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15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Almasalmeh, Nuha Saidi, Firas Trabelsi, Zouheir United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates Univ Manouba Natl Sch Comp Sci Manouba Tunisia

ISBN: (纸本)9781538677476

The proliferation of cyber-attacks brings up an urgent need to develop sophisticated detection tools. Some of these tools are based on algorithms inspired from the Human Immune System (HIS). The dendritic cell algorithm (DCA) is one of such HIS inspired methods, which is based on the Danger model. In the current study, two kinds of DCA algorithms (deterministic and classical DCA) are analyzed in order to detect DoS attacks. Moreover, this paper discusses the enhancement of the DCA algorithm to cover the detection of malicious TCP port scanning. By using different use-cases, the evaluation and results are accumulated to show the efficiency of the DCA algorithm used in the Port scanning detection.

关键词： Artificial immune systems dendritic cell algorithm denial of service intrusion detection port scanning

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The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using dendritic cell algorithm 5

The Significant Effect of Feature Selection Methods in Spam ...

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5th International Conference on Information and Communication Technology (ICoIC7)

作者： Zainal, Kamahazira Jali, Mohd Zalisham Univ Sains Islam Malaysia Fac Sci & Technol Nilai N Sembilan Malaysia

ISBN: (纸本)9781509049127

The vast amount of online documentation and the thriving of Internet especially mobile technology have caused a crucial demand to handle and organize unstructured data appropriately. An information retrieval or even knowledge discovery can be enhanced when a proper and structured data are available. This paper studies empirically the effect of pre-selected term weighting schemes, namely as Term Frequency (TF), Information Gain Ratio (IG Ratio) and Chi-Square (CHI2) in the assessment of a threat's impact loss. This feature selection method then further fed in conjunction with the dendritic cell algorithm (DCA) as the classifier to measure the risk concentration of a spam message. The final outcome of this research is very much expected to be able in assisting people to make a decision once they knew the possible impact caused by a particular spam. The findings showed that TF is the best feature selection methods and well suited to be demonstrated together with the DCA, resulted with high accuracy risk classification rate.

关键词： term weighting schemes feature selection methods dendritic cell algorithm spam severity assessment spam risk concentration

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Detecting Denial of Service Attacks using a Combination of dendritic cell algorithm and the Negative Selection algorithm 2

Detecting Denial of Service Attacks using a Combination of D...

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2nd IEEE International Conference on Smart Cloud (SmartCloud)

作者： Igbe, Obinna Ajayi, Oluwaseyi Saadawi, Tarek CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781538636848

As one of the most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on computing systems and networks. This paper presents a system for detecting denial of service (DoS) attacks in a network using a combination of the dendritic cell algorithm (DCA) and the negative selection algorithm (NSA). The proposed system classifies incoming network traffic into either of two classes: "normal" or "DoS attack." Experimentally, our approach follows a majority voting technique by creating multiple instances of the DCA and the NSA algorithm and assigning weights to their respective output. The effectiveness of our proposed detection system is evaluated using an in-house generated signal dataset. Our results show that our system is very effective in detecting DoS/DDoS attacks with very high accuracy. Analysis of the proposed DoS detection system is also presented.

关键词： DoS Artificial Immune System Negative Selection algorithm dendritic cell algorithm IDS

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A scalable and distributed dendritic cell algorithm for big data classification

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100432-000页

作者： Dagdia, Zaineb Chelly Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales Inst Super Gest Tunis LARODEC Tunis Tunisia

In the era of big data, scaling evolution up to large-scale data sets is a very interesting and challenging task. The application of standard biological systems in such data sets is not straightforward. Therefore, a new class of scalable biological systems that embraces the huge storage and processing capacity of distributed platforms is required. In this work, we focus on the dendritic cell algorithm (DCA), a bio-inspired classifier, and its limitation when coping with very large data sets. To overcome this limitation, we propose a novel distributed DCA version for data classification based on the MapReduce framework to distribute the functioning of this algorithm through a cluster of computing elements. Our experimental results show that our proposed distributed solution is suitable to enhance the performance of the DCA enabling the algorithm to be applied over big data classification problems.

关键词： dendritic cell algorithm Big data Distributed processing

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Multiresolution dendritic cell algorithm for network detection

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PEERJ COMPUTER SCIENCE 2021年 7卷 e749页

作者： Limon-Cantu, David Alarcon-Aquino, Vicente Univ Americas Puebla Dept Comp Elect & Mech Cholula Mexico

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as dendritic cell algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

关键词： Network anomaly detection Intruder detection systems Artificial immune systems Machine learning Wavelet transforms Wavelets dendritic cell algorithm

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Towards Bio-Inspired Anomaly Detection Using the Cursory dendritic cell algorithm

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algorithmS 2022年第1期15卷 1页

作者： Pinto, Carlos Pinto, Rui Goncalves, Gil Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal SYSTEC Res Ctr Syst & Technol Rua Dr Roberto Frias S-NOff i219 P-4200465 Porto Portugal

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems' dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the dendritic cell algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory dendritic cell algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work's contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms,

关键词： Artificial Immune Systems dendritic cell algorithm Cyber-Physical Production Systems anomaly detection fault detection Industry 4 0 smart manufacturing systems

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Principle and Application of dendritic cell algorithm for Intrusion Detection

Principle and Application of Dendritic Cell Algorithm for In...

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2011 3rd International Conference on Signal Processing Systems(ICSPS 2011)

作者： Xufei Zheng Yonghui Fang College of Computer Science Sichuan University School of Electronic and Information Engineering Southwest University

ISBN: (纸本)9781457701726

The dendritic cell algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. This paper describes biological mechanisms of dendritic cells, presents the principle of DCA and its development, introduces the applications of DCA in intrusion detection field, assesses the performance of DCA with other similar methods based on standard KDD 99 data-set, and points out some problems of DCA.

关键词： Artificial immune system Danger theory dendritic cell algorithm Intrusion detection

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Fault Diagnosis Research of Rotating Machinery Based on dendritic cell algorithm

Fault Diagnosis Research of Rotating Machinery Based on Dend...

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IEEE International Conference on Information and Automation

作者： Delong Cui Qinghua Zhang Jianbin Xiong Qinxue Li Mei Liu College of Computer and Electronic Information Guangdong University of Petrochemical Technology

ISBN: (纸本)9781467391054

Rotating machinery play an important role in modern industry. Ensuring security and reliability of manufacture equipments has been receiving more and more recognition. This paper proposes an innovative techniques for rotating machinery fault diagnosis. The fault diagnosis system is comprised of clearly defined separate submodels including antigen submodel, memory submodel, DC submodel, analyse submodel and diagnositic submodel etc. Based on the system model, a novel rotating machinery fault diagnosis scheme based on dendritic cell algorithm (DCA) and dimensionless parameter is proposed in this paper. To demonstrate our method, we apply our method to the real test bed of concurrent fault diagnosis for rotating machinery. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for rotating machinery and has high potential applications in real project.

关键词： Rotating machinery Fault diagnosis dendritic cell algorithm Dimensionless parameter Artificial immune

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A New Cryptojacking Malware Classifier Model Based on dendritic cell algorithm 2019

A New Cryptojacking Malware Classifier Model Based on Dendri...

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Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Azuan Ahmad Wan Shafiuddin Mohd Nazri Kama Madihah Mohd Saudi Islamic Science Institute Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Zainudin CyberSecurity Malaysia Seri Kembangan Selangor Malaysia Saiful Adli Bin Ismail Fakulti Teknologi dan Informatik Razak Universiti Teknologi Malaysia

ISBN: (纸本)9781450376259

A new threat known as "cryptojacking" has entered the picture where cryptojacking malware is the future trend for cyber criminals, who infect victim's device, install cryptojacking malware, and use the stolen resources for crytocurrency mining. Worse comes to worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. IoT devices are vulnerable to attacks because of their simple configuration, unpatched vulnerability and weak passwords. IoT devices also prone to be poorly monitored because of their nature. There is lack of studies that provide in depth analysis on cryptojacking malware classification using machine learning approach where the current research mostly focused on manual analysis of web-based cryptojacking attacks. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on machine learning technique that may fit in a low processing capability environment such as IoT and Cyber Physical Systems (CPS). This paper aim to disscuss a new approach based on dendritic cell algorithm in order to provide a lightweight cryptojacking classifier model. The output of this paper will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries.

关键词： Artificial Intelligence dendritic cell algorithm Malware Cryptojacking Cyber Security

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A dendritic cell algorithm for Real-Time Anomaly Detection

A Dendritic Cell Algorithm for Real-Time Anomaly Detection

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2012 IEEE International Conference on Computer Science and Automation Engineering（CSAE 2012）

作者： Song Yuan 1,2 ,Qi-juan Chen 1 1.College of Power and Mechanical Engineering Wuhan University Wuhan,China 2.College of Computer Science and Technology Wuhan University of Science and Technology Wuhan,China

The analysis process of the classical dendritic cell algorithm(DCA)is performed offline,which has constrained its application in the area of anomaly *** order to continuously detect abnormal behaviors as soon as they occur, a real-time analysis algorithm is proposed,when an antigen has been presented by sufficient dendritic cells,it will be immediately output and assessed,thus the purpose of real-time or near-to real-time analysis can be *** assessments will reduce the influence of the errors,and the antigen and signal pool of temporal correlation will eliminate the mutual interference of the antigens and signals,which are far *** results of the experiments show that the real- time analysis algorithm proposed has the considerable detection accuracy.

关键词： artificial immune danger theory dendritic cell algorithm anomaly detection real-time analysis

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