secur..

[156]  A. Mourad,et al.  An Android-based Trojan Spyware to Study the NotificationListener Service Vulnerability , 2016, ANT/SEIT.

[157]  Nurlida Basir,et al.  A Systematic Review Analysis of Root Exploitation for Mobile Botnet Detection , 2016 .

[158]  Christopher Krügel,et al.  Execute This! Analyzing Unsafe and Malicious Dynamic Code Loading in Android Applications , 2014, NDSS.

[159]  R. Umarani,et al.  A Survey on Various Most Common Encryption Techniques , 2012 .