Smart Malware Detection: From Signatures to Artificial Intelligence

2020 ◽  
Author(s):  
Jannatul Ferdaos ◽  
Chandani Vaya ◽  
Anchal Bhalla ◽  
Ami Tharayil ◽  
May El Barachi
Keyword(s):  
Artificial Intelligence ◽  
Malware Detection

scholarly journals A REVIEW OF MACHINE LEARNING AND ITS APPLICATIONS

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Vishal Bari ◽  
Dr.M.S Gaikwad ◽  
Dr. Rajendra Babar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Information Retrieval ◽  
Intrusion Detection ◽  
Malware Detection ◽  
Design Algorithms ◽  
Learning Machine ◽  
Image Decoding

Today, huge amounts of data are available everywhere. Therefore, analyzing this data is very important to derive useful information from it and develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an essential part of artificial intelligence used to design algorithms based on data trends and past relationships between data. Machine learning is used in a variety of areas such as bioinformatics, intrusion detection, information retrieval, games, marketing, malware detection, and image decoding. This paper shows the work of various authors in the field of machine learning in various application areas.

A review of artificial intelligence based malware detection using deep learning

2021 ◽  
Author(s):  
Al-Ani Mustafa Majid ◽  
Ahmed Jamal Alshaibi ◽  
Evgeny Kostyuchenko ◽  
Alexander Shelupanov
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Malware Detection

Malware Detection and Prevention using Artificial Intelligence Techniques

2021 ◽  
Author(s):  
Md Jobair Hossain Faruk ◽  
Hossain Shahriar ◽  
Maria Valero ◽  
Farhat Lamia Barsha ◽  
Shahriar Sobhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Malware Detection ◽  
Artificial Intelligence Techniques

scholarly journals Antivirus Applied to Jar Malware Detection based on Runtime Behaviors

2021 ◽  
Author(s):  
Ricardo Pinheiro ◽  
Sidney Lima ◽  
Danilo Souza ◽  
Sthéfano Silva ◽  
Petrônio Lopes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
World Wide ◽  
Initial Conditions ◽  
Malware Detection ◽  
Detection Methods ◽  
Controlled Environment ◽  
Modus Operandi ◽  
Average Performance ◽  
Learning Functions ◽  
The World

Abstract Background and Objective: Java vulnerabilities correspond to 91% of all exploits observed on the World Wide Web. Then, this present work aims to create an antivirus software with machine learning and artificial intelligence, master in Java malwares detection.. Methods: Within the proposed methodology, the suspect Jar sample is executed in order to infect, intentionally, Windows OS monitored in a controlled environment. In all, our antivirus monitors and ponders, statistically, 6,824 actions that the suspected Jar file can do when executed. Results: Our antivirus achieves an average performance of 91.58% in the distinction between benign and malwares Jar files. Different initial conditions, learning functions and architectures of our antivirus are investigated in order to maximize their accuracy.Conclusions: The limitations of commercial antiviruses can be supplied by intelligent antiviruses.Instead of blacklist-based models, our antivirus allows Jar malware detection in a preventive way and not in a reactive manner as Oracle's Java and traditional antivirus modus operandi.

scholarly journals Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection

2020 ◽  
Vol 10 (15) ◽  
pp. 5173 ◽  
Author(s):  
Sunoh Choi
Keyword(s):  
Artificial Intelligence ◽  
Detection Rate ◽  
Nearest Neighbor ◽  
Malware Detection ◽  
Search Time ◽  
Vantage Point ◽  
Detection Time ◽  
K Nearest Neighbor ◽  
Knn Classification ◽  
Long Time

Every day, hundreds of thousands of new malicious files are created. Existing pattern-based antivirus solutions have difficulty detecting these new malicious files. Artificial intelligence (AI)–based malware detection has been proposed to solve the problem; however, it takes a long time. Similarity hash–based detection has also been proposed; however, it has a low detection rate. To solve these problems, we propose k-nearest-neighbor (kNN) classification for malware detection with a vantage-point (VP) tree using a similarity hash. When we use kNN classification, we reduce the detection time by 67% and increase the detection rate by 25%. With a VP tree using a similarity hash, we reduce the similarity-hash search time by 20%.

scholarly journals Malicious Powershell Detection Using Graph Convolution Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6429
Author(s):  
Sunoh Choi
Keyword(s):  
Artificial Intelligence ◽  
Adjacency Matrix ◽  
Rapid Growth ◽  
Detection Rate ◽  
Detection Method ◽  
Malware Detection ◽  
Detection Methods ◽  
Jaccard Similarity ◽  
Malicious Behaviors

The internet’s rapid growth has resulted in an increase in the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used in malicious behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods have been widely studied. Among AI techniques, the graph convolution network (GCN) was recently introduced. Here, we propose a malicious powershell detection method using a GCN. To use the GCN, we needed an adjacency matrix. Therefore, we proposed an adjacency matrix generation method using the Jaccard similarity. In addition, we show that the malicious powershell detection rate is increased by approximately 8.2% using GCN.

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