Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1457-1490, 2024, DOI:10.32604/cmc.2024.051996

Abstract This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of… More >

Asad Raza^1,*, Shahzad Memon¹, Muhammad Ali Nizamani¹, Mahmood Hussain Shah²

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779

Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

Zhihui Song, Jinchen Xu, Kewei Li, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4573-4601, 2024, DOI:10.32604/cmc.2024.049310

Abstract Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST)… More >

Chunhui Li^1,*, Hua Jiang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431

Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

Widad Elbakri¹, Maheyzah Md. Siraj^1,*, Bander Ali Saleh Al-rimy¹, Sultan Noman Qasem², Tawfik Al-Hadhrami³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105

Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse… More >

Yutao Hu¹, Yuntao Zhao^1,*, Yongxin Feng², Xiangyu Ma¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 509-530, 2024, DOI:10.32604/cmc.2024.042216

Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on… More >

Bing Liu¹, Zhe Zhang¹, Shengrong Hu², Song Sun^3,*, Dapeng Liu⁴, Zhenyu Qiu⁵

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4049-4069, 2024, DOI:10.32604/cmc.2024.048099

Abstract Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities… More >

Ali Hamid Farea^1,*, Omar H. Alhazmi¹, Kerem Kucuk²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1525-1545, 2024, DOI:10.32604/cmc.2023.045794

Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work.… More >