* update 2023-11-22 06:16:55

arXiv_db/Malware/2023.md

@@ -3274,3 +3274,27 @@
 
 </details>
 
+<details>
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+<summary>2023-11-20 08:43:09 - Machine learning-based malware detection for IoT devices using control-flow data</summary>
+
+- *Gergely Hevesi*
+
+- `2311.11605v1` - [abs](http://arxiv.org/abs/2311.11605v1) - [pdf](http://arxiv.org/pdf/2311.11605v1)
+
+> Embedded devices are specialised devices designed for one or only a few purposes. They are often part of a larger system, through wired or wireless connection. Those embedded devices that are connected to other computers or embedded systems through the Internet are called Internet of Things (IoT for short) devices.   With their widespread usage and their insufficient protection, these devices are increasingly becoming the target of malware attacks. Companies often cut corners to save manufacturing costs or misconfigure when producing these devices. This can be lack of software updates, ports left open or security defects by design. Although these devices may not be as powerful as a regular computer, their large number makes them suitable candidates for botnets. Other types of IoT devices can even cause health problems since there are even pacemakers connected to the Internet. This means, that without sufficient defence, even directed assaults are possible against people.   The goal of this thesis project is to provide better security for these devices with the help of machine learning algorithms and reverse engineering tools. Specifically, I study the applicability of control-flow related data of executables for malware detection. I present a malware detection method with two phases. The first phase extracts control-flow related data using static binary analysis. The second phase classifies binary executables as either malicious or benign using a neural network model. I train the model using a dataset of malicious and benign ARM applications.
+
+</details>
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+<details>
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+<summary>2023-11-20 12:21:35 - Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review</summary>
+
+- *Oghenetejiri Okporokpo, Funminiyi Olajide, Nemitari Ajienka, Xiaoqi Ma*
+
+- `2311.11705v1` - [abs](http://arxiv.org/abs/2311.11705v1) - [pdf](http://arxiv.org/pdf/2311.11705v1)
+
+> The continuous rise in the adoption of emerging technologies such as Internet of Things (IoT) by businesses has brought unprecedented opportunities for innovation and growth. However, due to the distinct characteristics of these emerging IoT technologies like real-time data processing, Self-configuration, interoperability, and scalability, they have also introduced some unique cybersecurity challenges, such as malware attacks, advanced persistent threats (APTs), DoS /DDoS (Denial of Service & Distributed Denial of Service attacks) and insider threats. As a result of these challenges, there is an increased need for improved cybersecurity approaches and efficient management solutions to ensure the privacy and security of communication within IoT networks. One proposed security approach is the utilization of trust-based systems and is the focus of this study. This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT. A total of 23 articles were identified that satisfy the review criteria. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats and grouped them into three major categories, namely: Observation-Based, Knowledge-Based & Cluster-Based systems. Finally, several open issues were highlighted, and future research directions presented.
+
+</details>
+