A survey and taxonomy of program analysis for IoT platforms

Hamza, Alyaa A.; Abdel-Halim, Islam T.; Sobh, Mohamed A.; Bahaa-Eldin, Ayman M.

doi:10.1016/j.asej.2021.03.026

Cited by 7 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several surveys discussed malware detection using different techniques; for example, articles [ 39 , 40 , 41 ] offer a systematic and comprehensive description of machine-learning malware-detection strategies and DL techniques. It offers DL mechanisms categorized by the type of network input, according to the methods used to extract a feature vector representing the executable.…”

Section: Related Work and Research Goalsmentioning

confidence: 99%

“…In [ 5 , 41 ], security and privacy issues are presented in IoT platforms such as Samsung’s SmartThings, Apple’s HomeKit, Open-HAB, Amazon AWS IoT, and Android Things, that motivate program analysis techniques such as model checking, taint analysis, code instrumentation, and symbolic execution).…”

Section: Related Work and Research Goalsmentioning

confidence: 99%

“…Analysis types (static, dynamic, or hybrid) and analysis techniques are essential for building security analyzers to detect malware [ 41 ]. Many security analysis systems (SASs) are based on static analysis.…”

Section: Related Work and Research Goalsmentioning

confidence: 99%

See 2 more Smart Citations

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

Hamza

Halim

Sobh

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.

show abstract

Section: Related Work and Research Goalsmentioning

confidence: 99%

Section: Related Work and Research Goalsmentioning

confidence: 99%

See 1 more Smart Citation

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

Hamza

Halim

Sobh

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is due to the growth and expansion of the internet, smart mobile telephony, the expansion in Internet of Things (IoTs), and the increased digitization and digitalization. Though these technology penetrations are all positive signals to the global cyber-inclusion, it has come with corresponding increase in cyber risk mainly using malware [3]. Malware (malicious software) and Potentially Unwanted Software (PUS) is a broad term used to describe software with malicious intent that causes harm to computing resources and information systems.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Current Malware Trends and Defense Techniques: A Scoping Review with Empirical Case Studies

Cletus,

Opoku,

Weyori

2024

JAIT

View full text Add to dashboard Cite

attackers and defenders makes the malware ecosystems highly volatile, dynamic, stochastic, and unpredictable. The volatility of the ecosystem means that, both attackers and defenders are innovating to outwit each other, which requires regular evaluation to establish gaps for remediation. In this paper, the aim was to establish current malware trends, comparative weaknesses and strengths of existing malware defenses, the identification of research gaps and a proposal of future directions to malware defense. We adopted a scoping review with empirical case studies using data from extant literature and industrial sources for the study. The results revealed that, current malware are targeted, unknown, persistent and stealth and are increasing in volumes, variety and complexity. Attackers adopt innovative modes of transmission to spread malware from one network to another and use both anti-static and advanced forms of obfuscation to evade detection. The poor adaptability, learnability, memorability and generalizability of signature-based detection methods such as static, dynamic, hybrid makes ML algorithms the state-of-art, but they also show instability in classification, poor and redundant features, class imbalance and the associated "accuracy paradox", and poor resilience to detecting previously unknown malware. Additionally, user and organizational vulnerabilities also exacerbates the defense challenge. The paper concluded that with the increasing sophistication in malware, ensuring holistic malware defense requires novel techniques that addresses these gaps. This implies that, current research should refocus on providing hybrid defense approaches that are not only technical in nature but also non-technical leading to the provision of improved holistic malware defense.

show abstract

Identification of the Important Parameters for Ranking of Open-Source Home Automation Platforms for IoT Management

Borissova

Danev

Garvanov

et al. 2021

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A survey and taxonomy of program analysis for IoT platforms

Cited by 7 publications

References 55 publications

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

An Evaluation of Current Malware Trends and Defense Techniques: A Scoping Review with Empirical Case Studies

Identification of the Important Parameters for Ranking of Open-Source Home Automation Platforms for IoT Management

Contact Info

Product

Resources

About