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SMS-based mobile botnet detection framework using intelligent agents

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Journal of Cyber Security and Mobility 2017年第2期5卷 47-74页

作者： Alzahrani, Abdullah J. Ghorbani, Ali A. The College of Computer Science and Engineering (CCSE) University of Hail (UOH) Saudi Arabia Canadian Institute for Cybersecurity Canada Research Chair in Cybersecurity Faculty of Computer Science University of New Brunswick Canada

Along with increasing security measures in Android platforms, the amount of Android malware that use remote exploits has grown significantly. Using mobile botnets, attackers concentrate on reliable attack vectors such as SMS messages. Short Message Service (SMS) has been increasingly targeted by a number of malicious applications ("apps") that have the ability to abuse SMS features in order to send spam, to transfer command and control (C&C) instructions, to distribute malicious applications via URLs embedded in text messages, to send text messages to premium-rate numbers, and to exploit smartphones. In this paper, we propose an SMS-based botnet detection formwork that uses multi-agent technology based on observations of SMS and Android smartphone features. This formwork has the ability to detect SMS botnets and identify ways to block the attacks in order to prevent damage caused by botnet attacks. We developed an adaptive hybrid model of SMS botnet detectors by using a combination of signature-based and anomaly-based algorithms. These components utilize multi-agent technology to recognize malicious SMS and prevent users from opening these messages that infecting smartphones. This framework includes defence module that employed a more proactive approach that allows us to directly generate signatures and rules that can be used to protect Android smartphones from abuse by SMS botnets. The framework creates a user profile that is used to perform behavioural profiling analysis in order to identity malicious SMS and cut the C&C Channel. © 2017 River Publishers.

关键词： Android malware Intrusion detection Mobile botnet Multi-agent system SMS

A joint learning and communications framework for federated learning over wireless networks

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arXiv 2019年

作者： Chen, Mingzhe Yang, Zhaohui Saad, Walid Yin, Changchuan Vincent Poor, H. Cui, Shuguang Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Centre for Telecommunications Research Department of Informatics King's College London WC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

ASMCNN: An efficient brain extraction using active shape model and convolutional neural networks

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arXiv 2018年

作者： Nguyen, Duy H.M. Nguyen, Duy M. Truong, Mai T.N. Nguyen, Thu Tran, Khanh T. Triet, Nguyen A. Bao, Pham T. Nguyen, Binh T. Max Planck Institute For Informatics Germany Dublin City University Ireland Department Of Multimedia Engineering Dongguk University Korea Republic of Department Of Mathematics University Of Louisiana At Lafayette United States Center For Machine Vision And Signal Analysis University Of Oulu Finland Department Of Mathematics University Of Architecture Of Ho Chi Minh City Viet Nam Department Of Computer Science Saigon University Ho Chi Minh City Viet Nam Department Of Computer Science VNU-HCMUS Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnetic Resonance Imaging (MRI) scans, namely ASMCNN, by combining the Active Shape Model (ASM) and Convolutional Neural Network (CNN) for taking full of their advantages to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in the sagittal plane. First, we divide images into different groups such that, in each group, shapes and structures of brain boundaries have similar appearances. Second, a modified version of ASM is used to detect brain boundaries by utilizing prior knowledge of each group. Finally, CNN and post-processing methods, including Conditional Random Field (CRF), Gaussian processes, and several special rules are applied to refine the segmentation contours. Experimental results show that our proposed method outperforms current state-of-the-art algorithms by a significant margin in all *** Codes 68T10 Copyright © 2018, The Authors. All rights reserved.

关键词： Gaussian distribution

Quantum computing at the frontiers of biological sciences

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Acoustic Positioning for Deep Sea Neutrino Telescopes with a System of Piezo Sensors Integrated into Glass Spheres

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arXiv 2024年

作者： Albert, A. Alves, S. André, M. Ardid, M. Ardid, S. Aubert, J.-J. Aublin, J. Baret, B. Basa, S. Becherini, Y. Belhorma, B. Bendahman, M. Benfenati, F. Bertin, V. Biagi, S. Boumaaza, J. Bouta, M. Bouwhuis, M.C. Brânzaş, H. Bruijn, R. Brunner, J. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carr, J. Carretero, V. Celli, S. Cerisy, L. Chabab, M. El Moursli, R. Cherkaoui Chiarusi, T. Circella, M. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Díaz, A.F. De Martino, B. Distefano, C. Di Palma, I. Donzaud, C. Dornic, D. Drouhin, D. Eberl, T. Eddymaoui, A. van Eeden, T. van Eijk, D. El Hedri, S. El Khayati, N. Enzenhöfer, A. Fermani, P. Ferrara, G. Filippini, F. Fusco, L. Gagliardini, S. García, J. Oliver, C. Gatius Gay, P. Geißelbrecht, N. Glotin, H. Gozzini, R. Ruiz, R. Gracia Graf, K. Guidi, C. Haegel, L. van Haren, H. Heijboer, A.J. Hello, Y. Hennig, L. Hernández-Rey, J.J. Hößl, J. Huang, F. Illuminati, G. Jisse-Jung, B. de Jong, M. de Jong, P. Kadler, M. Kalekin, O. Katz, U. Kouchner, A. Kreykenbohm, I. Kulikovskiy, V. Lahmann, R. Lamoureux, M. Lazo, A. Lefèvre, D. Leonora, E. Levi, G. Le Stum, S. Loucatos, S. Manczak, J. Marcelin, M. Margiotta, A. Marinelli, A. Martínez-Mora, J.A. Migliozzi, P. Moussa, A. Muller, R. Navas, S. Nezri, E. Fearraigh, B.Ó. Oukacha, E. Păun, A. Păvălaş, G.E. Peña-Martínez, S. Perrin-Terrin, M. Piattelli, P. Poirè, C. Popa, V. Pradier, T. Randazzo, N. Real, D. Riccobene, G. Romanov, A. Sánchez-Losa, A. Saina, A. Greus, F. Salesa Samtleben, D.F.E. Sanguineti, M. Sapienza, P. Schüssler, F. Seneca, J. Spurio, M. Stolarczyk, Th. Taiuti, M. Tayalati, Y. Vallage, B. Vannoye, G. Van Elewyck, V. Viola, S. Vivolo, D. Wilms, J. Zavatarelli, S. Zegarelli, A. Zornoza, J.D. Zúñiga, J. Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace MulhouseF-68100 France IFIC - Instituto de Física Corpuscular CSIC Universitat de València c/ Catedrático José Beltrán Valencia Paterna2 E-46980 Spain Technical University of Catalonia Laboratory of Applied Bioacoustics Rambla Exposició Vilanova i la Geltrú Barcelona08800 Spain Universitat Politècnica de València C/ Paranimf 1 Gandia46730 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Aix Marseille Univ CNRS CNES LAM Marseille France National Center for Energy Sciences and Nuclear Techniques B.P.1382 R. P. Rabat10001 Morocco University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco INFN - Sezione di Bologna Viale Berti-Pichat 6/2 Bologna40127 Italy Dipartimento di Fisica e Astronomia dell’Università di Bologna Viale Berti-Pichat 6/2 Bologna40127 Italy Via S. Sofia 62 Catania95123 Italy University Mohammed I Laboratory of Physics of Matter and Radiations B.P.717 Oujda6000 Morocco Nikhef Science Park Amsterdam Netherlands Institute of Space Science Măgurele BucharestRO-077125 Romania Universiteit van Amsterdam Instituut voor Hoge-Energie Fysica Science Park 105 Amsterdam1098 XG Netherlands INFN - Sezione di Genova Via Dodecaneso 33 Genova16146 Italy INFN - Sezione di Roma P.le Aldo Moro 2 Roma00185 Italy Dipartimento di Fisica dell’Università La Sapienza P.le Aldo Moro 2 Roma00185 Italy LPHEA Faculty of Science - Semlali Cadi Ayyad University MarrakechP.O.B. 2390 Morocco INFN - Sezione di Bari Via E. Orabona 4 Bari70126 Italy Department of Computer Architecture and Technology CITIC University of Granada Granada18071 Spain Université Paris-Sud Orsay91405 Cedex France Erlangen Centre for Astroparticle Physics Erwin-Rommel-Str. 1 Erlangen91058 Germany Università di Salerno INF

Position calibration in the deep sea is typically done by means of acoustic multilateration using three or more acoustic emitters installed at known positions. Rather than using hydrophones as receivers that are exposed to the ambient pressure, the sound signals can be coupled to piezo ceramics glued to the inside of existing containers for electronics or measuring instruments of a deep sea infrastructure. The ANTARES neutrino telescope operated from 2006 until 2022 in the Mediterranean Sea at a depth exceeding 2000 m. It comprised nearly 900 glass spheres with 432 mm diameter and 15 mm thickness, equipped with photomultiplier tubes to detect Cherenkov light from tracks of charged elementary particles. In an experimental setup within ANTARES, piezo sensors have been glued to the inside of such – otherwise empty – glass spheres. These sensors recorded signals from acoustic emitters with frequencies from 46545 to 60235 Hz. Two waves propagating through the glass sphere are found as a result of the excitation by the waves in the water. These can be qualitatively associated with symmetric and asymmetric Lamb-like waves of zeroth order: a fast (early) one with ve ≈ 5 mm/µs and a slow (late) one with v ≈ 2 mm/µs. Taking these findings into account improves the accuracy of the position calibration. The results can be transferred to the KM3NeT neutrino telescope, currently under construction at multiple sites in the Mediterranean Sea, for which the concept of piezo sensors glued to the inside of glass spheres has been adapted for monitoring the positions of the photomultiplier tubes. © 2024, CC BY.

关键词： Glues

Towards Mixed Structural-functional Models for Algebraic Fault Attacks on Ciphers

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Towards Mixed Structural-functional Models for Algebraic Fau...

IEEE International Verification and Security Workshop

作者： Jan Burchard Ange-Salome Messeng Ekossono Jan Horacek Mael Gay Bernd Becker Tobias Schubert Martin Kreuzer Ilia Polian Computer Architecture Group University of Freiburg Freiburg Germany Faculty for Comp. Science & Mathematics University of Passau Passau Germany Chair of Comp. Engineering University of Passau Passau Germany

Fault attacks are a major threat for hardware-implemented security primitives, and algebraic techniques (equation-solving) are one of the most powerful building blocks for such attacks. We show that structural models obtained from a circuit implementation of the analyzed cipher can lead to more efficient attacks than the functional models used in literature. We also discuss possible synergies of the traditional functional and the proposed structural models and show first results on mixed models that combine structural and functional information. The overspecification provided by the mixed models creates an optimization potential through a partial mixed model with different filter rules for the combination of the two models.

关键词： Circuit faults Mathematical model Ciphers Encryption Integrated circuit modeling Computational modeling

Constraints on the energy spectrum of the diffuse cosmic neutrino flux from the ANTARES neutrino telescope

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arXiv 2024年

作者： Albert, A. Alves, S. André, M. Ardid, M. Ardid, S. Aubert, J.-J. Aublin, J. Baret, B. Basa, S. Becherini, Y. Belhorma, B. Bendahman, M. Benfenati, F. Bertin, V. Biagi, S. Boumaaza, J. Bouta, M. Bouwhuis, M.C. Brânzaş, H. Bruijn, R. Brunner, J. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carr, J. Carretero, V. Cartraud, T. Celli, S. Cerisy, L. Chabab, M. Moursli, R. Cherkaoui El Chiarusi, T. Circella, M. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Díaz, A.F. De Martino, B. Distefano, C. Di Palma, I. Donzaud, C. Dornic, D. Drouhin, D. Eberl, T. Eddymaoui, A. van Eeden, T. van Eijk, D. El Hedri, S. El Khayati, N. Enzenhöfer, A. Fermani, P. Ferrara, G. Filippini, F. Fusco, L.A. Gagliardini, S. García, J. Oliver, C. Gatius Gay, P. Geißelbrecht, N. Glotin, H. Gozzini, R. Ruiz, R. Gracia Graf, K. Guidi, C. Haegel, L. van Haren, H. Heijboer, A.J. Hello, Y. Hennig, L. Hernández-Rey, J.J. Hößl, J. Huang, F. Illuminati, G. Jisse-Jung, B. de Jong, M. de Jong, P. Kadler, M. Kalekin, O. Katz, U. Kouchner, A. Kreykenbohm, I. Kulikovskiy, V. Lahmann, R. Lamoureux, M. Lazo, A. Lefèvre, D. Leonora, E. Levi, G. Le Stum, S. Loucatos, S. Manczak, J. Marcelin, M. Margiotta, A. Marinelli, A. Martínez-Mora, J.A. Migliozzi, P. Moussa, A. Muller, R. Navas, S. Nezri, E. Fearraigh, B.Ó. Oukacha, E. Păun, A. Păvălaş, G.E. Peña-Martínez, S. Perrin-Terrin, M. Piattelli, P. Poirè, C. Popa, V. Pradier, T. Randazzo, N. Real, D. Riccobene, G. Romanov, A. Losa, A. Sánchez Saina, A. Greus, F. Salesa Samtleben, D.F.E. Sanguineti, M. Sapienza, P. Schüssler, F. Seneca, J. Spurio, M. Stolarczyk, Th. Taiuti, M. Tayalati, Y. Vallage, B. Vannoye, G. Van Elewyck, V. Viola, S. Vivolo, D. Wilms, J. Zavatarelli, S. Zegarelli, A. Zornoza, J.D. Zúñiga, J. Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace MulhouseF-68100 France c/ Catedrático José Beltrán Valencia Paterna2 E-46980 Spain Technical University of Catalonia Laboratory of Applied Bioacoustics Rambla Exposició Vilanova i la Geltrú Barcelona08800 Spain Universitat Politècnica de València. C/ Paranimf Gandia1 46730 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Aix Marseille Univ CNRS CNES LAM Marseille France National Center for Energy Sciences and Nuclear Techniques B.P.1382 R. P. Rabat10001 Morocco University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco INFN - Sezione di Bologna Viale Berti-Pichat 6/2 Bologna40127 Italy Dipartimento di Fisica e Astronomia dell’Università di Bologna Viale Berti-Pichat 6/2 Bologna40127 Italy Via S. Sofia 62 Catania95123 Italy University Mohammed I Laboratory of Physics of Matter and Radiations B.P.717 Oujda6000 Morocco Nikhef Science Park Amsterdam Netherlands Institute of Space Science Bucharest MăgureleRO-077125 Romania Universiteit van Amsterdam Instituut voor Hoge-Energie Fysica Science Park 105 Amsterdam1098 XG Netherlands INFN - Sezione di Genova Via Dodecaneso 33 Genova16146 Italy INFN - Sezione di Roma P.le Aldo Moro 2 Roma00185 Italy Dipartimento di Fisica dell’Università La Sapienza P.le Aldo Moro 2 Roma00185 Italy LPHEA Faculty of Science - Semlali Cadi Ayyad University MarrakechP.O.B. 2390 Morocco INFN - Sezione di Bari Via E. Orabona 4 Bari70126 Italy Department of Computer Architecture and Technology CITIC University of Granada Granada18071 Spain Université Paris-Sud Orsay91405 Cedex France Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Physics Erwin-Rommel-Str. 1 Erlangen91058 Germany Università di Salerno INFN Gruppo Collegato

High-significance evidences of the existence of a high-energy diffuse flux of cosmic neutrinos have emerged in the last decade from several observations by the IceCube Collaboration. The ANTARES neutrino telescope took data for 15 years in the Mediterranean Sea, from 2007 to 2022, and collected a high-purity all-flavour neutrino sample. The search for a diffuse cosmic neutrino signal using this dataset is presented in this article. This final analysis did not provide a statistically significant observation of the cosmic diffuse flux. However, this is converted into limits on the properties of the cosmic neutrino spectrum. In particular, given the sensitivity of the ANTARES neutrino telescope between 1 and 50 TeV, constraints on single-power-law hypotheses are derived for the cosmic diffuse flux below 20 TeV, especially for power-law fits of the IceCube data with spectral index softer than 2.8. © 2024, CC BY.

关键词： Cosmology

Correction to: Lion and Man Game in Compact Spaces

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Dynamic Games and Applications 2018年第1期9卷 293-293页

作者： Olga Yufereva Krasovskii Institute of Mathematics and Mechanics UrB RAS Ekaterinburg Russia Chair of Applied Mathematics and Mechanics Institute of Mathematics and Computer Science Ural Federal University Ekaterinburg Russia

The original version of this article unfortunately contained a mistake in the author name.

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AutoFault: Towards Automatic Construction of Algebraic Fault Attacks

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AutoFault: Towards Automatic Construction of Algebraic Fault...

Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC)

作者： Jan Burchard Mañl Gay Ange-Salomé Messeng Ekossono Jan Horáček Bernd Becker Tobias Schubert Martin Kreuzer Ilia Polian Computer Architecture Group University of Freiburg Freiburg Germany Faculty for Comp. Science & Mathematics University of Passau Passau Germany Chair of Comp. Engineering University of Passau Passau Germany

A prototype of the framework AutoFault, which automatically constructs fault-injection attacks for hardware realizations of ciphers, is presented. AutoFault can be used to quickly evaluate the resistance of security-critical hardware blocks to fault attacks and the adequacy of implemented countermeasures. The framework takes as inputs solely the circuit description of the cipher and the fault(s) and produces an algebraic formula that can be handed over to an external solver. In contrast to previous work, attacks constructed by AutoFault do not incorporate any cipher-specific cryptoanalytic derivations, making the framework accessible to users without cryptographic background. We report successful application of AutoFault in combination with a state-of-the-art SAT solver to LED-64 and to small-scale AES. To the best of our knowledge, this is the first time that a state-of-the-art cipher (LED-64) was broken by a fault attack with no prior manual cryptanalysis whatsoever.

关键词： Circuit faults Ciphers Integrated circuit modeling Fault location Hardware