dsmishsms -一个检测诈骗短信的系统。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sandhya Mishra, Devpriya Soni
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引用次数: 16

摘要

随着智能家居、智能城市、智能万物的诞生,智能手机作为一个领域出现了惊人的增长和发展。这些设备成为人类日常活动的一部分。这种影响和增长使得这些设备比台式机或笔记本电脑等其他设备更容易受到攻击。短信或SMS(短文本消息)是智能手机的一部分,攻击者通过它来攻击用户。短信钓鱼(SMS Phishing)是一种通过短信攻击智能手机用户的攻击。虽然短信诈骗是网络钓鱼的一种,但它与网络钓鱼在很多方面都有所不同,比如短信中可用的信息量、攻击策略等。因此,在攻击者共享的信息量最小的情况下,检测欺骗是一个挑战。在smishing的例子中,我们有短文本信息,通常是简短的或象征性的形式。单条短信包含的与欺骗相关的特征很少,而且短信中包含缩写和习语,这给欺骗检测增加了难度。欺骗检测是一个挑战,不仅因为特征的限制,而且由于真实欺骗数据集的稀缺性。为了区分垃圾邮件和欺骗消息,我们正在评估消息中URL(统一资源定位符)的合法性。我们从文本消息中提取了五个最有效的特征,使机器学习能够使用有限数量的特征进行分类。本文提出了一个包含两个阶段的短信欺骗检测模型:域检测阶段和短信分类阶段。我们检查了短信中URL的真实性,这是短信网络钓鱼检测的关键部分。在我们的系统中,域名检查阶段检查URL的真实性。短信分类阶段对短信的文本内容进行检测,提取出一些有效的特征。最后,采用反向传播算法对消息进行分类,并与三种传统分类器进行比较。该系统的原型已经开发并使用SMS数据集进行了评估。评价结果表明,该方法检测诈骗信息的准确率为97.93%,是一种非常有效的检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DSmishSMS-A System to Detect Smishing SMS.

DSmishSMS-A System to Detect Smishing SMS.

DSmishSMS-A System to Detect Smishing SMS.

DSmishSMS-A System to Detect Smishing SMS.

With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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