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Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers

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Journal of Physics: Conference Series 2019年第1期1314卷 012184-012184页

作者： Fan, SiJie Zhao, YaQun State Key Laboratory of Mathematical Engineering and Advanced Computing 450001 China State Key Laboratory of Cryptography and Science 450001 China

In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification.

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Current Status of Electromagnetic Particle Detectors for LHAASO-KM2A

Current Status of Electromagnetic Particle Detectors for LHA...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Liu, Jia Zhang, Xiaopeng Sheng, Xiangdong Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Key Laboratory of Particle Astrophyics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D’epartement de Physique Nucl’eaire et Corpusculaire Facult’e de Sciences Universi

The Large High Altitude Air Shower Observatory (LHAASO) is a new generation hybrid cosmic ray observatory which is expected to reveal the mystery of the origin of cosmic rays. The one square kilometer array (KM2A) containing 5195 Electromagnetic particle Detectors (ED) and 1188 Muon Detectors (MD) is a sub-array of the LHAASO. The EDs are designed to measure the density and arriving time of the secondary particles of cosmic rays. KM2A reaches its 3/4 scale by December 2020. 3978 electromagnetic particle detectors (EDs) and 917 muon detectors (MDs) are now in stable operation. In this paper, we will introduce the construction process of ED, the performances and long-term stability of the detectors. © Copyright owned by the author(s).

关键词： Observatories

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Efficient and secure authentication scheme based on certificateless aggregate signature in space networks 7

Efficient and secure authentication scheme based on certific...

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2017 7th International Workshop on Computer Science and engineering, WCSE 2017

作者： Hu, Zhiyan Du, Xuehui Cao, Lifeng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811136719

According to the requirements of the space networks for computing cost, communication cost and security, a new efficient certificateless aggregate signature algorithm is proposed. The signature length is only 2 group elements, the verification stage requires only 3 pairing operations, and the algorithm is proved secure based on the computational Diffie-Hellman problem under the random oracle model. Compared with the other similar algorithm, this algorithm achieves higher security with higher efficiency and lower cost of communication. Then according to the signature algorithm proposed in this paper, an authentication scheme with the function of mutual authentication, aggregate authentication and group key agreement is presented. The analysis results show that the scheme is secure and efficient, especially suitable for the large-scale space networks.

关键词： Authentication

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Detection of emission from Cygnus Cocoon above 100TeV with LHAASO

Detection of emission from Cygnus Cocoon above 100TeV with L...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Li, Cong Chen, Songzhan Wu, Sha Wang, Lingyu Yang, Ruizhi Liu, Ruoyu Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China University of Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-Sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D’epartement de Physique Nucl’eaire et Corpusculaire Facult’e de Sciences Universit’e de Gen‘eve 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Fr

The origin of PeV cosmic rays in the Galaxy is a long-standing puzzle. Superbubble or massive star cluster (MSC) has been suggested as powerful cosmic-ray accelerators. Cygnus Cocoon, an extended gamma-ray source, is likely associated with the MSC Cygnus OB2. Previous observations have shown that its spectrum extends up to 100 TeV with a break around 10 TeV. The spatial coincidence between the gamma-ray emission and molecular clouds in the region implies the hadronic origin of the gamma-ray emission, supporting Cygnus cocoon as a cosmic-ray proton accelerator. However, lack of a precise measurement of the spectrum beyond 100 TeV as well as relevant morphology prevent people from concluding that it is the source of PeV cosmic rays. We here report the observation of LHAASO-KM2A on this region. The maximum significance above 25 TeV is about 21.8σ. A photo with energy up to 1.4 PeV is detected from this region, which indicates the spectrum can extend up to 1 PeV. Such a result may be considered as the evidence for cosmic ray accelerated beyond PeV in Cygnus Cocoon. © Copyright owned by the author(s).

关键词： Cosmic rays

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HTTP Tunnel Trojan Detection Model Based on Deep Learning

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Journal of Physics: Conference Series 2019年第4期1187卷

作者： Yubo He Yuefei Zhu Wei Lin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

Aiming at reducing the high False Negative rate of the existing Trojan horse detection method based on behavior, this paper utilized the sequence characteristics of tunnel Trojan communication extracted from the transport layer and the bi-directional recurrent neural network in deep learning to build a HTTP tunnel Trojan detection model. The experimental result showed that the deep learning-based detection model reduced the false positive rate of normal network traffic and improved the Trojan detection rate. We also found that the deep learning-based detection model reduced the feature selecting and data cleaning process of generating samples and improved the easy-using of the HTTP tunnel Trojan detection model.

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A compact construction for non-monotonic online/offline CP-ABE scheme 10th

A compact construction for non-monotonic online/offline CP-A...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Zhang, Junqi Cheng, Qingfeng Wei, Fushan Zhang, Xinglong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783319723945

Nowadays the mobile devices are becoming the necessities in our life, while they are generally resource-constrained, CP-ABE schemes designed for mobile devices should have the property of low computational complexity, therefore Online/Offline mechanism has prospect future in cryptographic mechanism. In this paper, we attempt to construct an unbounded Online/Offline CP-ABE scheme based on a non-monotonic access structure. During the offline phase, most of the computations for encryption are done;during the online phase, we transform the non-monotonic access structure with positive attribute sets into a monotonic access structure which is based on the LSSS access structure with positive and negative attribute sets, then it only needs a small amount of addition and multiplication operations for the rest components of encryption. Compared with the original non-monotonic CP-ABE scheme, our scheme remains the same on the public keys and the master secret keys, with only a small increase in computational complexity. The computational complexity during online phase is very small. © Springer International Publishing AG 2017.

关键词： Computational complexity

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A formal android permission model based on the B method 10th

A formal android permission model based on the B method

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Ren, Lu Chang, Rui Yin, Qing Man, Yujia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783319723884

The rapid development of Android devices brings the increase of security requirements, especially for access control. Recently, many enhancements have been put forward towards the Android permission mechanism. However, few researches focus on the formalization and verification of security schemes. In this paper, we propose a formal Android permission model based on the B method, describing mechanism specifications and proving security properties. All model components are type checked by AtelierB, with 87% (154 out of 178) of generated proof obligations proved yet. The model is fully animated and checked by ProB. The results show that all specifications are well-defined without any deadlock and invariant violation. The proposed B model is for not only security analysis, but also system animation and extension. It presents a feasible approach to specify and verify the security scheme in the embedded system, which is able to translate into executable codes and implement practical module as well. © 2017, Springer International Publishing AG.

关键词： Specifications

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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An Attack Surface Metric Suitable for Heterogeneous Redundant System with the Voting Mechanism

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Journal of Physics: Conference Series 2019年第3期1168卷

作者： Liqun Wang Zheng Zhang Weichao Li Zhenwu Liu Hao Liu State key Laboratory of Mathematical Engineering and Advanced Computing MEAC Zhengzhou Henan 450001 China

With the development of cyberspace security 'asymmetry in attack and defense', heterogeneous redundancy design is gradually being widely used. Heterogeneous redundancy design can enhance the robustness and security of the system through the differentiation of heterogeneous components. But the complex structural design of the system makes it difficult to apply the existing attack surface metric to measure the security of heterogeneous redundant system. In this paper, based on the attack surface metric proposed by Manadhata, we provide a new attack surface metric suitable for heterogeneous redundant system with voting mechanism. In this new metric, we define a new notion named attack surface arbitration, and also propose a new method for quantifying result of arbitration. The experiment result shows that the new attack surface metric can properly describe the attack surface of a heterogeneous redundant system with voting mechanism, which changes with the adjustment of voting algorithm.

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A 3D Likelihood Analysis Tool for LHAASO-KM2A data

A 3D Likelihood Analysis Tool for LHAASO-KM2A data

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37th International Cosmic Ray Conference, ICRC 2021

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D'Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, Kaikai Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, Xiaoyuan Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, Jian Li, Jie Li, K. Li, W.L. Li, X.R. Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.R. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-Sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli "Federico II" Complesso Universitario di Monte Sant'Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D'epartement de Physique Nucl'eaire et Corpusculaire Facult'e de Sciences Universit'e de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Front

The square kilometer array (KM2A) is the main array of the Large High Altitude Air Shower Observatory (LHAASO), which is the most sensitive gamma-ray detector for energies above a few tens of TeV. We are developing a software pipeline based on the experimental data, Monte-Carlo simulations and the pointing track of the arrays. The pipeline is able to perform 3D (sky images at different energies) fits of KM2A data, similar to those used for Fermi-LAT and DAMPE gamma-ray analysis. This 3D likelihood analysis could fit source models of arbitrary morphology to the sky images, and get energy spectra information and detection significances simultaneously. The analysis with this software could give consistent results with those using traditional method. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Pipelines

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