Bader Rasheed¹, Adil Khan¹, S. M. Ahsan Kazmi², Rasheed Hussain², Md. Jalil Piran^3,*, Doug Young Suh⁴

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 921-939, 2021, DOI:10.32604/cmc.2021.015452

Abstract Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes. Recent researches have investigated the role of machine learning (ML) models to detect malicious URLs. By using ML algorithms, first, the features of URLs are extracted, and then different ML models are trained. The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL. Therefore, deep learning (DL) models are used to solve these issues since they are able to perform featureless detection. Furthermore, DL models give better accuracy and generalization to newly… More >

Zhaoquan Gu¹, Yu Su¹, Chenwei Liu¹, Yinyu Lyu¹, Yunxiang Jian¹, Hao Li², Zhen Cao³, Le Wang^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1437-1452, 2020, DOI:10.32604/cmc.2020.011834

Abstract The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper,… More >

Zhaoquan Gu¹, Yinyin Cai¹, Sheng Wang¹, Mohan Li^{1, *}, Jing Qiu¹, Shen Su¹, Xiaojiang Du¹, Zhihong Tian¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739

Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks against content-based filtering JRS. We… More >