Security and Communication Networks
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Acceptance rate10%
Submission to final decision143 days
Acceptance to publication35 days
CiteScore2.600
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Impact Factor-

Exploring the Security Vulnerability in Frequency-Hiding Order-Preserving Encryption

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 Journal profile

Security and Communication Networks provides a prestigious forum for the R&D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.

 Editor spotlight

Chief Editor Dr Roberto Di Pietro is a full professor of cybersecurity at the HBKU College of Science and Engineering, Qatar. His research interests include distributed systems security, cloud security, and wireless security.

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Research Article

Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, attacks against fog servers can take many forms, such as distributed denial of service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of fog computing-based SYN Flood DDoS attacks using an adaptive neuro-fuzzy inference system (ANFIS) and software defined networking (SDN) assistance (FASA). The simulation results show that the FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP-SYN floods and DDoS attacks.

Research Article

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

Objective. Wireless sensor networks, crucial for various applications, face growing security challenges due to the escalating complexity and diversity of attack behaviours. This paper presents an advanced intrusion detection algorithm, leveraging feature-weighted Naive Bayes (NB), to enhance network attack detection accuracy. Methodology. Initially, a feature weighting algorithm is introduced to assign context-based weights to different feature terms. Subsequently, the NB algorithm is enhanced by incorporating Jensen–Shannon (JS) divergence, feature weighting, and inverse category frequency (ICF). Eventually, the improved NB algorithm is integrated into the intrusion detection model, and network event classification results are derived through a series of data processing steps applied to corresponding network traffic data. Results. The effectiveness of the proposed intrusion detection algorithm is evaluated through a comprehensive comparative analysis using the NSL-KDD dataset. Results demonstrate a significant enhancement in the detection accuracy of various attack types, including normal, denial of service (DoS), probe, remote-to-local (R2L), and user-to-root (U2R). Moreover, the proposed algorithm exhibits a lower false alarm rate compared to other algorithms. Conclusion. This paper introduces a wireless network intrusion algorithm that not only ensures improved detection accuracy and rate but also reduces the incidence of false detections. Addressing the evolving threat landscape faced by wireless sensor networks, this contribution represents a valuable advancement in intrusion detection technology.

Research Article

A Postquantum Linkable Ring Signature Scheme from Coding Theory

Linkable ring signatures (LRSs) are ring signatures with the extended property that a verifier can detect whether two messages were signed by the same ring member. LRSs play an important role in many application scenarios such as cryptocurrency and confidential transactions. The first code-based LRS scheme was put forward in 2018. However, this scheme was pointed out to be insecure. In this paper, we put forward a code-based LRS scheme by constructing a new Stern-like interactive protocol and prove that it meets the security requirements of LRSs. We also give the specific parameters and the performance on the platform of our scheme.

Research Article

MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

The explosive growth of web-based technology has led to an increase in sophisticated and complex attacks that target web applications. To protect against this growing threat, a reliable web attack detection methodology is essential. This research aims to provide a method that can detect web attacks accurately. A character-level multichannel multilayer dilated convolutional neural network (MC-MLDCNN) is proposed to identify web attacks accurately. The model receives the full text of HTTP requests as inputs. Character-level embedding is applied to embed HTTP requests to the model. Therefore, feature extraction is carried out automatically by the model, and no additional effort is required. This approach significantly simplifies the preprocessing phase. The methodology consists of multichannel dilated convolutional neural network blocks with various kernel sizes. Each channel involves several layers with exponentially increasing dilation sizes. Through the integration of multichannel and multilayer dilated convolutional neural networks, the model can efficiently capture the temporal relation and dependence of character granularity of HTTP requests at different scales and levels. As a result, the structure enables the model to easily capture dependencies over extended and long sequences of HTTP requests and consequently identify attacks accurately. The outcomes of the experiments carried out on the CSIC 2010 dataset show that the proposed model outperforms several state-of-the-art deep learning-based models in the literature and some traditional deep learning models by identifying web attacks with a precision score of 99.65%, a recall score of 98.80%, an score of 99.22%, and an accuracy score of 99.36%. A useful web attack detection system must be able to balance accurate attack identification with minimizing false positives (identifying normal requests as attacks). The success of the model in recognizing normal requests is further evaluated to guarantee increased security without sacrificing web applications’ usability and availability.

Research Article

Stackelberg Security Game for Optimizing Cybersecurity Decisions in Cloud Computing

As it is difficult to cover all cybersecurity threats, an optimal defense strategy is one of the focal issues in cloud computing due to its dynamic abstraction and scalability. On this basis, Stackelberg security games (SSG) have received significant attention for their better deployment of limited security. To deal with uncertainty and incomplete information, we introduce a modified quantal response (Mod-QR) approach that incorporates bounded rationality and preference into the decision-making process. Formally, this can be done by using the quantal response equilibrium (QRE) framework to find a trade-off between the effectiveness and operating costs of cloud computing. In this case, the most effective countermeasures to defend the cloud can be viewed as a mixed strategy in which all the actions of the defender are played with a nonzero probability. This framework has been evaluated using an experimental study on MATLAB optimization toolbox to understand the behavioral aspects of cybersecurity actors and then to proactively protect cloud computing.

Research Article

An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.

Security and Communication Networks
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate10%
Submission to final decision143 days
Acceptance to publication35 days
CiteScore2.600
Journal Citation Indicator-
Impact Factor-
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