Towards a Fast Off-Line Static Malware Analysis Framework

Chikapa, Macdonald; Namanya, Anitta Patience

doi:10.1109/w-ficloud.2018.00035

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…The goal of deploying cyber-threats is to gain access to sensitive data for the purpose of conducting hostile activities. In [13], the author describes an approach for developing a protected data purposeful model that complies with sensitive data requirements. Modern technologies are not being utilised at the current level of development.…”

Section: Related Studymentioning

confidence: 99%

AI-enhanced Defense Against Ransomware Within the Organization’s Architecture

Brahmananda

2022

JCSANDM

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Ransomware is a type of revenue-generating tactic that cybercriminals utilize to improve their income. Businesses have spent billions of dollars recovering control of their resources, which may include confidential data, operational applications and models, financial transactions, and other information, as a result of malicious software. Ransomware can infiltrate a resource or device and restrict the owner from accessing or utilizing it. There are various obstacles that a business must overcome in order to avoid ransomware attacks. Traditional ransomware detection systems employ a static detection method in which a finite dataset is provided into the system and a logical check is performed to prevent ransomware attacks against the system. This was effective in the early stages of the internet, but the scenario of recent times is far more advanced, and as more and more cyber world contrivances have been analyzed, multiple gaps have been identified, to the benefit of ransomware attackers, who use these gaps to generate astronomically large sums of money. As a result, the suggested methodology aims to efficiently detect diverse patterns associated with various file formats by starting with their sources, data collecting, probabilistic identification of target devices, and deep learning classifier with intelligent detection. An organization can use the recommended approach to safeguard its data and prepare for future ransomware attacks by using it as a roadmap to lead them through their security efforts.

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Section: Related Studymentioning

confidence: 99%

AI-enhanced Defense Against Ransomware Within the Organization’s Architecture

Brahmananda

2022

JCSANDM

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“…Another mobile malware called Skulls [30] was developed in the same year with criminal motives and later formed the basis of PC malware. The later malware was found to overwrite mobile application files and substituted applications' icons with crossbones and skulls that stopped mobile device functionalities.…”

Section: A History Of Mobile Malwarementioning

confidence: 99%

Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies

Ashawa

Morris

2021

JISCR

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The open-source and popularity of Android attracts hackers and has multiplied security concerns targeting devices. As such, malware attacks on Android are one of the security challenges facing society. This paper presents an analysis of mobile malware evolution between 2000-2020. The paper presents mobile malware types and in-depth infection strategies malware deploys to infect mobile devices. Accordingly, factors that restricted the fast spread of early malware and those that enhance the fast propagation of recent malware are identified. Moreover, the paper discusses and classifies mobile malware based on privilege escalation and attack goals. Based on the reviewed survey papers, our research presents recommendations in the form of measures to cope with emerging security threats posed by malware and thus decrease threats and malware infection rates. Finally, we identify the need for a critical analysis of mobile malware frameworks to identify their weaknesses and strengths to develop a more robust, accurate, and scalable tool from an Android detection standpoint. The survey results facilitate the understanding of mobile malware evolution and the infection trend. They also help mobile malware analysts to understand the current evasion techniques mobile malware deploys

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“…First of all, most of the IoT devices have low computation power which limits the complication of security system [7]. In addition, the intensification of hidden malware attacks that target the IoT systems, necessitates the quick adoption of detection method which in turn is a complicated approach [8,9]. Next, the rapid developments in IoT devices and the resultant security risks must be dealt with extremely strong data protection methods.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

Maray¹,

Alqahtani²,

Alotaibi³

et al. 2023

Computers, Materials &Amp; Continua

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Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBN-CMC) model. The presented OBDDBN-CMC model intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Zscore data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, Grasshopper Optimization Algorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for 3102 CMC, 2023, vol.74, no.2 validation and the results confirmed the enhanced performance of OBDDBN-CMC model over recent approaches.

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Towards a Fast Off-Line Static Malware Analysis Framework

Cited by 11 publications

References 16 publications

AI-enhanced Defense Against Ransomware Within the Organization’s Architecture

AI-enhanced Defense Against Ransomware Within the Organization’s Architecture

Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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