IoT Vulnerabilities and Attacks: SILEX Malware Case Study

Mukhtar, Basem Ibrahim; Elsayed, Mahmoud Said; Jurcut, Anca D.; Azer, Marianne A.

doi:10.3390/sym15111978

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Several factors make IoT devices inherently vulnerable: limited computational power, variations in equipment, software, and communication protocols [21,22,23]. This creates a significant gap between the security demands placed on these devices and their actual defense capabilities [24].…”

Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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Background and Motivation:The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Methods: Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. Proposed Approach: This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. Results: The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. Conclusion: Our findings demonstrate the potential of the proposed methods for enhanced attack and intrusion detection. This improved detection capability can be instrumental in safeguarding system integrity, identifying fraudulent activity, and optimizing system performance in IoT networks.

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Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…From compromising personal data to disrupting critical infrastructure, the threats posed by undetected malware can lead to significant financial, privacy, and security ramifications. As digital technologies continue to advance and integrate more deeply into various aspects of life, ensuring robust defense mechanisms against such covert cyber threats becomes not just a technical necessity but a cornerstone for maintaining trust and integrity in the digital landscape (Beaman et al 2021;Mukhtar et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Hossain,

Islam

2024

Cybersecurity

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In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.

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Long Term Trends in Security Threats and an Approach for Integrating Them into System Architecture and Design

Rosser

2024

INCOSE International Symp

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Security is an important consideration in today's systems, and it promises to be both relevant and challenging in the future. Our design and development processes are evolving to incorporate modern methods such as agile development and model‐based engineering with the intent to provide flexible and resilient capabilities to our customers quickly and with high quality. As we continue to enhance our practices to achieve these aspirations, security capabilities both at initial deployment and throughout the system lifespan are an important consideration. A literature review of security concerns in general and challenges across a range of domains including civil aviation, smart cities, eLearning, finance and defense reveals a set of recurring long‐term trends. These trends suggest challenges to security capabilities in the future. In this paper, we summarize and analyze these trends and propose an approach for addressing them in architecture and design activities.

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IoT Vulnerabilities and Attacks: SILEX Malware Case Study

Cited by 7 publications

References 28 publications

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Long Term Trends in Security Threats and an Approach for Integrating Them into System Architecture and Design

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