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AI assisted Malware Analysis: A Course for Next Generation Cybersecurity Workforce [article]

Maanak Gupta, Sudip Mittal, Mahmoud Abdelsalam
2020 arXiv   pre-print
classification and attribution, and (6) advanced malware research topics and case studies such as adversarial learning and Advanced Persistent Threat (APT) detection.  ...  datasets to learn about Cyber Threat Intelligence (CTI), malware analysis, and classification, among other important topics in cybersecurity.  ...  Acknowledgement This work was supported by National Science Foundation awards 2025682, 2025685, and 2025686.  ... 
arXiv:2009.11101v1 fatcat:jdqtzpffdbbtxfmqpg5o654uma

Machine Learning for Cyber Security using Big Data Analytics

R. Vijaya Lakshmi
2019 Zenodo  
., copying human behavior) but it can also reduce the efforts and/or time spent for both simple and difficult tasks like stock price prediction.  ...  In this paper, I emphasis on how Big Data be able to progress information security best practices. I am trying to apply machine learning procedures in cyber security using big data Analytics.  ...  Today, big data analytics is integral to Sophos' daily malware detection in multiple use cases: 1. Malware research and analysis. Malware is becoming more evasive and pervasive.  ... 
doi:10.5281/zenodo.3362228 fatcat:ehzzbba5l5gbjaeuj7evqymxgq

Artificial Intelligence in the Cyber Domain: Offense and Defense

Thanh Cong Truong, Quoc Bao Diep, Ivan Zelinka
2020 Symmetry  
This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.  ...  Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis.  ...  A recent trend of research in malware detection focused on mobile malware in general and Android malware in particular.  ... 
doi:10.3390/sym12030410 fatcat:7gyse3gaxjguhgkvfnbi7knkf4

Machine Learning Algorithms Applied to System Security: A Systematic Review

Ibrahim Goni, Salisu Bello, Umar T. Maigari
2020 Asian journal of applied science and technology  
make a decision, prediction, detection, classification, pattern recognition, responding and clustering based on the data collected.  ...  In [33] they presented a general review on the malware detection in mobile devices based on parallel and distributed network.  ... 
doi:10.38177/ajast.2020.4311 fatcat:jrtmzlswvjbtpms4m3c5hgan4q

Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
2018 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)  
Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware  ...  classification.  ...  Later on, researchers also applied file-size based segmentation on opcode frequency and improved the average accuracy using different Machine Learning models and claimed that Random Forest can provide  ... 
doi:10.1109/snpd.2018.8441123 dblp:conf/snpd/SewakSR18 fatcat:cn7aq5e5abbz7gnuxt7en5yy34

Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions [article]

Li Chen
2019 arXiv   pre-print
I will present the perspectives of efficacy, reliability and resiliency to formulate threat detection as computer vision problems and develop state-of-the-art image-based malware classification.  ...  This overview summarizes one component of my research: combining computer vision with malware exploit detection for enhanced security solutions.  ...  My research explores and addresses the current gaps within the intersection of security and artificial intelligence.  ... 
arXiv:1904.10504v1 fatcat:qqu2nyn2wbfaroltdhqmu7lpv4

IDS Malicious Flow Classification

I-Hsien Liu, Cheng-Hsiang Lo, Ta-Che Liu, Jung-Shian Li, Chuan-Gang Liu, Chu-Fen Li
2020 Journal of Robotics, Networking and Artificial Life (JRNAL)  
A B S T R A C T We will display two different kinds of experiments, which are Network-based Intrusion Detection System (NIDS)-based and dynamic-based analysis shows how artificial intelligence helps us  ...  detecting and classify malware.  ...  ACKNOWLEDGMENTS This work was supported by the MOST, Taiwan under contracts numbers MOST 108-2221-E-006-110-MY3 and MOST 108-2218-E-006-035-.  ... 
doi:10.2991/jrnal.k.200528.006 fatcat:7va2ph5nmvhlnf5q2q6c3tosaa

Crossing Point of Artificial Intelligence in Cybersecurity

Praveen Kumar Donepudi
2015 American Journal of Trade and Policy  
On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent  ...  To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions  ...  RESEARCH METHODOLOGY This paper plans to reveal insight into the idea of artificial intelligence, recognize the main areas of artificial intelligence that can be utilized in cybersecurity, and explain  ... 
doi:10.18034/ajtp.v2i3.493 fatcat:7xnhwohhuvhzbetquabg7qtw2e

Intelligent Hybrid Approach for Android Malware Detection based on Permissions and API Calls

Altyeb Altaher, Omar Mohammed
2017 International Journal of Advanced Computer Science and Applications  
For this purpose, two features selection algorithms, Information Gain (IG) and Pearson CorrCoef (PC) are employed to rank the individual permissions and API's calls based on their importance for classification  ...  In the second step, the proposed new hybrid approach for Android malware detection based on the combination of the Adaptive neural fuzzy Inference System (ANFIS) with the Particle Swarm Optimization (PSO  ...  Several research efforts have been presented for malware detection depending on the Android permissions used in the app.  ... 
doi:10.14569/ijacsa.2017.080608 fatcat:6ex4vkpabrhgbm4bda2vlv7rzi

Malware Classification for Cyber Physical System (CPS) based on Phylogenetics

2019 International Journal of Engineering and Advanced Technology  
This malware classification approach includes malware behavior, mode of attack and connected assets in the network. It can detect numerous forms of malware attacks based on correlation.  ...  Therefore, this paper presents a malware cyber physical system (CPS) classification to detect attacks.  ...  Moreover, based on the existing work, most of them focus on different techniques for mobile botnet detection, but few mention malware classification, detection and response models for the CPS.  ... 
doi:10.35940/ijeat.a2711.109119 fatcat:bdw5rdi5szeylgipjb4gwspzbu

Hancitor malware recognition using swarm intelligent technique

Laheeb M. Ibrahim, Maisirreem Atheeed Kamal, AbdulSattar A. Al-Alusi
2020 Computer Science and Information Technologies  
Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other.  ...  This paper introduces a malware recognition system for Hancitor malware using the Gray Wolf Optimization algorithm (GWO) and Artificial Bee Colony algorithm (ABC), which can effectively recognize Hancitor  ...  Figure 2 show the Hancitor malware Infected system WHY SWARM INTELLIGENCE (SI) Swarm intelligence (SI) is a branch of artificial intelligence (AI) branches and is specified by Gerardo Beni and Jing  ... 
doi:10.11591/csit.v2i3.p103-112 fatcat:bcjjkfg7ifg53g23pzmsi2h7iy

A Survey on Artificial Intelligence in Malware as Next-Generation Threats

Cong Truong Thanh, Ivan Zelinka
2019 The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL  
Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space.  ...  This survey aims at providing an overview on the way artificial intelligence can be used to power a malicious program that is: intelligent evasion techniques, autonomous malware, AI against itself, and  ...  Acknowledgement: The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2019/137, VSB Technical University of Ostrava.  ... 
doi:10.13164/mendel.2019.2.027 fatcat:okjpoz6uxnf4hgqbqsa4qxrare

Intelligent Malware - Trends and Possibilities

Jan Plucar, Jiří Frank, Daniel Walter, Ivan Zelinka
2021 The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL  
For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment  ...  This software has not only malware capabilities but also antimalware and can be used on both sides.  ...  Acknowledgement: The following grants are acknowledged for the financial support provided for this research: VŠB-TU internal grant SGS SP2021/72.  ... 
doi:10.13164/mendel.2021.1.018 fatcat:yeyjk7qybff5jbrysobfhhn5wa

Intelligent Malware Detection Using a Neural Network Ensemble Based on a Hybrid Search Mechanism

Stephen M. Akandwanaho, Muni Kooblal
2019 The African journal of information and communication  
This article outlines the development and testing of a neural network ensemble approach to malware detection, based on a hybrid search mechanism.  ...  Malware threats have become increasingly dynamic and complex, and, accordingly, artificial intelligence techniques have become the focal point for cybersecurity, as they are viewed as being more suited  ...  Most existing research has reported on the efficiency of machine learning (ML) and artificial intelligence (AI) techniques in malware detection and mitigation (Chen, Su & Qiao, 2018) .  ... 
doi:10.23962/10539/28660 fatcat:fa6sbqzsfjdhrgj3ny2g7nbzwa


2020 Issues in Information Systems  
This research paper will propose a hybrid detection model as a solution approach using artificial intelligence and machine learning (AI/ML) to combat and mitigate IoT cyber threats on cloud computing environments  ...  At the same time, it requires artificial intelligence (AI) to analyze the data stored at cloud infrastructure and make fast and reliable intelligent decisions.  ...  In flow-based malware detection using convolutional neural network research, the researchers suggested an automated malware detection method using CNN, and other machine learning algorithms (Yeo, et al  ... 
doi:10.48009/4_iis_2020_253-263 fatcat:ij6pq3vyyjgfjdk6drf7otymby
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