Malware detection is a crucial term for preserving the integrity and safety of computer systems and different networks from malicious digital threats and attacks. The Malware Detection Model plays an important role in keeping our system secure from malware. but with the advancement in machine learning, there is a drastic rise in advanced attacks where Conventional malware detection techniques frequently fail to identify new versions of attacks. These attacks misguide the classifier by giving false input and are capable of escaping from the security system. To overcome this constraint first, we need to identify advanced persistent threats (APTs) and then train a detection model to enhance safety and security. However, this paper first discusses the unified malware classification framework to understand the step-by-step malware detection process. Then it will summarize the challenges faced by attackers and emphasize on the limitations of other research done in this area. the explanation of proposed malware detection techniques are also explained.
REFERENCES
1.
S.
Verwer
, A.
Nadeem
, C.
Hammerschmidt
, L.
Bliek
, A.
Al-Dujaili
, and U.-M.
O’Reilly
, “The robust malware detection challenge and greedy random accelerated multi-bit search
,” in Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security
, 2020
, pp. 61
–70
.2.
O.
Ibitoye
, R.
Abou-Khamis
, A.
Matrawy
, and M. O.
Shafiq
, “The threat of adversarial attacks on machine learning in network security–a survey
,” arXiv preprint
arXiv:1911.02621, 2019
.3.
D.
Maiorca
, B.
Biggio
, and G.
Giacinto
, “Towards adversarial malware detection: Lessons learned from pdf-based attacks
,” ACM Computing Surveys (CSUR)
, vol. 52
, no. 4
, pp. 1
–36
, 2019
.4.
Y.
Zhang
, H.
Li
, Y.
Zheng
, S.
Yao
, and J.
Jiang
, “Enhanced dnns for malware classification with gan-based adversarial training
,” Journal of Computer Virology and Hacking Techniques
, vol. 17
, pp. 153
–163
, 2021
.5.
L.
Demetrio
, S. E.
Coull
, B.
Biggio
, G.
Lagorio
, A.
Armando
, and Roli
, “Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection
,” ACM Transactions on Privacy and Security (TOPS)
, vol. 24
, no. 4
, pp. 1
–31
, 2021
.6.
S.
Patil
, V.
Varadarajan
, D.
Walimbe
, S.
Gulechha
, S.
Shenoy
, Raina
, and K.
Kotecha
, “Improving the robustness of ai-based malware detection using adversarial machine learning
,” Algorithms
, vol. 14
, no. 10
, 2021
. [Online]. Available: https://www.mdpi.com/1999-4893/14/10/2977.
J.
Wang
, X.
Chang
, Y.
Wang
, R. J.
Rodŕıguez
, and J.
Zhang
, “Lsgan-at: enhancing malware detector robustness against adversarial examples
,” Cybersecurity
, vol. 4
, pp. 1
–15
, 2021
.8.
O.
Suciu
, S. E.
Coull
, and J.
Johns
, “Exploring adversarial examples in malware detection,” in 2019 IEEE Security and Privacy Workshops (SPW)
. IEEE
, 2019
, pp. 8
–14
.9.
S.
Shukla
, “Design of secure and robust cognitive system for malware detection
,” arXiv preprint arXiv:2208.02310, 2022
.10.
N.
Akhtar
and A.
Mian
, “Threat of adversarial attacks on deep learning in computer vision: A survey
,” Ieee Access
, vol. 6
, pp. 14 410
–14 430
, 2018
.11.
A.
Dhavlle
, S.
Shukla
, S.
Rafatirad
, H.
Homayoun
, and S. M. P.
Di-nakarrao
, “Hmd-hardener: Adversarially robust and efficient hardware-assisted runtime malware detection,” in 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
. IEEE
, 2021
, pp. 1769
–1774
.12.
X.
Qi
, Y.
Tang
, H.
Wang
, T.
Liu
, and J.
Jing
, “Adversarial example attacks against intelligent malware detection: A survey,” in 2022 4th International Conference on Applied Machine Learning (ICAML)
. IEEE
, 2022
, pp. 1
–7
.13.
K.
Grosse
, N.
Papernot
, P.
Manoharan
, M.
Backes
, and P.
McDaniel
, “Adversarial perturbations against deep neural networks for malware classification
,” arXiv preprint arXiv:1606.04435, 2016
.14.
B.
Kolosnjaji
, A.
Demontis
, B.
Biggio
, D.
Maiorca
, G.
Giacinto
, Eckert
, and F.
Roli
, “Adversarial malware binaries: Evading deep learning for malware detection in executables,” in 2018 26th European signal processing conference (EUSIPCO)
. IEEE
, 2018
, pp. 533
–537
.15.
F.
Kreuk
, A.
Barak
, S.
Aviv-Reuven
, M.
Baruch
, B.
Pinkas
, and J.
Keshet
, “Deceiving end-to-end deep learning malware detectors using adversarial examples
,” arXiv preprint arXiv:1802.04528, 2018
.16.
I.
Rosenberg
, A.
Shabtai
, L.
Rokach
, and Y.
Elovici
, “Generic black-box end-to-end attack against state of the art api call based malware classifiers,” in Research in Attacks, Intrusions, and Defenses: 21st International Symposium, RAID 2018
, Heraklion, Crete, Greece
, September 10-12, 2018
, Proceedings 21. Springer, 2018
, pp. 490
–510
.17.
L.
Demetrio
, B.
Biggio
, G.
Lagorio
, F.
Roli
, and A.
Armando
, “Explaining vulnerabilities of deep learning to adversarial malware binaries
,” arXiv preprint arXiv:1901.03583, 2019
.18.
K.
Lucas
, M.
Sharif
, L.
Bauer
, M. K.
Reiter
, and S.
Shintre
, “Malware makeover: Breaking ml-based static analysis by modifying executable bytes
,” in Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security
, 2021
, pp. 744
–758
.19.
W.
Hu
and Y.
Tan
, “Generating adversarial malware examples for black-box attacks based on gan
,” in International Conference on Data Mining and Big Data. Springer
, 2022
, pp. 409
–423
.20.
W.
Hu
and Y.
Tan
, “Black-box attacks against rnn based malware detection algorithms
,” arXiv preprint arXiv:1705.08131, 2017
.21.
N.
Dabas
, P.
Sharma
et al., “Malanalyser: An effective and efficient windows malware detection method based on api call sequences
,” Expert Systems with Applications
, vol. 230
, p. 120756
, 2023
.22.
H.
Rathore
, A.
Nandanwar
, S. K.
Sahay
, and M.
Sewak
, “Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses
,” Forensic Science International: Digital Investigation
, vol. 44
, p. 301511
, 2023
.23.
Y.
Nagano
and R.
Uda
, “Static analysis with paragraph vector for malware detection
,” in Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication
, 2017
, pp. 1
–7
.24.
X.
Jianhua
, S.
Jing
, Z.
Yongjing
, L.
Wei
, and Z.
Yuning
, “Research on malware variant detection method based on deep neural network
,” in 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP)
, Jan 2021
, pp. 144
–147
.25.
L.
Jia
, Y.
Yang
, B.
Tang
, and Z.
Jiang
, “Ermds: A obfuscation dataset for evaluating robustness of learning-based malware detection system
,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations
, vol. 3
, no. 1
, p. 100106
, 2023
.26.
M. R.
Ebrahimi
, W.
Li
, Y.
Chai
, J.
Pacheco
, and H.
Chen
, “An adversarial reinforcement learning framework for robust machine learning-based malware detection,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW)
. IEEE
, 2022
, pp. 567
–576
.27.
K.
Ren
, T.
Zheng
, Z.
Qin
, and X.
Liu
, “Adversarial attacks and defenses in deep learning
,” Engineering
, vol. 6
, no. 3
, pp. 346
–360
, 2020
.28.
H.
Xu
, Y.
Ma
, H.-C.
Liu
, D.
Deb
, H.
Liu
, J.-L.
Tang
, and A. K.
Jain
, “Adversarial attacks and defenses in images, graphs and text: A review
,” International Journal of Automation and Computing
, vol. 17
, pp. 151
–178
, 2020
.29.
Y.
Fang
, Y.
Zeng
, B.
Li
, L.
Liu
, and L.
Zhang
, “Deepdetectnet vs rlattacknet: An adversarial method to improve deep learning-based static malware detection model
,” Plos one
, vol. 15
, no. 4
, p. e0231626
, 2020
.30.
X.
Peng
, H.
Xian
, Q.
Lu
, and X.
Lu
, “Semantics aware adversarial malware examples generation for black-box attacks
,” Applied Soft Computing
, vol. 109
, p. 107506
, 2021
.31.
H.
Rathore
, S. K.
Sahay
, P.
Nikam
, and M.
Sewak
, “Robust android malware detection system against adversarial attacks using q-learning
,” Information Systems Frontiers
, vol. 23
, pp. 867
–882
, 2021
.32.
K.
Shaukat
, S.
Luo
, and V.
Varadharajan
, “A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks
,” Engineering Applications of Artificial Intelligence
, vol. 116
, p. 105461
, 2022
.33.
M. E.
Khoda
, T.
Imam
, J.
Kamruzzaman
, I.
Gondal
, and A.
Rahman
, “Robust malware defense in industrial iot applications using machine learning with selective adversarial samples
,” IEEE Transactions on Industry Applications
, vol. 56
, no. 4
, pp. 4415
–4424
, 2019.34.
S.
Huda
, S.
Miah
, J.
Yearwood
, S.
Alyahya
, H.
Al-Dossari
, and R.
Doss
, “A malicious threat detection model for cloud assisted internet of things (cot) based industrial control system (ics) networks using deep belief network
,” Journal of Parallel and Distributed Computing
, vol. 120
, pp. 23
–31
, 2018
.35.
I.
´Incer Romeo
, M.
Theodorides
, S.
Afroz
, and D.
Wagner
, “Adver-sarially robust malware detection using monotonic classification
,” in Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics
, 2018
, pp. 54
–63
.36.
W.
Qiang
, L.
Yang
, and H.
Jin
, “Efficient and robust malware detection based on control flow traces using deep neural networks
,” Computers & Security
, p. 102871
, 2022
.37.
W.
Li
, N.
Bala
, A.
Ahmar
, F.
Tovar
, A.
Battu
, and P.
Bambarkar
, “A robust malware detection approach for android system against adversarial example attacks,” in 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)
. IEEE
, 2019
, pp. 360
–365
.38.
S.
Rani
, K.
Tripathi
, Y.
Arora
, and A.
Kumar
, “Analysis of Anomaly detection of Malware using KNN,” in 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
, Gautam Buddha Nagar, India
, 23–25 February 2022 (IEEE
, 2022
). 39.
A.
Kumar
, H.
Singh
, P.
Kumar
, and B.
AlMangour
, Handbook of Smart Manufacturing
(CRC Press
, Boca Raton
, 2023
). 40.
S.
Rani
, A.
Kumar
, A.
Bagchi
, S.
Yadav
, and S.
Kumar
, “RPL Based Routing Protocols for Load Balancing in IoT Network
,” J. Phys.
1950
(1
), 012073
(2021
).
This content is only available via PDF.
© 2024 Author(s). Published under an exclusive license by AIP Publishing.
2024
Author(s)
You do not currently have access to this content.
