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Analyzing darknet traffic through machine learning and neucube spiking neural networks

Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Using Contextual Text Mining Algorithm to Analysis Yearly Trend for Population Ageing and Declining Fertility with Government Science and technology Projects in Taiwan 21

Using Contextual Text Mining Algorithm to Analysis Yearly Tr...

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4th International Conference on Computer Science and Software Engineering, CSSE 2021

作者： Chuang, C.Y. Huang, M.C. Lin, Y.Y. Hsiao, Y.H. Wang, T.C. Science and Technology Policy Research and Information Center National Applied Research Laboratories Taiwan National Center for High-performance Computing National Applied Research Laboratories Taiwan

ISBN: (纸本)9781450390675

The growth of the world population has been steadily slowing down, primarily owing to an almost worldwide decline of fertility and ageing of population. In Taiwan, the fertility rate is experiencing long-Term downtrends, and consequently, ageing of their populations. In view of this, the purpose of this paper is to analyze the yearly trend for population ageing and declining fertility. We proposed the yearly trend analysis based on contextual text mining methods from government science and technology projects to extract the Chinese pattern and word about the population ageing and declining fertility. The government science and technology projects, which archives research projects sponsored by government, are retrieved from Government Research Bulletin (GRB) from 1981 to April 2021. Then, the two issue of policy "Major trends and the importance of issues" and "Policies and action plans" combine the population ageing and declining fertility with yearly trend are explored and discussed. The obtained results can be analyzed the yearly trend about fertility rate, elderly population, and youth workforce with population ageing and declining fertility on the first policy issue "Major trends and the importance of issues". And it can also be analyzed the yearly trend about formulates the policies and action plan on the second policy issue "Policies and action plans" in Taiwan. According to the results of the analysis, we observe that the fertility rate and youth workforce have been a progressive decrease every year. Within 10 years, it will be expected to transition into a super-Aged society about the elderly population. In the future, the specify topic of population ageing and declining fertility with yearly trend will be analyzed on other policy issues by government. The analysis with yearly trend is used by the government to evaluate how to allocate research funding to the specify topic and it can be as the reference for decision of government in Taiwan. © 2021 Association

关键词： Public policy

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Strategic Defense Post Allocation Using the Corona Cover Set for Enhanced Military Zone Security

Strategic Defense Post Allocation Using the Corona Cover Set...

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Women in computing (InCoWoCo), International Conference for

作者： Niveditha. P G. Mahadevan C. Sivagnanam Department of Mathematics The Gandhigram Rural Institute (Deemed to be University) Gandhigram Tamilnadu India Mathematics and Computing Skills Unit University of Technology and Applied Sciences-Sur Sultanate of Oman

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Defense forces are vital for national safety, peace-keeping, disaster response, and economic support. They require strong support systems, medical access, rapid deployment, and effective command centers to operate efficiently. Proper placement and a well-structured defense strategy focusing on sustainability and readiness are essential for ensuring safety and stability for both military and civilians. Recently G. Mahadevan *** established the concept of Corona covering number of a graph and obtained many results. A vertex cover set $S\subseteq V(G)$ is a corona cover set if every vertex $v\in S$ such that $d_{\langle S\rangle}(v)=1$ or there exist a vertex $u\in S$ with $d_{\langle S\rangle}(u)=1$ and $uv\in E$ . The least cardinality of a corona cover set is the corona covering number of a graph and it is expressed as $\tau_{C}(G)$ . In this article, the investigation of corona covering number in the strategic placement of defense posts within military zones, aiming to enhance operational efficiency and effectively deter unauthorized incursions has been attained.

关键词： Protected areas Systematics Military computing Focusing Corona Security Resource management Sustainable development Monitoring Resilience

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Phase Diagram and Physical Properties Anisotropy of Strontianite

SSRN

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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xilong Jin, Neng-Zhi Zhang, Cai-Rong Sun, Xiao-Wei Zhang, Lei School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

The physical properties and anisotropy of carbonate at high pressure are the basis of understanding the deep carbon cycle and storage. Here, the phase diagram of strontianite (SrCO3) was constructed by first-principles calculations and its physical properties and anisotropy at high pressure were investigated. The results show that SrCO3 only has the Pnma and Pmmn phases, and there is no R3m phase. SrCO3 polymorphs have large elastic anisotropy. Compared with CaCO3 polymorphs, SrCO3 polymorphs have lower wave velocity and smaller seismic anisotropy. The linear elastic compressibility combined with the volumetric thermal expansion coefficient was used to obtain the linear thermal expansion coefficient of SrCO3-Pmmn. © 2024, The Authors. All rights reserved.

关键词： Strontium compounds

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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Coding Observer Nodes for Sybil Attacks Detection in Mobile Wireless Sensor Networks 19

Coding Observer Nodes for Sybil Attacks Detection in Mobile ...

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19th IEEE International Conference on Embedded and Ubiquitous computing, EUC 2021

作者： Sassani Sarrafpour, Bahman A. Alomirah, Abdulaziz Pang, Shaning Sarrafpour, Soshian School of Computing Engineering Applied Technology Unitec New Zealand Auckland New Zealand School of Computing Engineering Applied Technology Unitec New Zealand Auckland New Zealand Federation University School of Engineering Information Technology Physical Science Melbourne Australia Yale University School of Medicine Newhaven United States

ISBN: (纸本)9781665400367

Sybil attack is one of the most common and serious attacks in wireless sensor networks, in which a malicious node illegitimately forges several (fake) identities. These fake copies confuse and collapse the network. Sybil attack causes too many threats to the routing algorithm, data aggregation, fair resource allocation, voting system, and misbehavior detection. In this paper, we propose a new lightweight algorithm for detecting the Sybil attack in mobile wireless sensor networks using observer nodes. Observer nodes are normal, trustful nodes which have been initially programmed to observe the network and report malicious behaviors. An observer node counts the number of times a node has appeared as a common neighbor between itself and its neighbors. After collecting some information about its neighbors, each observer node considers the nodes whose counters are above a threshold as critical, and nodes having all critical nodes in their neighborhood are considered suspicious nodes. The results show that the true detection rate of the proposed algorithm is 98.1%, and its false detection rate is 0.5%, while similar algorithms could not achieve better than 95.4% and 1.2% for these metrics, respectively. In addition, the proposed algorithm outperforms other algorithms in terms of overhead. © 2021 IEEE.

关键词： Cybersecurity

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Infeasibility of Sequitur-Based Motif for Mouse Dynamics in Digital Forensics

Infeasibility of Sequitur-Based Motif for Mouse Dynamics in ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Richard Ikuesan Farkhund Iqbal Abdul Kadhim Hayawi Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi UAE Computational Systems College of Interdisciplinary Studies Zayed University Abu Dhabi UAE

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Utilizing and deploying behavioral biometric modalities (BBM), specifically mouse dynamics for user attribution in a digital investigation, has seen a rapid upsurge. However, as asserted in a recent study, the current reliability threshold of BBM falls short of the required standard for forensic attributes. This poor reliability can be attributed, in part, to the low signal-to-noise ratio in a typical behavioral dataset. This study proposed a context-free signature identification and extraction technique for BBM to extract a unique mouse dynamics signature suitable for a forensic process. A Re-Pair Grammar induction approach, which identifies and extracts unique Grammar sequences, was used to achieve this proposition. The grammar generation leverages symbolic aggregate approximation techniques to generate behavioral string subsequences from the mouse dataset. The Re-Pair approach was then used to develop a user attribution mechanism, which can be deployed for digital forensic analysis. The outcome of the implementation of the proposition, however, shows a poor performance relative to existing studies, hence its infeasibility as a benchmark for forensic science. However, it shows promising potential to reveal the inherent noise in mouse dynamics data, which can provide further insight into digital forensic science. This result further extends the literature on establishing digital forensic science, a significant requirement for any forensic discipline.

关键词： Biometrics (access control) Generative AI Aggregates Heuristic algorithms Digital forensics Grammar Reliability

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Study on the Structure, Elasticity, and Thermal Conductivity of Orthocarbonate Sr2CO4

SSRN

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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xi-Long Jin, Neng-Zhi Zhang, Cai-Rong Wang, Xiao-Yun Wu, Liang School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

Orthocarbonate Sr2CO4 is a recently discovered Sr-carbonate that plays a crucial role in understanding the global long-term carbon cycle. In this work, the structure, equation of state, elasticity, and thermal conductivity of Sr2CO4 were studied by first-principles calculations. The results show that the surfaces of the SrO9 and SrO11 polyhedrons, which make up Sr2CO4, are composed of triangles and quadrangles. By comparison with other alkaline earth metal orthocarbonates, SrCO3 polymorphs and the Preliminary Reference Earth Model (PREM), Sr2CO4 is found to be a carbonate with high density, low wave velocity, low anisotropy and low thermal conductivity in the entire Earth's mantle. This study may help to understand the high-pressure behavior of alkaline earth metal orthocarbonates in the Earth's interior. © 2024, The Authors. All rights reserved.

关键词： Elasticity

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Analysis of ECM Battery Modeling Techniques for Different Battery Types

Analysis of ECM Battery Modeling Techniques for Different Ba...

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International Workshop on Robot Motion and Control (RoMoCo)

作者： Mohsen Heydarzadeh Mohammad Gerami Tehrani Anam Tahir Eero Immonen Hashem Haghbayan Juha Plosila Faculty of Technology Industry Turku University of Applied Sciences Turku Finland Department of Product Development at Valmet Automotive Plc Department of Computing University of Turku Turku Finland

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

The paper presents a comparative study of two methods for identifying equivalent circuit models (ECM) parameters for battery modeling. In order to comprehensively compare the methods, the electrical behavior of a lithium titanate (LTO), a lithium iron phosphate (LFP), a lithium nickel manganese cobalt oxides (NMC), and a nickel cobalt aluminium (NCA) battery are analysed. A second-order ECM is used to model batteries, while two different theoretical methods are applied to identify the ECM's parameters. The first method finds a fitted function for battery performance based on time response of ECM dynamics, while the second method uses a subspace system identification approach. The models are compared by calculating the errors between simulated data and measurement data based on the discharge pulse power characterization (DPPC) test. Simulation results show that the second method is more accurate in batteries modeling, while the values of the identified parameters are more stable with respect to changes in state of charge (SOC).

关键词： Analytical models Lithium Nickel Batteries System identification Time factors State of charge

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Machinery Fault Diagnoses in High-Voltage Circuit Breakers Using Empirical Mode Decomposition-Support Vector Machine Model 3

Machinery Fault Diagnoses in High-Voltage Circuit Breakers U...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Al-Farouni, Mohammed Sudhakar, A.V.V. Divya, G. Bala Dhandayuthapani, V. Shivakanth, Gandla Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Electrical And Electronics Engineering Sr University Warangal India Department Of Ece Nitte Meenakshi Institute Of Technology Bengaluru India Department Of It College Of Computing And Information Sciences University Of Technology And Applied Sciences Shinas campus Shinas Oman Department Of Computer Science And Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

ISBN: (纸本)9798350318609

The circuit breakers have a flaw in their trip mechanism. Early mechanical faults like coil-plunger jam are hard to report using traditional fault diagnosis based on the coil current. However, users still have difficulty understanding and trusting the diagnosis results since the standard EMD is opaque in terms of interpretability. In this work, an Empirical Mode Decomposition with Support Vector Machine (EMP-SVM) proposed for machinery fault diagnosis. This work uses the vibration signal recovered from the trip process as an intrinsic time series of the data. At first, The Improved EMD is a data-adaptive multiresolution approach that breaks down signals into physically relevant components based on vibration and coil current signals. Lastly, utilizing the characteristics that were retrieved, the SVM is used to diagnose the defect. The implemented improved EMP-SVM model demonstrates superior performance, achieving Precision of (98.37%), recall of (91.03%) and F1-Score of (96.34%). When compared to previous models like Improved Dempster-Shafer Evidence Theory-Echo State Neural Network (IDSET-ESNN) and Phase Space Reconstruction with Support Vector Machine (PSR-SVM). © 2024 IEEE.

关键词： Empirical mode decomposition

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