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Static-RWArmor: A Static Analysis Approach for Prevention of Cryptographic Windows Ransomware 22

Static-RWArmor: A Static Analysis Approach for Prevention of...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Ayub, Md Ahsan Siraj, Ambareen Filar, Bobby Gupta, Maanak Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA Sublime Secur Inc Washington DC USA

ISBN: (纸本)9798350381993;9798350382006

The everlasting fight between security researchers and ransomware authors, including cyber criminals who leverage ransomware to cripple organizations worldwide, has continued to evolve as novel techniques are used to evade ransomware detection. The victim not only endures paramount financial loss from business downtime for several days and/or paying ransom to regain control of their environment but also becomes at risk of being exposed to the stolen digital assets out on the Internet. To tackle these threats against ransomware, our research project aims to identify (1) structural similarities among 2,436 cryptographic Windows ransomware samples per calendar year between 2017 and 2021 and (2) structural dissimilarities against 3,014 benign applications using machine learning classifiers. We base our analysis on PE metadata for similarity analysis and binary classification tasks. With the Cosine Index, we capture 71% - 87.80% and 66% - 82.30% of similarities based on imports and function names feature spaces, respectively. On the other hand, after designing four experimental settings, Random Forest outperforms other applied classifiers by achieving 91.75%, 91.99%, 90.47%, and 91.05% at best for accuracy, precision, recall, and F1 scores, respectively, for ransomware detection.

关键词： machine learning Ransomware Similarity Analysis Static Analysis

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Leveraging machine learning and Deep learning Models for Enhanced Stock Price Prediction: A State-of-the-Art Analysis 21st

Leveraging Machine Learning and Deep Learning Models for Enh...

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21st international Symposium on Distributed computing and Artificial Intelligence

作者： Alaoui, Safae Belamfedel Hafid, Abdelatif Sayyouri, Mhamed Rahouti, Mohamed Ecole Natl Sci Appl ENSA LISA 72 Fes Morocco Ecole Super Ingn Sci Appl ESIS ESISA Analyt 505 Fes Morocco Fordham Univ Comp & Informat Sci Dept New York NY USA

ISBN: (纸本)9783031820724;9783031820731

The prediction of stock prices has recently gained considerable attention as a complex and challenging issue within the realms of economics and finance. Stock prices are affected by various factors, such as the business environment, stock market operations, inflation, and unexpected events. Since the stock market is volatile and nonlinear, finding the most effective model to forecast stock prices is one of the most challenging problems. Researchers have increasingly explored various machine learning (ML) and Deep learning (DL) models to address this issue due to their capacity to handle time series data and nonlinear patterns. These models often outperform traditional approaches in predicting stock prices with high accuracy and lower root mean square error (RMSE). This paper reviews various works that have utilized ML approaches for stock price prediction, covering research published between 2017 and 2023. This literature review discusses various techniques, their performance, limitations, and future work. We assess the latest techniques in many studies, including ML and DL models. The findings of this review conclude that Neural Networks (NNs) are the most commonly used approaches in predicting stock prices due to their effectiveness in detecting complex patterns in financial data.

关键词： Stock price prediction machine learning Deep learning Neural Networks Sentiment Analysis

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Decision Model of Ship Intelligent Collision Avoidance Based on Automatic Information System Data and Generic Adversary Imitation learning-Deep Deterministic Policy Gradient 23

Decision Model of Ship Intelligent Collision Avoidance Based...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Jiao Shi, Guoyou Zhu, Kaige Shi, Jiahui Wang, Yuchuang Navigation College Dalian Maritime University Liaoning Dalian116026 China Key Laboratory of Navigation Safety Guarantee of Liaoning Province China

ISBN: (纸本)9781450398633

Aiming at the problems that the current decision-making model of ship collision avoidance does not consider international Regulations for Preventing Collisions at Sea (COLREGS), ship maneuverability, and the need for a lot of training time, combined with the advantages of reinforcement learning and imitation learning, a ship intelligent collision avoidance decision-making model based on Generic Adversary Imitation learning (GAIL) is proposed: Firstly, the collision avoidance data in Automatic Information System (AIS) data is extracted as expert data;Secondly, in the generator part, the environment model is established based on Mathematical Model Group (MMG) and S-57 chart rendering, and the state space, behaviour space and reward function of reinforcement learning are constructed. The deep deterministic policy gradient (DDPG) is used to interact with the environment model to generate ship trajectory data. At the same time, the generator can constantly learn expert data;Finally, a discriminator can distinguish the expert data from the data generated by the generator is constructed and trained. The model training is completed when the discriminator cannot distinguish the two. In order to verify the performance of the model, AIS data near the South China Sea is used to process and extract collision avoidance decision data, and a ship intelligent collision avoidance decision model based on GAIL is established. After the model converges, the final generated data is compared with the expert data. The experimental results verify that the model proposed in this paper can reproduce the expert collision avoidance trajectory and is a practical decision model of ship collision avoidance. © 2023 ACM.

关键词： Collision avoidance

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The State of Algorithmic Fairness in Mobile Human-Computer Interaction

The State of Algorithmic Fairness in Mobile Human-Computer I...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Yfantidou, Sofia Constantinides, Marios Spathis, Dimitris Vakali, Athena Quercia, Daniele Kawsar, Fahim Aristotle Univ Thessaloniki Thessaloniki Greece Nokia Bell Labs Cambridge England

ISBN: (纸本)9798400702006

This paper explores the intersection of Artificial Intelligence and machine learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.

关键词： machine learning bias fairness responsible artificial intelligence mobile computing

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An Enhanced Intrusion Detection System Model Using machine learning Algorithm 4

An Enhanced Intrusion Detection System Model Using Machine L...

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4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Chandra, Leena Singh Singh, Devendra Kumar Guru Ghasidas Vishwavidyalaya Department of Computer Science and Engineering Bilaspur India

ISBN: (纸本)9798350349856

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding the integrity and performance of an organization. Throughout recent years, various approaches have been devised and put into practice to fortify the security, reliability, and availability of computer networks within enterprises. Our research specifically on IDSs constructed using machine learning (ML) techniques. In our research, we have conducted an in-depth analysis of the CICIDS2017 intrusion detection dataset, which serves as the foundation for training and evaluating our machine learning models. We have implemented several machine learning approaches, including Decision Trees (DT), Extra Trees, Random Forest, and XGBoost, to study their effectiveness in detecting intrusions and ensuring the security of computer systems. In our experiments, we explored the use of Principal Component Analysis (PCA) for feature selection and HyperOpt for optimization. Our findings revealed that the XGBoost classifier achieved the highest accuracy among all three other methods (DT,ET,RF) the methods, with an impressive accuracy rate of 99.331%. This suggests that XGBoost is particularly effective in our context. © 2024 IEEE.

关键词： Decision trees

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Joint Action Representation and Prioritized Experience Replay for Reinforcement learning in Large Discrete Action Spaces 23

Joint Action Representation and Prioritized Experience Repla...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wei, Xueyu Xue, Wei Zhao, Wei Shen, Yuanxia Yu, Gaohang School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Department of Mathematics School of Science Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450398633

In dealing with the large discrete action spaces, a joint action representation and prioritized experience replay method is proposed in this paper, which consists of three modules. In the first module, we use the k-nearest neighbor method to reduce the dimensionality of the original action space, generating a compact action space, and then the critic network is introduced to further evaluate and filter this compact space to obtain the optimal action. Note that the optimal action may have inconsistency with the actual desired action. Then in the second module, we introduce a multi-step update technique to reduce the training variance when storing data in the replay buffer. In the third module, considering the existence of correlation between samples when sampling data, we assign the corresponding weight to the sample experience by calculating the absolute value of temporal difference error and use such a non-uniform sampling method to prioritize the samples for sampling. Experimental results on four benchmark environments demonstrate the effectiveness and efficiency of the proposed method in dealing with the large discrete action spaces. © 2023 ACM.

关键词： Reinforcement learning

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Cost-Sensitive Ensemble learning for Venture Capital Exit Prediction 24

Cost-Sensitive Ensemble Learning for Venture Capital Exit Pr...

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16th international conference on machine learning and computing (ICMLC)

作者： Fang, Heng Ma, Ding Wuhan Univ Technol Sch Econ Wuhan Peoples R China

ISBN: (纸本)9798400709234

This study presents a methodologically robust framework aimed at refining the precision of Venture Capital (VC) exit prediction. Focused on addressing the inherent data imbalance in VC exit forecasting, our approach strategically integrates both data-level and algorithm-level methodologies. Data-level methods encompass data sampling and cost-sensitive learning to mitigate class imbalance, while algorithm-level techniques employ Stacking and Cascading strategies to alleviate bias towards the majority class. The research specifically aims to forecast VC exits within the Chinese landscape through Initial Public Offering (IPO) or Merger and Acquisition (M&A) events spanning the years 2014 to 2017, with a forecasting horizon extending into the subsequent four years. Our findings highlight that an optimal algorithmic fusion of cost-sensitive learning with a Stacking Classifier outperforms traditional models such as Logistic Regression, Extra Tree, XGBoost, and Random Forest. This optimized approach achieves a superior recall rate of 81.9% coupled with an F2-score of 80.8%. Utilizing SHAP (Shapley Additive Explanations) analysis, we delve into the significance of explanatory features, elucidating the key factors influencing VC exit determinations. This study underscores the efficacy of employing cost-sensitive learning methods and resampling techniques in conjunction with ensemble learning to enhance the accuracy of VC exit forecasting. The insights provided serve as valuable references for practitioners involved in the management of VC projects, emphasizing the importance of robust methodologies in predictive modeling.

关键词： cost-sensitive learning ensemble learning VC exit forecasting SHAP analysis

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Enhancing Differential Evolution Algorithm: Adaptation for CEC 2017 and CEC 2021 Test Suites 9

Enhancing Differential Evolution Algorithm: Adaptation for C...

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9th international conference on soft computing and machine Intelligence (ISCMI)

作者： Salgotra, Rohit Mirjalili, Seyedali Gandomi, Amir H. Swansea Univ Dept Comp Sci Swansea W Glam Wales Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

ISBN: (纸本)9798350320886

Differential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealingbased scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate ( CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.

关键词： Differential evolution LSHADE CEC 2017 CEC 2021 numerical optimization

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AIOps and Sustainability: Transforming Data Centers for a Greener Future

AIOps and Sustainability: Transforming Data Centers for a Gr...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Joshi, Subrahmanya Vinayak Serebryakov, Sergey Nanjundaiah, Deepak Hegde, Tejas Hewlett Packard Enterprise HPC AI & Labs United States

ISBN: (纸本)9798350355543

Enterprise and high-performance computing (HPC) data centers are dealing with thousands of sensor metrics and associated data. A top-end target for exascale machines is 10 million data points per second. The escalating volume and speed of data generation are making management of such systems more difficult, and outages are increasing. The latest Uptime Institute's Outage Analysis report [1], published in June 2022, states that 30% of all outages in 2021 lasted more than 24 hours, a disturbing increase from 8% in 2017. While equipment is idle during downtime, it often continues to consume power, especially for cooling systems. This leads to wasted energy and higher operational costs. We propose an AIOps solution that uses advanced data analytics, machine learning, and deep learning methods to develop automated and advanced anomaly detection and predictive tools for data centers. They perform at scale and speed, and improve data center resiliency and energy efficiency, thereby promoting the sustainability of data centers. © 2024 IEEE.

关键词： AIOps Artificial Intelligence Data centers Deep learning HPC machine learning Sustainability

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Quantum machine learning for Network Intrusion Detection Systems, a Systematic Literature Review 2

Quantum Machine Learning for Network Intrusion Detection Sys...

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2nd IEEE international conference on AI in Cybersecurity, ICAIC 2023

作者： Nicesio, Otavio Kiyatake Leal, Adriano Galindo Gava, Vagner Luiz Institute for Technological Research Artificial Intelligence and Analytics Department São Paulo Brazil

ISBN: (纸本)9798350396966

Quantum computing presents potential advantages over classical computing in terms of computational complexity. Therefore, it is expected for quantum machine learning applications to have improvements in capacity and learning efficiency over classical machine learning methods. This paper aims to present a Systematic Literature Review of articles published between 2017 and 2022, identifying, analyzing, and comparing different proposals of quantum machine learning applications for network intrusion detection systems (IDS). This study focused on identifying papers that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms found were variational hybrid quantum-classical, with models based on quantum support vector machines and quantum neural networks. Benefits compared to classical models were observed and described, such as reduced training time and improved classification accuracy for attacking traffic. © 2023 IEEE.

关键词： Support vector machines

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