IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

Venkatasubramanian, Madumitha; Lashkari, Arash Habibi; Hakak, Saqib

doi:10.1109/access.2023.3235389

Cited by 41 publications

(7 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… The length of the matrix LM must be at least greater than or equal to the length of the Cover-Media LCM  The length of the selected key LK must equal the length of the Message LMSG  The length of the Cover-Media LCM must be greater than length of the message LMSG: From Eq. ( 1), (2), and (3) we can conclude:…”

Section: A Matrix Selection Phasementioning

confidence: 78%

“…This transformation is largely fueled by the seamless integration of physical devices with digital networks, enabling them to communicate, analyze, and act upon data with unprecedented efficiency and scale [24]. However, this rapidly expanding network of interconnected devices presents substantial security challenges, particularly in the realm of data privacy and network integrity [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secure IoT Seed-based Matrix Key Generator

AINE,

LEGHRIS

2024

IJACSA

View full text Add to dashboard Cite

The rapid evolution of the Internet of Things (IoT) has significantly transformed various aspects of both personal and professional spheres, offering innovative solutions in fields from home automation to industrial manufacturing. This progression is driven by the integration of physical devices with digital networks, facilitating efficient communication and data processing. However, such advancements bring forth critical security challenges, especially regarding data privacy and network integrity. Conventional cryptographic methods often fall short in addressing the unique requirements of IoT environments, such as limited device computational power and the need for efficient energy consumption. This paper introduces a novel approach to IoT security, inspired by the principles of steganographythe art of concealing information within other non-secret data. This method enhances security by embedding secret information within the payload or communication protocols, aligning with the low-power and minimal processing capabilities of IoT devices. We propose a steganographic key generation algorithm, adapted from the Diffie-Hellman key exchange model, tailored for IoT. This approach eliminates the need for explicit parameter exchange, thereby reducing vulnerability to key interception and unauthorized access, prevalent in IoT networks. The algorithm utilizes a pre-shared 2D matrix and a synchronized seed-based approach for covert communication without explicit data exchange. Furthermore, we have rigorously tested our algorithm using the NIST Statistical Test Suite (STS), comparing its execution time with other algorithms. The results underscore our algorithm's superior performance and suitability for IoT applications, highlighting its potential to secure IoT networks effectively without compromising on efficiency and device resource constraints. This paper presents the design, implementation, and potential implications of this algorithm for enhancing IoT security, ensuring the full realization of IoT benefits without compromising user security and privacy.

show abstract

Section: A Matrix Selection Phasementioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Secure IoT Seed-based Matrix Key Generator

AINE,

LEGHRIS

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…It allows models to be trained across multiple edge devices or servers while keeping data localized [588]. In agriculture, where data privacy is crucial, this technique enables collaborative model training without centralized data storage [589]. Farmers can contribute to a global model without sharing sensitive data, improving model accuracy for various agricultural tasks.…”

Section: ) Federated Learning (Fl)mentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

show abstract

“…The rapid increase in digital interconnectivity and reliance on technology has made cyber-attacks, especially malware, a significant global threat [ 1 , 2 , 3 ]. Malware, or malicious software, has evolved since the early 1970s, represented by various emerging types such as viruses, worms, Trojans, spyware, and ransomware [ 4 , 5 , 6 ]. Ransomware, which holds user data and files for ransom by denying access, gained popularity among attackers when enabling technologies such as Ransomware-as-a-Service (RaaS), internet, cryptography, and hard-to-trace digital currencies emerged [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance

Alqahtani,

Sheldon

2024

Sensors

View full text Add to dashboard Cite

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency–Inverse Document Frequency (TF–IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features’ relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance.

show abstract

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

Cited by 41 publications

References 102 publications

Secure IoT Seed-based Matrix Key Generator

Secure IoT Seed-based Matrix Key Generator

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance

Contact Info

Product

Resources

About