Mohammad Atrees^*, Ashraf Ahmad, Firas Alghanim

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1405-1422, 2022, DOI:10.32604/iasc.2022.020229

Abstract A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage… More >

Hani Mohammed Alshahrani^*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 739-766, 2022, DOI:10.32604/cmc.2022.019623

Abstract One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing private information. As reported by one of the recent studies Android malware… More >

Salam Al-E’mari¹, Mohammed Anbar^1,*, Yousef Sanjalawe^1,2, Selvakumar Manickam¹, Iznan Hasbullah¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 87-112, 2022, DOI:10.32604/csse.2022.017941

Abstract Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter… More >

Sobia Wassan¹, Chen Xi^1,*, Nz Jhanjhi², Hassan Raza³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 187-199, 2021, DOI:10.32604/iasc.2021.015859

Abstract Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments. The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers’ information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites. This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm). The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar… More >

Marwan Albahar^*, Mohammed Thanoon, Monaj Alzilai, Alaa Alrehily, Munirah Alfaar, Maimoona Algamdi, Norah Alassaf

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2181-2202, 2021, DOI:10.32604/cmc.2021.018260

Abstract Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM)… More >

T. Kowsalya¹, R. Ganesh Babu², B. D. Parameshachari³, Anand Nayyar⁴, Raja Majid Mehmood^5,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1447-1465, 2021, DOI:10.32604/cmc.2021.014606

Abstract Lightweight Cryptography (LWC) is widely used to provide integrity, secrecy and authentication for the sensitive applications. However, the LWC is vulnerable to various constraints such as high-power consumption, time consumption, and hardware utilization and susceptible to the malicious attackers. In order to overcome this, a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious attacks. The True Random Number Generator-Pseudo Random Number Generator (TRNG-PRNG) based key generation is proposed to generate the unpredictable keys, being highly difficult to predict by the hackers. Moreover, the hardware utilization of PRESENT architecture is optimized using the Dual port… More >

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 >

Zhengqiu Weng^1,2, Timing Chen^1,*, Tiantian Zhu¹, Hang Dong¹, Dan Zhou¹, Osama Alfarraj³

Computer Systems Science and Engineering, Vol.37, No.1, pp. 105-119, 2021, DOI:10.32604/csse.2021.015074

Abstract Internet traffic encryption is a very common traffic protection method. Most internet traffic is protected by the encryption protocol called transport layer security (TLS). Although traffic encryption can ensure the security of communication, it also enables malware to hide its information and avoid being detected. At present, most of the malicious traffic detection methods are aimed at the unencrypted ones. There are some problems in the detection of encrypted traffic, such as high false positive rate, difficulty in feature extraction, and insufficient practicability. The accuracy and effectiveness of existing methods need to be improved. In this paper, we present TLSmell,… More >

Ganeshan Keerthana^1,*, Panneerselvam Anandan², Nandhagopal Nachimuthu³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 903-917, 2021, DOI:10.32604/cmc.2020.012255

Abstract Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced… More >

Chunlai Du¹, Shenghui Liu¹, Lei Si², Yanhui Guo^{2, *}, Tong Jin¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091

Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More >