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  • Open Access

    ARTICLE

    Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

    Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Journal of Cyber Security, Vol.4, No.4, pp. 287-298, 2022, DOI:10.32604/jcs.2023.042486

    Abstract Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and… More >

  • Open Access

    ARTICLE

    Intrusion Detection in 5G Cellular Network Using Machine Learning

    Ishtiaque Mahmood1, Tahir Alyas2, Sagheer Abbas3, Tariq Shahzad4, Qaiser Abbas5,6, Khmaies Ouahada7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2439-2453, 2023, DOI:10.32604/csse.2023.033842

    Abstract Attacks on fully integrated servers, apps, and communication networks via the Internet of Things (IoT) are growing exponentially. Sensitive devices’ effectiveness harms end users, increases cyber threats and identity theft, raises costs, and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods. Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security. Following the 1, 2, 3, and 4G cellular networks, the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

    Okba Taouali1,*, Sawcen Bacha2, Khaoula Ben Abdellafou1, Ahamed Aljuhani1, Kamel Zidi3, Rehab Alanazi1, Mohamed Faouzi Harkat4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1593-1609, 2023, DOI:10.32604/csse.2023.039984

    Abstract Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to effectively combat a diverse range… More >

  • Open Access

    ARTICLE

    Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

    Mahmoud Ragab1,2,*, Sultanah M. Alshammari2,3, Abdullah S. Al-Malaise Al-Ghamdi2,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446

    Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult. This study… More >

  • Open Access

    ARTICLE

    An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

    Adil Hussain1, Kashif Naseer Qureshi2,*, Khalid Javeed3, Musaed Alhussein4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2513-2528, 2023, DOI:10.32604/csse.2023.040305

    Abstract Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the existing NIDS systems and different… More >

  • Open Access

    ARTICLE

    Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

    Ammar Almomani1,2,*, Iman Akour3, Ahmed M. Manasrah4,5, Omar Almomani6, Mohammad Alauthman7, Esra’a Abdullah1, Amaal Al Shwait1, Razan Al Sharaa1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2499-2517, 2023, DOI:10.32604/iasc.2023.039687

    Abstract The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest, Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing… More >

  • Open Access

    ARTICLE

    Signature-Based Intrusion Detection System in Wireless 6G IoT Networks

    Mansoor Farooq1,*, Mubashir Hassan Khan2

    Journal on Internet of Things, Vol.4, No.3, pp. 155-168, 2022, DOI:10.32604/jiot.2022.039271

    Abstract An “Intrusion Detection System” (IDS) is a security measure designed to perceive and be aware of unauthorized access or malicious activity on a computer system or network. Signature-based IDSs employ an attack signature database to identify intrusions. This indicates that the system can only identify known attacks and cannot identify brand-new or unidentified assaults. In Wireless 6G IoT networks, signature-based IDSs can be useful to detect a wide range of known attacks such as viruses, worms, and Trojans. However, these networks have specific requirements and constraints, such as the need for real-time detection and low-power operation. To meet these requirements,… More >

  • Open Access

    ARTICLE

    MEM-TET: Improved Triplet Network for Intrusion Detection System

    Weifei Wang1, Jinguo Li1,*, Na Zhao2, Min Liu1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 471-487, 2023, DOI:10.32604/cmc.2023.039733

    Abstract With the advancement of network communication technology, network traffic shows explosive growth. Consequently, network attacks occur frequently. Network intrusion detection systems are still the primary means of detecting attacks. However, two challenges continue to stymie the development of a viable network intrusion detection system: imbalanced training data and new undiscovered attacks. Therefore, this study proposes a unique deep learning-based intrusion detection method. We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data. Then the original data is fed into the triplet network… More >

  • Open Access

    ARTICLE

    XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

    Yuna Han1, Hangbae Chang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463

    Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains… More >

  • Open Access

    ARTICLE

    Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

    Turke Althobaiti1,2, Yousef Sanjalawe3,*, Naeem Ramzan4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 453-469, 2023, DOI:10.32604/csse.2023.039207

    Abstract Flash Crowd attacks are a form of Distributed Denial of Service (DDoS) attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing (CC). Botnets are often used by attackers to perform a wide range of DDoS attacks. With advancements in technology, bots are now able to simulate DDoS attacks as flash crowd events, making them difficult to detect. When it comes to application layer DDoS attacks, the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue. This is mainly because it can imitate… More >

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