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Artificial intelligence based optimization for mapping IP addresses to prevent cyber-based attacks

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Measurement: Sensors 2022年 24卷

作者： Kalai chelvi, T. Gnanaprakasam, C. Indumathy, M. Khilar, Rashmita Sathish Kumar, P.J. Artificial Intelligence and Data Science Panimalar Engineering College India CSE SRM Institute of Science and Technology Vadapalani India Department of IT Saveetha School of Engineering Chennai India Department of Computer Science & Engineering Panimalar Engineering College Chennai India

Dynamic capacity decides the bearing of mechanical turn of events, logical and sane dynamic capacity is a vital factor in the turn of events and development of an industry. With the quick advancement of man-made reasoning and PC, man-made consciousness has been generally utilized in game, the computer generated simulation and bionic robot research has become a significant piece of bionics research and has perceived a lot of consideration. As of now, the savvy job of man-made consciousness has not been concentrated inside and out. A large number of the current virtual characters have issues of moderate reaction and helpless dynamic. Hence, in this paper, the virtual soccer match has been taken for instance, and the dynamic capacity of the virtual person of the game was investigated, to settle on a superior discretion and dynamic capacity, with the goal that most of clients can truly feel the knowledge of virtual job. With the advancement of current science and innovation, PC network innovation has been applied to different fields of creation and life. Notwithstanding, a wide range of organization assaults arise in unendingly, and the security and insurance of PC network is vital. On this premise, this paper took wellbeing assurance from the PC on the organization data as the beginning from the sensible and specialized angles to frame a blend of various data security innovation of PC network safeguard inside and out model and presented and investigated for wellbeing innovation by utilizing the model. The paper discussed about the PC network data security innovation of various play in the data security assurance work. The outcomes show that the profundity assurance model can viably forestall network assaults. © 2022

关键词： Genetic algorithms

Towards Constituting Mathematical Structures for Learning to Optimize

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arXiv 2023年

作者： Liu, Jialin Chen, Xiaohan Wang, Zhangyang Yin, Wotao Cai, HanQin Inc BellevueWA United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL United States

Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned model can overfit and may not generalize well to out-of-distribution test sets. In this paper, we derive the basic mathematical conditions that successful update rules commonly satisfy. Consequently, we propose a novel L2O model with a mathematics-inspired structure that is broadly applicable and generalized well to out-of-distribution problems. Numerical simulations validate our theoretical findings and demonstrate the superior empirical performance of the proposed L2O model. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Prediction Aided by Surrogate Training

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arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer

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arXiv 2023年

作者： Cao, Yuzhou Mozannar, Hussein Feng, Lei Wei, Hongxin An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore CSAIL and IDSS Massachusetts Institute of Technology CambridgeMA United States Department of Statistics and Data Science Southern University of Science and Technology China

Enabling machine learning classifiers to defer their decision to a downstream expert when the expert is more accurate will ensure improved safety and performance. This objective can be achieved with the learning-to-defer framework which aims to jointly learn how to classify and how to defer to the expert. In recent studies, it has been theoretically shown that popular estimators for learning to defer parameterized with softmax provide unbounded estimates for the likelihood of deferring which makes them uncalibrated. However, it remains unknown whether this is due to the widely used softmax parameterization and if we can find a softmax-based estimator that is both statistically consistent and possesses a valid probability estimator. In this work, we first show that the cause of the miscalibrated and unbounded estimator in prior literature is due to the symmetric nature of the surrogate losses used and not due to softmax. We then propose a novel statistically consistent asymmetric softmax-based surrogate loss that can produce valid estimates without the issue of unboundedness. We further analyze the non-asymptotic properties of our method and empirically validate its performance and calibration on benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Importance of Lightweight Algorithm for Embedded Security in Machine-to-Machine Communication towards Internet of Things 4

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Importance of Lightweight Algorithm for Embedded Security in...

4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Salunke, Mahendra Balkrishna Mahalle, Parikshit N. Shinde, Gitanjali Rahul Department of Computer Engineering Smt. Kashibai Navale College of Engineering Sppu Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Sppu Pune India Department of CSE-AIML Vishwakarma Institute of Information Technology Sppu Pune India

ISBN: (纸本)9781450399937

The Internet of Things (IoT) has been regarded as an important technological advancement that has changed how people think about and performs their activities. It is a framework that aims to create an efficient and effective method of managing various assets. The IoT also offers a paradigm shift that will enable the treatment of both physical and virtual assets uniformly. The increasing number of devices connected to the Internet will allow users to collect and exchange large amounts of data, making better decisions and enhancing their lives. One of the most important factors that the Internet is constantly working on is the development of new connectivity methods. One of these involves the Internet of Things, a framework for exchanging information among various sensors and devices. The rapid emergence and evolution of machine-to-machine communications will bring new opportunities for various applications. Unfortunately, many challenges still need to be resolved to make this technology work properly. One of the most important factors that need to be considered is the availability of secure infrastructure. One of the most important factors that need to be considered when implementing the security features of the Internet of Things is the availability of lightweight security algorithms. These are designed to help prevent unauthorized access and manipulation of the data sent and received by various devices. To function properly, the algorithms should not require a lot of resources, such as processing power. One of the advantages of implementing lightweight security algorithms is that they can help reduce the overall overhead of the security operations of the IoT. This is especially important since large-scale deployments are expected to involve thousands or millions of devices. This paper aims to comprehensively overview the various advantages of implementing these security algorithms. © 2022 ACM.

关键词： Internet of things

Co-modality graph contrastive learning for imbalanced node classification 22

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Co-modality graph contrastive learning for imbalanced node c...

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Yiyue Qian Chunhui Zhang Yiming Zhang Qianlong Wen Yanfang Ye Chuxu Zhang Department of Compute Science and Engineering University of Notre Dame Department of Computer Science Brandeis University Department of Computer and Data Sciences Case Western Reserve University

ISBN: (纸本)9781713871088

Graph contrastive learning (GCL), leveraging graph augmentations to convert graphs into different views and further train graph neural networks (GNNs), has achieved considerable success on graph benchmark datasets. Yet, there are still some gaps in directly applying existing GCL methods to real-world data. First, handcrafted graph augmentations require trials and errors, but still can not yield consistent performance on multiple tasks. Second, most real-world graph data present class-imbalanced distribution but existing GCL methods are not immune to data imbalance. Therefore, this work proposes to explicitly tackle these challenges, via a principled framework called Co-Modality Graph Contrastive Learning (CM-GCL) to automatically generate contrastive pairs and further learn balanced representation over unlabeled data. Specifically, we design inter-modality GCL to automatically generate contrastive pairs (e.g., node-text) based on rich node content. Inspired by the fact that minority samples can be "forgotten" by pruning deep neural networks, we naturally extend network pruning to our GCL framework for mining minority nodes. Based on this, we co-train two pruned encoders (e.g., GNN and text encoder) in different modalities by pushing the corresponding node-text pairs together and the irrelevant node-text pairs away. Meanwhile, we propose intra-modality GCL by co-training non-pruned GNN and pruned GNN, to ensure node embeddings with similar attribute features stay closed. Last, we fine-tune the GNN encoder on downstream class-imbalanced node classification tasks. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baseline models and learns more balanced representations on real-world graphs.

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Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home data

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arXiv 2023年

作者： Waheed, Nazar Khan, Fazlullah Mastorakis, Spyridon Jan, Mian Ahmad Alalmaie, Abeer Z. Nanda, Priyadarsi School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Department of Computer Engineering Abdul Wali Khan University Mardan Pakistan Computer Science and Engineering Department University of Notre Dame United States

The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities. © 2023, CC BY.

关键词： Internet of things

Demystifying Cyber terrorism: Causes, Costs & its impact on individuals’ psyche and public confidence

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Demystifying Cyber terrorism: Causes, Costs & its impact on ...

Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), International Conference on

作者： Syed Rameem Zahra Tausifa Jan Saleem Sonia Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi Delhi India Yogananda School of AI Computer and Data Science Shoolini University Solan H.P India

Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions.

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems

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Air Purification Robotics using Cloud and Deep Q Networks fo...

Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Elangovan Guruva Reddy T. S. Balaji Damodhar Yuvaraj S B. Santha Vathani S. Sivakumar N. Malathi Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Management Studies Mahalashmi Women's College of Arts and Science Chennai Tamil Nadu India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues.

关键词： Cloud computing Technological innovation Purification Autonomous systems Robot kinematics Decision making Urban areas