What Are The Types of Malware? Must Read

Feb 29, 20240 likes17 views

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What Are The Types of Malware
Analysis
Malware analysis is a crucial process in cybersecurity, aimed at understanding the
behavior, purpose, and impact of malicious software. By analyzing malware, security
professionals can develop effective countermeasures to protect systems and networks.
There are primarily two types of malware analysis: Static Analysis and Dynamic Analysis.
Each type has its methodologies and tools, and they often complement each other in the
malware analysis process.
1. Static Analysis
Static analysis involves examining the malware without executing it. The goal is to extract
as much information as possible from the malware's binary code and resources. This type
of analysis can provide insights into the functionality, origin, and potential capabilities of
the malware without the risk of infection or triggering any malicious behavior.
Key Aspects of Static Analysis include:
• Code Disassembly: Using disassemblers (like IDA Pro, Ghidra) to convert binary
code into assembly language, making it easier to understand the malware's
instructions.
• Signature Extraction: Identifying unique strings, patterns, or sequences of bytes
that can be used to detect and classify malware.
• Cryptography Analysis: Identifying cryptographic algorithms used for
communication or data obfuscation.
• Resource Extraction: Analyzing embedded resources such as images, strings, or
configuration data that can reveal the malware's behavior or intent.
2. Dynamic Analysis
Dynamic analysis, on the other hand, involves executing the malware in a controlled,
isolated environment (often referred to as a sandbox) to observe its behavior in real-time.
This method allows analysts to understand how the malware interacts with the system,
network, and other applications.
Key Aspects of Dynamic Analysis include:
• Behavior Observation: Monitoring the actions taken by the malware, such as file
creation/deletion, registry changes, network communications, and system
modifications.
• Network Traffic Analysis: Using tools (like Wireshark, TCPDump) to capture and
analyze network traffic generated by the malware, identifying command and control
(C&C) servers, data exfiltration techniques, and other network-based indicators.

• API Calls Monitoring: Observing the system and library calls made by the malware,
which can provide insights into its operational tactics.
• Sandbox Testing: Utilizing automated sandbox environments (like Cuckoo
Sandbox) to safely run malware and collect detailed reports on its activities and
behaviors.
Complementary Approaches
In practice, both static and dynamic analysis are often used together to provide a
comprehensive understanding of malware. Static analysis can quickly provide an overview
and identify key components without the risks associated with running the malware.
Dynamic analysis complements this by revealing how the malware behaves within a
system and how it communicates over networks.
Advanced Techniques
Beyond these foundational approaches, advanced techniques like reverse engineering and
memory forensics are also employed to delve deeper into complex malware samples.
Reverse engineering involves deconstructing the malware to its source code to thoroughly
understand its mechanisms, while memory forensics examines the system's memory for
malicious artifacts and indicators of compromise that are only observable while the
malware is running.
Understanding the types of malware analysis and applying the appropriate methods are
crucial for effectively combating malware and enhancing cybersecurity defenses.
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Abstract: The exponential growth of the internet and new technology lead today's world in a hectic situation both positive as well as the negative module. Cybercriminals gamble in the dark net using numerous techniques. This leads to cybercrime. Cyber threats like Malware attempt to infiltrate the computer or mobile device offline or internet, chat(online), and anyone can be a potential target. Malware is also known as malicious software is often used by cybercriminals to achieve their goal by tracking internet activity, capturing sensitive information, or blocking computer access. Reverse engineering is one of the best ways to prevent and is a powerful tool to keep the fight against cyber attacks. Most people in the cyber world see it as a black hat—It is said as being used to steal data and intellectual property. But when it is in the hands of cybersecurity experts, reverse engineering dons the white hat of the hero. Looking at the program from the outside in –often by a third party that had no hand in writing the code. It allows those who practice it to understand how a given program or system works when no source code is available. Reverse engineering accomplishing several tasks related to cybersecurity: finding system vulnerabilities, researching malware &analyzing the complexity of restoring core software algorithms that can further protect against theft. It is hard to hack certain software. Keywords: Malware, threat, vulnerablity, detection, reverse engineering, analysis. Title: Malware analysis and detection using reverse Engineering Author: B.Rashmitha, J. Alwina Beauty Angelin, E.R. Ramesh International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 10, Issue 2, Month: April 2022 - June 2022 Page: (1-4) Published Date: 01-April-2022 Research Publish Journals Available at: www.researchpublish.com You can Direct download full research paper at given below link: https://www.researchpublish.com/papers/malware-analysis-and-detection-using-reverse-engineering Academia Link: https://www.academia.edu/76069664/Malware_analysis_and_detection_using_reverse_Engineering_Available_at_www_researchpublish_com_journal_name_International_Journal_of_Computer_Science_and_Information_Technology_Research

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In the last few years, the evolution of information technology has resulted in thedevelopmentof several interesting and sensitive fields such as the dark Web and cyber-criminality, especially using ransomware attacks. This paper aims to bring out only critical features and make their observation, or not, in software behaviour sufficient to decide whether it is ransomware or not. Therefore, we propose a new solution for ransomware detection based on machine learning algorithms and system calls. First, we introduce our produced dataset of collected system calls of both ransomware and Benignware. Then, we push pre-processing steps deeply to reduce efficiently data dimensionality. After that, we introduce a new technique to select pertinent features. Next, we bring out the critical system calls, their importance and their contribution to the distinction between dataset elements. Finally, we present our model that achieves an overall accuracy of 99.81% after K-Fold cross-validation.

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In the last few years, the evolution of information technology has resulted in the development of several interesting and sensitive fields such as the dark Web and cyber-criminality, especially using ransomware attacks. This paper aims to bring out only critical features and make their observation, or not, in software behaviour sufficient to decide whether it is ransomware or not. Therefore, we propose a new solution for ransomware detection based on machine learning algorithms and system calls. First, we introduce our produced dataset of collected system calls of both ransomware and Benignware. Then, we push preprocessing steps deeply to reduce efficiently data dimensionality. After that, we introduce a new technique to select pertinent features. Next, we bring out the critical system calls, their importance and their contribution to the distinction between dataset elements. Finally, we present our model that achieves an overall accuracy of 99.81% after K-Fold cross-validation.

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Malicious Code Intrusion Detection using Machine Learning and Indicators of C...IJCSIS Research Publications

Malicious activities (malcodes) are self replicating malware and a major security threat in a network environment. Timely detection and system alert flags are very essential to prevent rapid malcodes spreading in the network. The difficulty in detecting malcodes is that they evolve over time. Despite the fact that signature-based tools, are generally used to secure systems, signature-based malcode detectors neglect to recognize muddled and beforehand concealed malcode executables. Automatic signature generation systems has likewise been use to address the issue of malcodes, yet there are many works required for good detection. Base on the behavior way of malcodes, a behavior approach is required for such detection. Specifically, we require a dynamic investigation and behavior Rule Base system that distinguishes malcodes without erroneously block legitimate traffic or increase false alarms. This paper proposed and discussed the approach using Machine learning and Indicators of Compromise (IOC) to analyze intrusion in a network, to identify the cause of the attack and to provide future detection. This paper proposed the use of behaviour malware analysis framework to analyze intrusion data, apply clustering algorithm on the analyzed data and generate IOC from the clustered data for IOCRule, which will be implemented into Snort Intrusion Detection System (IDS) for malicious code detection.

data mining for security applicationbharatsvnit

The document discusses various ways that data mining can be applied for security applications such as intrusion detection, firewall policy management, worm detection, and counter-terrorism surveillance. It describes techniques like anomaly detection, link analysis, classification, and prediction that can help detect cyber attacks, trace malware authors, and predict future threats. It also addresses challenges of working with real-time streaming data from sensors for critical applications.

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