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A Survey of Software Clone Detection from Security Perspective

Haibo Zhang, Kouichi Sakurai
2021 IEEE Access  
Yujie Gu and all the reviewers for their helpful advice on this manuscript. We thank Dr.  ...  Maxine Garcia from Edanz Group ( for editing a draft of this manuscript.  ...  [12] presented a malicious code clone detection technique for binary code based on token normalization levels.  ... 
doi:10.1109/access.2021.3065872 fatcat:fh6ysdrcqvawpay6s767i3na2u


David A. Noever, Samantha E. Miller Noever
2021 Zenodo  
It further extends the image classification problem to other cybersecurity benchmarks such as malware signatures extracted from binary headers, with an 80% overall accuracy to detect computer viruses as  ...  Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms.  ...  For cybersecurity applications, this work updates the UNSW-NB15 attack dataset [1] [2] [3] [4] and extends the popular intrusions detection system (IDS) originally inspired by the KDD-99/DARPA challenge  ... 
doi:10.5281/zenodo.4910770 fatcat:33dmempm5fhnrf5rt2bmkii2ie

Introduction to Hardware Security

Yier Jin
2015 Electronics  
However, the understanding of hardware security is often mixed with cybersecurity and cryptography, especially cryptographic hardware.  ...  For the same reason, the research scope of hardware security has never been clearly defined.  ...  cybersecurity solutions, including emerging devices in hardware security applications and hardware-assisted cybersecurity.  ... 
doi:10.3390/electronics4040763 fatcat:njdmrvdogzc73mp4yun6rpnun4

ThingGate: A Gateway for Managing Traffic of Bare-metal IoT Honeypot

Chun-Jung Wu, Katsunari Yoshioka, Tsutomu Matsumoto
2020 Journal of Information Processing  
In order to observe and analyze such a variety of attacks in depth, there is an increasing need for bare-metal IoT devices as a honeypot, since it is costly to emulate device-specific vulnerabilities and  ...  For instances, web UI of IP Cameras and routers are constantly searched and accessed if vulnerable.  ...  Special thanks go to Dr. Erwan Le Malecot for the critical comments and help on the network infrastructure.  ... 
doi:10.2197/ipsjjip.28.481 fatcat:xwy2g5pxcjctxa47n366nhkfsq

Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks

Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda K. Toscano-Medina, Jesus Olivares-Mercado, Jose Portillo-Portilo, Juan-Gerardo Avalos, Luis Javier García Villalba
2022 Applied Sciences  
This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of  ...  However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user's knowledge.  ...  Acknowledgments: The authors thank the National Science and Technology Council of Mexico (CONACyT) and the Instituto Politécnico Nacional for the financial support for this research.  ... 
doi:10.3390/app12073234 fatcat:u65ziddminc2lf6ohgmzetnvne

Android Malware Family Classification Based on Resource Consumption over Time [article]

Luca Massarelli, Leonardo Aniello, Claudio Ciccotelli, Leonardo Querzoni, Daniele Ucci, Roberto Baldoni
2017 arXiv   pre-print
Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques  ...  The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior.  ...  Acknowledgments This present work has been partially supported by a grant of the Italian Presidency of Ministry Council, and by CINI Cybersecurity National Laboratory within the project FilieraSicura:  ... 
arXiv:1709.00875v1 fatcat:2rnuz7n7ofdsxdrzesrsmqjaki

ICSREF: A Framework for Automated Reverse Engineering of Industrial Control Systems Binaries [article]

Anastasis Keliris, Michail Maniatakos
2018 arXiv   pre-print
To evaluate our framework we create a database of samples by collecting real PLC binaries from public code repositories, as well as developing binaries in-house.  ...  Despite this attention, automation of the reverse engineering process of ICS binaries for programmable logic controllers remains an open problem, mainly due to the use of proprietary compilers by ICS vendors  ...  When analyzing PLC malware, ICSREF can also assist in PLC code authorship attribution by identifying malicious actors through fingerprinting the code they utilize.  ... 
arXiv:1812.03478v1 fatcat:yb7nciblmvdj5bviy6vnqtvlly

A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes

Rosario Arjona, Miguel Prada-Delgado, Javier Arcenegui, Iluminada Baturone
2018 Sensors  
Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required.  ...  Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module  ...  Acknowledgments: The authors would like to thank Alfredo Vázquez-Reyes for the first experiments with BLE chips. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s18082429 pmid:30049967 fatcat:6qs43dn7vrccjk3zycdhvrxmhq

SPAM: Signal Processing to Analyze Malware [Applications Corner]

Lakshmanan Nataraj, B.S. Manjunath
2016 IEEE Signal Processing Magazine  
Acknowledgments We would like to thank Prof. Giovanni Vigna and Prof. Christopher Kruegel of UCSB Seclab for providing the malware data and for their valuable suggestions.  ...  We are thankful to our colleagues who worked in this project: Dr. Gregoire Jacob, Dr. Dhilung Kirat and Dr. S. Karthikeyan. We would also like to thank Dr.  ...  During the query phase, the fingerprint for the new sample is computed and matched with the existing fingerprints in the database to retrieve the top matches.  ... 
doi:10.1109/msp.2015.2507185 fatcat:bkypczxmo5efdll4i7u46dlw6a

An emerging threat Fileless malware: a survey and research challenges

Sudhakar, Sushil Kumar
2020 Cybersecurity  
The malware leverages the power of operating systems, trusted tools to accomplish its malicious intent.  ...  The fileless malware attack is catastrophic for any enterprise because of its persistence, and power to evade any anti-virus solutions.  ...  Acknowledgements Not applicable. Author's contribution The first author conceived the idea of the study and wrote the paper; all authors discussed and revised the final manuscript.  ... 
doi:10.1186/s42400-019-0043-x fatcat:yfi4kodjirgx5e5xp36fwibpia

POW-HOW: An enduring timing side-channel to evade online malware sandboxes [article]

Antonio Nappa, Panagiotis Papadopoulos, Matteo Varvello, Daniel Aceituno Gomez, Juan Tapiador, Andrea Lanzi
2021 arXiv   pre-print
technique used by malware for evading the analysis system is to monitor the execution environment, detect the presence of any debugging artifacts, and hide its malicious behavior if needed.  ...  malicious behaviors.  ...  An example [63] of application and analysis of hierarchical similarity for binary program comparison shows O(n 2 ) complexity.  ... 
arXiv:2109.02979v2 fatcat:2pgzanyjtrdjbmz7pqhz7qmcdi

The Curious Case of Machine Learning In Malware Detection [article]

Sherif Saad, William Briguglio, Haytham Elmiligi
2019 arXiv   pre-print
Next, we discuss possible solutions to these challenges and present the requirements of next-generation malware detection.  ...  A comprehensive review of machine learning for malware detection is presented.  ...  The proposed method requires access to the malware binaries and source code, besides, it is mainly useful for static malware detection systems.  ... 
arXiv:1905.07573v1 fatcat:o2nv3rrua5gpzng3sm2zyxbwla

A Deep Learning Approach for Malware and Software Piracy Threat Detection

K. Aldriwish
2021 Engineering, Technology & Applied Science Research  
Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats.  ...  Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism.  ...  Therefore, a fingerprinting method was proposed instead of the parse tree kernel. Authors in [21] introduced a behavioral approach called BPlag to detect source code plagiarism.  ... 
doi:10.48084/etasr.4412 fatcat:tdu5smb5hjgsdeegll6nmlo7vy

MobiX: A software proposal based authentication service for mobile devices

Ali Alsaadi, Anup Kumar, Bin Xie, Adel Elmaghraby
2016 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)  
MobiX is a software security solution technology to enhance the networked mobile device trustworthiness, application integrity, preventing users from malware attacks.  ...  MobiX provides two authentication layers and two additional security layers in addition to the conventional username-password for mobile devices in a unique system design.  ...  The other recent advanced methods, e.g., using biometrics such as fingerprint, have been shown to be vulnerable to cybersecurity attacks, e.g., [6, 7] .  ... 
doi:10.1109/isspit.2016.7886041 dblp:conf/isspit/AlsaadiK0E16 fatcat:xsmxsrbswjeotkfgbtvvny2nte

On the application of sensor authentication with intrinsic physical features to vehicle security

Gianmarco Baldini, Raimondo Giuliani, Monica Gemo, Franc Dimc
2021 Computers & electrical engineering  
The application of different signal processing algorithms to an experimental dataset of 12 Hall sensors was evaluated.  ...  A B S T R A C T Cybersecurity in the automotive sector is becoming increasingly important as modern vehicles can be exposed to cybersecurity threats and regulatory fraud.  ...  Acknowledgment We acknowledge the technical support of the European Commission Joint Research Centre employee Jorge Figueiredo Morgado for the set up of the materials.  ... 
doi:10.1016/j.compeleceng.2021.107053 fatcat:q7sq7gebebhybgw7ewohpr32da
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