已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

A malware detection model based on imbalanced heterogeneous graph embeddings

计算机科学 恶意软件 数据挖掘 分类器(UML) 图形 人工智能 机器学习 Android(操作系统) 理论计算机科学 计算机安全 操作系统
作者
Tun Li,Ya Wen Luo,Xin Wan,Qian Li,Qilie Liu,Rong Wang,Chaolong Jia,Yunpeng Xiao
出处
期刊:Expert Systems With Applications [Elsevier]
卷期号:246: 123109-123109 被引量:8
标识
DOI:10.1016/j.eswa.2023.123109
摘要

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
fly发布了新的文献求助10
3秒前
7秒前
lll完成签到,获得积分10
7秒前
7秒前
songyuuuuu完成签到,获得积分20
9秒前
11秒前
丁丁发布了新的文献求助10
11秒前
12秒前
hh发布了新的文献求助10
12秒前
songyuuuuu发布了新的文献求助30
14秒前
14秒前
JamesPei应助fly采纳,获得10
16秒前
16秒前
NNN7完成签到,获得积分10
16秒前
朴实的宝莹完成签到 ,获得积分10
17秒前
大模型应助jiaobuyimi采纳,获得10
18秒前
22秒前
23秒前
Bob发布了新的文献求助10
27秒前
滴滴滴完成签到 ,获得积分10
28秒前
丘比特应助vio_107采纳,获得10
33秒前
英俊的铭应助Bob采纳,获得10
34秒前
Lucas应助科研小兵兵采纳,获得10
37秒前
AURORA发布了新的文献求助10
39秒前
39秒前
彭于晏应助小伟采纳,获得10
40秒前
zhi完成签到,获得积分10
43秒前
jiaobuyimi发布了新的文献求助10
45秒前
WXHL发布了新的文献求助20
46秒前
跳跃安波完成签到 ,获得积分10
47秒前
48秒前
xrang完成签到 ,获得积分10
52秒前
奋豆完成签到,获得积分10
53秒前
54秒前
陶醉的雁风完成签到,获得积分10
56秒前
今天要睡觉完成签到,获得积分10
57秒前
57秒前
独钓寒江雪完成签到 ,获得积分10
57秒前
59秒前
雪落发布了新的文献求助10
59秒前
高分求助中
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
Very-high-order BVD Schemes Using β-variable THINC Method 830
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 800
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3248577
求助须知:如何正确求助?哪些是违规求助? 2892029
关于积分的说明 8269412
捐赠科研通 2560089
什么是DOI,文献DOI怎么找? 1388851
科研通“疑难数据库(出版商)”最低求助积分说明 650913
邀请新用户注册赠送积分活动 627798