J. Pavithra^*, S. Selvakumarasamy

Journal of Cyber Security, Vol.4, No.3, pp. 135-151, 2022, DOI:10.32604/jcs.2022.031889

Abstract Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier “AFC-XG Boost” is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance. At preprocessing stage, the data… More >

Jingwei Hao^*, Senlin Luo, Limin Pan

Computer Systems Science and Engineering, Vol.46, No.1, pp. 719-734, 2023, DOI:10.32604/csse.2023.033923

Abstract The byte stream is widely used in malware detection due to its independence of reverse engineering. However, existing methods based on the byte stream implement an indiscriminate feature extraction strategy, which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes, resulting in byte semantic confusion. To address this issue, an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure, code, and data. The Minhash algorithm, grayscale mapping, and state transition probability statistics… More >

Muhammad Waqar¹, Sabeeh Fareed¹, Ajung Kim^2,*, Saif Ur Rehman Malik³, Muhammad Imran¹, Muhammad Usman Yaseen¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4399-4415, 2023, DOI:10.32604/cmc.2023.032984

Abstract The Android Operating System (AOS) has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things (IoT). Due to the high popularity and reliability of AOS for IoT, it is a target of many cyber-attacks which can cause compromise of privacy, financial loss, data integrity, unauthorized access, denial of services and so on. The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are developed to conceal their malicious… More >

Mohammed Maray¹, Hamed Alqahtani², Saud S. Alotaibi³, Fatma S. Alrayes⁴, Nuha Alshuqayran⁵, Mrim M. Alnfiai⁶, Amal S. Mehanna⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3101-3115, 2023, DOI:10.32604/cmc.2023.032969

Abstract Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten the integrity, secrecy, and accessibility… More >

Lojain Nahhas¹, Marwan Albahar^1,*, Abdullah Alammari², Anca Jurcut³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316

Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious… More >

Hasan H. Al-Khshali^1,*, Muhammad Ilyas²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 153-178, 2023, DOI:10.32604/cmc.2023.032182

Abstract One aspect of cybersecurity, incorporates the study of Portable Executables (PE) files maleficence. Artificial Intelligence (AI) can be employed in such studies, since AI has the ability to discriminate benign from malicious files. In this study, an exclusive set of 29 features was collected from trusted implementations, this set was used as a baseline to analyze the presented work in this research. A Decision Tree (DT) and Neural Network Multi-Layer Perceptron (NN-MLPC) algorithms were utilized during this work. Both algorithms were chosen after testing a few diverse procedures. This work implements a method of subgrouping features to answer questions such… More >

P. Pandi Chandran^1,*, N. Hema Rajini², M. Jeyakarthic³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3349-3364, 2023, DOI:10.32604/iasc.2023.029946

Abstract Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware exist in the PDFs. The… More >

K. Santosh Jhansi^1,2,*, P. Ravi Kiran Varma², Sujata Chakravarty³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms… More >

Sumit Kumar^1,*, S. Indu², Gurjit Singh Walia¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1035-1051, 2023, DOI:10.32604/iasc.2023.024469

Abstract Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification… More >

R Punithavathi¹, K Venkatachalam², Mehedi Masud³, Mohammed A. AlZain⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 559-574, 2022, DOI:10.32604/iasc.2022.024715

Abstract The challenges in providing e-health services with the help of Internet of Medical Things (IoMT) is done by connecting to the smart medical devices. Through IoMT sensor devices/smart devices, physicians share the sensitive information of the patient. However, protecting the patient health care details from malware attack is necessary in this advanced digital scenario. Therefore, it is needed to implement cryptographic algorithm to enhance security, safety, reliability, preventing details from malware attacks and privacy of medical data. Nowadays blockchain has become a prominent technology for storing medical data securely and transmit through IoMT concept. The issues in the existing research… More >