Results 61 to 70 of about 1,517 (174)

CAR‐T Cells: Current Status, Challenges, and Future Prospects

MedComm, Volume 7, Issue 5, May 2026.
This graphical abstract outlines the current status, challenges, and future prospects of CAR‐T cells. The biological basis of CAR‐T cell therapy is the elegant redirection of adaptive immunity. Its initial successes have exposed a landscape of multifaceted challenges.
Aya Sedky Adly, Guillaume Cartron, Afnan Sedky Adly, Jean‐Christophe Egea, Pierre‐Yves Collart Dutilleul, Mahmoud Sedky Adly, Martin Villalba   +6 more
wiley   +1 more source

RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification

IEEE Access
Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts.
S. Puneeth, Shyam Lal, Mahendra Pratap Singh, B. S. Raghavendra   +3 more
doaj   +1 more source

GRASE: Granulometry Analysis With Semi Eager Classifier to Detect Malware.

International Journal of Interactive Multimedia and Artificial Intelligence
Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT).
Mahendra Deore, Manoj Tarambale, Jambi Ratna Raja Kumar, Sachin Sakhare   +3 more
doaj   +1 more source

Efficient Malware Classification using Transfer Learning and Stacked Ensemble Techniques [PDF]

International Journal of Mathematical, Engineering and Management Sciences
The exponential growth of internet usage and communication devices has led to heightened security vulnerabilities, including the proliferation of malware such as viruses, ransomware, trojans, and spyware. These increasingly sophisticated malware variants
Krishna Kumar, Hardwari Lal Mandoria, Rajeev Singh   +2 more
doaj   +1 more source

Retrieval-Augmented Few-Shot Malware Detection via Binary Visualization and Vision–Language Embeddings

Applied Sciences
The rapid evolution of malware families poses significant challenges for cybersecurity systems, particularly when newly emerging threats lack sufficient labeled data.
Woo Jin Jung, Nae-Joung Kwak, Byoung-Yup Lee   +2 more
doaj   +1 more source

Deep visualization classification method for malicious code based on Ngram-TFIDF

Tongxin xuebao
With the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable.
WANG Jinwei, CHEN Zhengjia, XIE Xue, LUO Xiangyang, MA Bin   +4 more
doaj   +2 more sources

Robust Intelligent Malware Detection Using Deep Learning

IEEE Access, 2019
Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks ...
R. Vinayakumar, Mamoun Alazab, K. P. Soman, Prabaharan Poornachandran, Sitalakshmi Venkatraman   +4 more
doaj   +1 more source

Visualization techniques for malware behavior analysis

SPIE Proceedings, 2011
Malware spread via Internet is a great security threat, so studying their behavior is important to identify and classify them. Using SSDT hooking we can obtain malware behavior by running it in a controlled environment and capturing interactions with the target operating system regarding file, process, registry, network and mutex activities.
André R. A. Grégio, Rafael D. C. Santos   +1 more
openaire   +2 more sources

Wavelet-Based and MAML-Driven Framework for Enhanced Few-Shot Malware Classification

Applied Sciences
Traditional malware classification approaches primarily address fixed sets of well-studied malware types and therefore struggle to accommodate the continual emergence of novel or previously unseen malware strains.
Abdullah Almuqrin, Ibrahim Mutambik, Majed Abusharhah   +2 more
doaj   +1 more source

Through the static: Demystifying malware visualization via explainability

Journal of Information Security and Applications
Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task.
Brosolo, Matteo, P., Vinod, Conti, Mauro
openaire   +2 more sources