* update 2023-12-15 06:16:51

arXiv_db/Malware/2023.md

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 </details>
 
+<details>
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+<summary>2023-12-13 12:36:03 - Security aspects in Smart Meters: Analysis and Prevention</summary>
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+- *Rebeca P. Díaz Redondo, Ana Fernández Vilas, Gabriel Fernández dos Reis*
+
+- `2312.08101v1` - [abs](http://arxiv.org/abs/2312.08101v1) - [pdf](http://arxiv.org/pdf/2312.08101v1)
+
+> Smart meters are of the basic elements in the so-called Smart Grid. These devices, connected to the Internet, keep bidirectional communication with other devices in the Smart Grid structure to allow remote readings and maintenance. As any other device connected to a network, smart meters become vulnerable to attacks with different purposes, like stealing data or altering readings. Nowadays, it is becoming more and more popular to buy and plug-and-play smart meters, additionally to those installed by the energy providers, to directly monitor the energy consumption at home. This option inherently entails security risks that are under the responsibility of householders. In this paper, we focus on an open solution based on Smartpi 2.0 devices with two purposes. On the one hand, we propose a network configuration and different data flows to exchange data (energy readings) in the home. These flows are designed to support collaborative among the devices in order to prevent external attacks and attempts of corrupting the data. On the other hand, we check the vulnerability by performing two kind of attacks (denial of service and stealing and changing data by using a malware). We conclude that, as expected, these devices are vulnerable to these attacks, but we provide mechanisms to detect both of them and to solve, by applying cooperation techniques
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+</details>
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+<details>
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+<summary>2023-12-13 17:39:44 - Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4</summary>
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+- *Pei Yan, Shunquan Tan, Miaohui Wang, Jiwu Huang*
+
+- `2312.08317v1` - [abs](http://arxiv.org/abs/2312.08317v1) - [pdf](http://arxiv.org/pdf/2312.08317v1)
+
+> Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware, thereby preventing them from invading computers. As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods. Though there have been numerous deep learning models for malware detection based on API sequences, the quality of API call representations produced by those models is limited. These models cannot generate representations for unknown API calls, which weakens both the detection performance and the generalization. Further, the concept drift phenomenon of API calls is prominent. To tackle these issues, we introduce a prompt engineering-assisted malware dynamic analysis using GPT-4. In this method, GPT-4 is employed to create explanatory text for each API call within the API sequence. Afterward, the pre-trained language model BERT is used to obtain the representation of the text, from which we derive the representation of the API sequence. Theoretically, this proposed method is capable of generating representations for all API calls, excluding the necessity for dataset training during the generation process. Utilizing the representation, a CNN-based detection model is designed to extract the feature. We adopt five benchmark datasets to validate the performance of the proposed model. The experimental results reveal that the proposed detection algorithm performs better than the state-of-the-art method (TextCNN). Specifically, in cross-database experiments and few-shot learning experiments, the proposed model achieves excellent detection performance and almost a 100% recall rate for malware, verifying its superior generalization performance. The code is available at: github.com/yan-scnu/Prompted_Dynamic_Detection.
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+</details>
+