* update 2024-01-02 06:17:06

arXiv_db/Malware/2023.md

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 </details>
 
+<details>
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+<summary>2023-12-28 20:48:16 - Can you See me? On the Visibility of NOPs against Android Malware Detectors</summary>
+
+- *Diego Soi, Davide Maiorca, Giorgio Giacinto, Harel Berger*
+
+- `2312.17356v1` - [abs](http://arxiv.org/abs/2312.17356v1) - [pdf](http://arxiv.org/pdf/2312.17356v1)
+
+> Android malware still represents the most significant threat to mobile systems. While Machine Learning systems are increasingly used to identify these threats, past studies have revealed that attackers can bypass these detection mechanisms by making subtle changes to Android applications, such as adding specific API calls. These modifications are often referred to as No OPerations (NOP), which ideally should not alter the semantics of the program. However, many NOPs can be spotted and eliminated by refining the app analysis process. This paper proposes a visibility metric that assesses the difficulty in spotting NOPs and similar non-operational codes. We tested our metric on a state-of-the-art, opcode-based deep learning system for Android malware detection. We implemented attacks on the feature and problem spaces and calculated their visibility according to our metric. The attained results show an intriguing trade-off between evasion efficacy and detectability: our metric can be valuable to ensure the real effectiveness of an adversarial attack, also serving as a useful aid to develop better defenses.
+
+</details>
+
+<details>
+
+<summary>2023-12-29 17:02:54 - Malware Detection in IOT Systems Using Machine Learning Techniques</summary>
+
+- *Ali Mehrban, Pegah Ahadian*
+
+- `2312.17683v1` - [abs](http://arxiv.org/abs/2312.17683v1) - [pdf](http://arxiv.org/pdf/2312.17683v1)
+
+> Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.
+
+</details>
+