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An antigen space density based real-value negative selection algorithm

APPLIED SOFT COMPUTING 2017年 61卷 860-874页

作者： Yang, Tao Chen, Wen Li, Tao Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an "antibody suppression rate" to replace the "expected coverage" as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Detector Subspace Antibody suppression

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An Improved negative selection algorithm Based on Subspace Density Seeking

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IEEE ACCESS 2017年 5卷 12189-12198页

作者： Liu, Zhengjun Li, Tao Yang, Jin Yang, Tao Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Sichuan Peoples R China

negative selection algorithm (NSA) is an important method for generating detectors in artificial immune systems. Traditional NSAs randomly generate detectors in the whole feature space. However, with increasing dimensions, data samples aggregate in some specific subspaces, not uniformly distributed in the whole space. The detectors randomly generated by traditional NSAs cannot exactly fall into these specific subspaces, which results in a low coverage of detectors and a poor performance in a high-dimensional space. To overcome this defect, an improved real NSA based on subspace density seeking (SDS-RNSA) is proposed in this paper. In an SDS-RNSA, a subspace density seeking algorithm is adopted to procure the dense subspace regions of samples. Then, detectors are generated in each subspace region to cover up nonself-region efficiently and improve the performance of the algorithm. During the process of detector generation, the redundancy of candidate detectors is calculated, and the redundant is eliminated to minimize the time expense of the algorithm. Experimental results demonstrate that, compared with the classic NSAs, the SDS-RNSA can significantly improve the detection rate with an approximative false alarm rate and a smaller time expense. At the best case, the detection rate of the SDS-RNSA is increased by 14.7%, while the time expense is decreased by 78.1%.

关键词： negative selection algorithm subspace density seeking artificial immune system anomalym detection detector generation

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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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Enhanced negative selection algorithm for Malicious Node Detection in MANET 9

Enhanced Negative Selection Algorithm for Malicious Node Det...

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9th International Conference on Advanced Computing (ICoAC)

作者： Raja, Kathiroli Natarajan, Indra Anna Univ Dept Comp Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9781538643495

Intrusion detection in MANETS is inevitable as these networks are dynamic, open and no central authority to monitor the nodes' activities. Since the network is open, it allows the node to enter and exit at any point time which provides ease of access to all nodes. Misbehaving nodes utilize these opportunity to enter into the network and cause disruption. This requires building appropriate IDS to monitor the activities of nodes in the network. An Artificial Immune System (AIS) analogous to Human Immune System (HIS) is presented to provide appropriate IDS. The major role of AIS is to classify the samples as self (which are specific to the system) and non-self (which are the foreign body to the system) by means of proposed Enhanced negative selection algorithm (ENSA). The non-self patterns act as a defense mechanism to detect the anomalies caused by invaders. ENSA regards the immune system as a classification system for matching patterns. The proposed ENSA optimizes the detector generation process and performs accurate and precise classification of the network traffic. The ENSA includes two operation detector generation and classification systems. ENSA adapts Particle Swarm Optimization (PSO) technique to enhance the random detector generation to achieve maximum coverage in the non-self space. The classification involves matching the bit strings of the antigen with the generated detectors. The strings which match with the defined detector set are classified as intruders (non-self). The performance result shows that ENSA significantly outperforms other traditional classification algorithms in terms of classification accuracy, detection rate and classification time.

关键词： Artificial immune system Anomaly detection Mobile ad hoc networks negative selection algorithm

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Ransomware Detection Based on V-detector negative selection algorithm

Ransomware Detection Based on V-detector Negative Selection ...

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International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC)

作者： Lu, Tianliang Zhang, Lu Wang, Shunye Gong, Qi Peoples Publ Secur Univ China Inst Informat Technol & Network Secur Beijing Peoples R China

ISBN: (纸本)9781538630167

As a new type of malicious software, ransomw are is one of the biggest security threats in recent years. Inspired by biological immune system, a ransomware detection method based on V-detector negative selection algorithm with mutation optimization is proposed, which is referred to op-RDVD. The behavioral features of ransomware are extracted through dynamic analysis, such as hard disk reading and writing, the document encryption and deletion, etc. Some of benign samples are used to build the self space. The variable-sized detectors are generated both randomly and extracted from ransomware. To improve the ransomware detection accuracy and efficiency, optimize the space distribution of detectors through clone and mutation, achieving maximized coverage of non-self space and minimized overlapping among detectors. The experimental results show that our algorithm has better detection ability than that of the previous method.

关键词： ransomware detection artificial immune system V-detector negative selection algorithm

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A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 93-105页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

The traditional negative selection algorithm (NSA) lacks online adaptive learning ability, and this restricts its application range. A new NSA, boundary-fixed negative selection algorithm with online adaptive learning under small samples (OALFB-NSA), is proposed in this paper. Boundary-fixed negative selection algorithm (FB-NSA) generates a layer of detectors, which are around the self space. These detectors are only related to the training samples, and have nothing to do with the training times. OALFB-NSA detectors can adapt themselves to real-time variety of self space during the testing stage. Experimental comparison among FB-NSA, V-detector and other anomaly detection algorithms on Iris data sets and biomedical dataset shows that the FB-NSA can obtain the higher detection rate and lower false alarm rate in most cases. The experimental comparison between OALFB-NSA, interface detector with online adaptive learning under small training samples (OALI-detector) and V-detector on Iris data sets shows that when overfitting does not occur, the OALFB-NSA can obtain the higher detection rate and lower false alarm rate, even if only one self sample is used for training. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Online adaptive learning

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Application of negative selection algorithm (NSA) for test data generation of path testing

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APPLIED SOFT COMPUTING 2016年 49卷 1118-1128页

作者： Mohi-Aldeen, Shayma Mustafa Mohamad, Radziah Deris, Safaai Univ Teknol Malaysia Fac Comp Utm Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Teknol Kreatif Dan Warisan Kelantan 16100 Malaysia Univ Mosul Coll Comp Sci & Math Mosul 41002 Iraq

Path testing is one of the areas covered in structural testing. In this process, it is a key challenge to search for a set of test data in the whole search space to satisfy path coverage. Thus, finding an efficient method for generating test data automatically is a key issue in software testing. This paper proposed a method based on negative selection algorithm (NSA) for generating test data to satisfy the path coverage criterion. The results show that NSA could reduce the number of test data generated and improve the coverage percentage, as well as enhance the efficiency of the test data generation process. To evaluate the performance of the method, results from the proposed method were compared with random testing and a previous work that used Genetic algorithm and Ant Colony Optimization. The results demonstrate that NSA outperforms other methods in reducing the number of test data that cover all program paths even the difficult ones. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Software testing Path testing Automatic test data generation negative selection algorithm

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Integrated negative selection algorithm and Positive selection algorithm for Malware Detection 4

Integrated Negative Selection Algorithm and Positive Selecti...

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4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Zhang, Fuyong Ma, Ying Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781509034840

Run-time malware detection strategies are efficient and robust, which get more and more attention. In this paper, we use I/O Request Package (IRP) sequences for malware detection. N-gram will be used to analyze IRP sequences for feature extraction. Integrated negative selection algorithm (NSA) and Positive selection algorithm (PSA), through a selection of n-gram sequences which only exist in malware IRP sequences, we have more than 96% true positive rate and 0% false positive rate.

关键词： I/O Request Package artificial immune system negative selection algorithm positive selection algorithm malware detection

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negative selection algorithm with constant detectors for anomaly detection

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APPLIED SOFT COMPUTING 2015年 36卷 618-632页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FB-NSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFB-NSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to eliminate the effect of training times on detectors, in the proposed approaches, detectors are generated in non-random ways. To determine the performances of the approaches, the experiments on 2-dimensional synthetic datasets, Iris dataset and ball bearing fault data were performed. Results show that FB-NSA and FFB-NSA outperforms the other anomaly detection methods in most cases. Besides, CFB-NSA can detect the abnormal degree of mechanical equipment. To determine the performances of CFB-NSA, the experiments on ball bearing fault data were performed. Results show that the abnormal degree based on the CFB-NSA can be used to diagnose the different fault types with the same fault degree, and the same fault type with the different fault degree. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Bearing

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negative selection algorithm Based Unknown Malware Detection Model 10th

Negative Selection Algorithm Based Unknown Malware Detection...

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10th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Zeng, Jinquan Tang, Weiwen Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Sichuan Commun Res Planning & Designing Co Ltd Chengdu 610041 Peoples R China

ISBN: (纸本)9783662490143;9783662490136

Nowadays, malwares have become one of the most serious security threats for computer systems and how to detect malwares is a difficult task, especially, unknown malwares. Artificial immune systems (AIS) is spired by biological immune system (BIS) and it is a relatively novel field. AIS is used to detect malwares and gets some exciting results. The most known AIS model is negative selection algorithm (NSA) and it can only use normal samples to train. The traditional NSAs generate detectors in the training phase and then detect anomaly elements in the testing phase. There are some drawbacks in the traditional NSAs. Firstly, the real applications often change, normal can change to anomalous, and vice versa. The traditional NSAs easily produce many of false alarm and false negative in the real applications. Secondly, the traditional NSAs lack continuous learning ability in the testing phase and it is costly to generate enough detectors to cover the total non-self space in the training. In order to overcome the drawbacks of the traditional NSAs, a new scheme with online adaptive learning is introduced to NSA, and it includes that constructing the appropriate profile of the system, generating new detectors cover the holes of the non-self space, deleting these detectors which lie in the self-space decreases false alarms and amending these detectors which cover partly self-space decreases false alarm and increase detecting rate.

关键词： Artificial immune systems negative selection algorithm Adaptive learning Malware

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