* update 2024-02-01 06:17:04

arXiv_db/Malware/2024.md

@@ -198,3 +198,27 @@
 
 </details>
 
+<details>
+
+<summary>2024-01-29 19:58:34 - Unveiling Human Factors and Message Attributes in a Smishing Study</summary>
+
+- *Daniel Timko, Daniel Hernandez Castillo, Muhammad Lutfor Rahman*
+
+- `2311.06911v2` - [abs](http://arxiv.org/abs/2311.06911v2) - [pdf](http://arxiv.org/pdf/2311.06911v2)
+
+> With the booming popularity of smartphones, threats related to these devices are increasingly on the rise. Smishing, a combination of SMS (Short Message Service) and phishing has emerged as a treacherous cyber threat used by malicious actors to deceive users, aiming to steal sensitive information, money or install malware on their mobile devices. Despite the increase in smishing attacks in recent years, there are very few studies aimed at understanding the factors that contribute to a user's ability to differentiate real from fake messages. To address this gap in knowledge, we have conducted an online survey on smishing detection with 214 participants. In this study, we presented them with 16 SMS screenshots and evaluated how different factors affect their decision making process in smishing detection. Next, we conducted a follow-up survey to garner information on the participants' security attitudes, behavior and knowledge. Our results highlighted that attention and security behavioral scores had a significant impact on participants' accuracy in identifying smishing messages. Interestingly, we found that participants had more difficulty identifying real messages from fake ones, with an accuracy of 65.6% with fake messages and 44.6% with real messages. Our study is crucial in developing proactive strategies to encounter and mitigate smishing attacks. By understanding what factors influence smishing detection, we aim to bolster users' resilience against such threats and create a safer digital environment for all.
+
+</details>
+
+<details>
+
+<summary>2024-01-30 13:10:33 - ActDroid: An active learning framework for Android malware detection</summary>
+
+- *Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A Lones*
+
+- `2401.16982v1` - [abs](http://arxiv.org/abs/2401.16982v1) - [pdf](http://arxiv.org/pdf/2401.16982v1)
+
+> The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android malware detection as a streaming data problem and explore the use of active online learning as a means of mitigating the problem of labelling applications in a timely and cost-effective manner. Our resulting framework achieves accuracies of up to 96\%, requires as little of 24\% of the training data to be labelled, and compensates for concept drift that occurs between the release and labelling of an application. We also consider the broader practicalities of online learning within Android malware detection, and systematically explore the trade-offs between using different static, dynamic and hybrid feature sets to classify malware.
+
+</details>
+