Results 81 to 90 of about 465,682 (204)

Malware behavior image for malware variant identification

, 2014
Several methods have been devised by researchers to facilitate malware analysis and one of them is through malware visualization. Malware visualization is a field that focuses on representing malware features in a form of visual cues that could be used ...
Mohd. Shaid, Syed Zainudeen, Mohd Aizaini Maarof, Maarof, Mohd. Aizaini, Syed Zainudeen Mohd Shaid   +3 more
core   +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

A few-shot malware classification approach for unknown family recognition using malware feature visualization

, 2021
With the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware is constantly growing and evolving with the use of sophisticated obfuscation techniques.
Khandhar, Shubham (author)
core  

Learning and classification of malware behavior

, 2022
S.108-125Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature ...
Düssel, P., Rieck, K., Holz, T., Willems, C., Laskov, P.   +4 more
core   +1 more source

Systematic literature review for malware visualization techniques [PDF]

, 2018
Analyzing the activities or the behaviors of malicious scripts highly depends on extracted features. It is also significant to know which features are more effective for certain visualization types.
Magalingam, Pritheega, Samy, Ganthan Narayana, Mohd. Ariffin, Aswami Fadillah, Yunos, Zahri, Md. Senan, Mohd. Firham Efendy, Mohd. Khairudin, Wafa   +5 more
core   +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

Reanimating Historic Malware Samples

, 2020
Many types of malicious software are controlled from an attacker’s command and control (C2) servers. Anti-virus organizations seek to defeat malware attacks by requesting removal of C2 server Domain Name Server (DNS) records.
Iqbal Gondal, Lakhotia, Arun, Black, Paul, Peter Vamplew, Arun Lakhotia, Paul Black, Vamplew, Peter, Gondal, Iqbal   +7 more
core   +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