* update 2024-04-05 06:15:47

arXiv_db/Malware/2024.md

@@ -5,6 +5,7 @@
 - [2024-01](#2024-01)
 - [2024-02](#2024-02)
 - [2024-03](#2024-03)
+- [2024-04](#2024-04)
 
 ## 2024-01
 
@@ -806,3 +807,30 @@
 
 </details>
 
+
+## 2024-04
+
+<details>
+
+<summary>2024-04-02 22:37:34 - Effective Malware Detection for Embedded Computing Systems with Limited Exposure</summary>
+
+- *Sreenitha Kasarapu, Sanket Shukla, Rakibul Hassan, Avesta Sasan, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao*
+
+- `2404.02344v1` - [abs](http://arxiv.org/abs/2404.02344v1) - [pdf](http://arxiv.org/pdf/2404.02344v1)
+
+> One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being efficient, the existing techniques require a tremendous number of benign and malware samples for training and modeling an efficient malware detector. Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training. To address such concerns, we introduce a code-aware data generation technique that generates multiple mutated samples of the limitedly seen malware by the devices. Loss minimization ensures that the generated samples closely mimic the limitedly seen malware and mitigate the impractical samples. Such developed malware is further incorporated into the training set to formulate the model that can efficiently detect the emerging malware despite having limited exposure. The experimental results demonstrates that the proposed technique achieves an accuracy of 90% in detecting limitedly seen malware, which is approximately 3x more than the accuracy attained by state-of-the-art techniques.
+
+</details>
+
+<details>
+
+<summary>2024-04-03 00:13:23 - Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis</summary>
+
+- *S M Rakib Hasan, Aakar Dhakal*
+
+- `2404.02372v1` - [abs](http://arxiv.org/abs/2404.02372v1) - [pdf](http://arxiv.org/pdf/2404.02372v1)
+
+> In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open-access for reproducibility and future research endeavours. It can be accessed at https://bit.ly/MalMemCode.
+
+</details>
+