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Ensemble-Based Approach for Efficient Intrusion Detection in network Traffic

Intelligent Automation & Soft Computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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IoT-Based Smart Beehive Monitoring System

IoT-Based Smart Beehive Monitoring System

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Advancements in Computing (ICAC), International Conference on

作者： Nisal Liyanage Chamindra Attanayaka Tarinya Perera Daniru Neilkumara Isuru Sri Bandara Lakshman Chandrasiri Department of Computer Science and Data Science NSBM Green University Town Homagama Sri Lanka Department of Computer Security and Network Systems NSBM Green University Town Homagama Sri Lanka Department of Electrical Electronic and Systems Engineering NSBM Green University Town Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies.

关键词： Temperature sensors Temperature measurement Weight measurement Productivity Humidity measurement Humidity Sensor systems Real-time systems Monitoring Intelligent sensors

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MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization

MEAT: Median-Ensemble Adversarial Training for Improving Rob...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China Department of Computer Science and Engineering Yuan Ze University Taiwan

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which severely affects the generalization performance. Empirically, in the late phases of training, the AT becomes more overfitting to the extent that the individuals for weight averaging also suffer from overfitting and produce anomalous weight values, which causes the self-ensemble model to continue to undergo robust overfitting due to the failure in removing the weight anomalies. To solve this problem, we aim to tackle the influence of outliers in the weight space in this work and propose an easy-to-operate and effective Median-Ensemble Adversarial Training (MEAT) method to solve the robust overfitting phenomenon existing in self-ensemble defense from the source by searching for the median of the historical model weights. Experimental results show that MEAT achieves the best robustness against the powerful AutoAttack and can effectively allievate the robust overfitting. We further demonstrate that most defense methods can improve robust generalization and robustness by combining with MEAT.

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An Android Malware Detection Approach Based on Static Feature Analysis Using Machine Learning Algorithms

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Procedia computer Science 2022年 201卷 653-658页

作者： Ahmed S. Shatnawi Qussai Yassen Abdulrahman Yateem Department of Computer Information Systems Jordan University of Since and Technology Irbid Jordan Department of Network and Security Engineering Jordan University of Since and Technology Irbid Jordan

In the past decade, mobile devices became necessary for modern civilization and contributed directly to its development stages in defining mobile information access. Nonetheless, along with these rapid developments in modern mobile devices, security issues rise dramatically, and malware is the most concerning of all. Therefore, many studies and research are still trending in this spectrum, using Machine Learning approaches to prevent and reduce malware’s impact. This paper seeks to add to what is already a foundation of various malware detection efforts by presenting a static base classification approach for malware detection based on android permissions and API calls. This approach is based on three well-known Machine Learning algorithms, Support Vector Machines (SVM), K-nearest neighbors (KNN), and Naive Bayes (NB) against a comprehensive new Android malware dataset (CICInvesAndMal2019), in pursuit of achieving high malware detection rates and contribution to the efforts and studies in protecting the development of mobile information. access.

关键词： Mobile Information Access Android Malware Malware Detection Machine Learning SVM KNN Naive Bayes CICInves Mal2019 Dataset

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MEAT: MEDIAN-ENSEMBLE ADVERSARIAL TRAINING FOR IMPROVING ROBUSTNESS AND GENERALIZATION

arXiv

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arXiv 2024年

作者： Hu, Zhaozhe Yin, Jia-Li Chen, Bin Lin, Luojun Chen, Bo-Hao Liu, Ximeng Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou350108 China College of Computer Science and Big Data Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering Yuan Ze University Taiwan

关键词： network security

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Fault Tolerant Neural Control Barrier Functions for Robotic systems under Sensor Faults and Attacks

Fault Tolerant Neural Control Barrier Functions for Robotic ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hongchao Zhang Luyao Niu Andrew Clark Radha Poovendran Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. Louis MO Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Safety is a fundamental requirement of many robotic systems. Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems. However, the effectiveness of these approaches highly relies on the choice of CBFs. Inspired by the universal approximation power of neural networks, there is a growing trend toward representing CBFs using neural networks, leading to the notion of neural CBFs (NCBFs). Current NCBFs, however, are trained and deployed in benign environments, making them ineffective for scenarios where robotic systems experience sensor faults and attacks. In this paper, we study safety-critical control synthesis for robotic systems under sensor faults and attacks. Our main contribution is the development and synthesis of a new class of CBFs that we term fault tolerant neural control barrier function (FT-NCBF). We derive the necessary and sufficient conditions for FT-NCBFs to guarantee safety, and develop a data-driven method to learn FT-NCBFs by minimizing a loss function constructed using the derived conditions. Using the learned FT-NCBF, we synthesize a control input and formally prove the safety guarantee provided by our approach. We demonstrate our proposed approach using two case studies: obstacle avoidance problem for an autonomous mobile robot and spacecraft rendezvous problem, with code available via https://***/HongchaoZhang-HZ/FTNCBF.

关键词： Space vehicles Sufficient conditions Fault tolerance Neural networks Fault tolerant systems Robot sensing systems Safety

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Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

arXiv

引用

arXiv 2023年

作者： Ali, Mohammad Liu, Ximeng The Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran The Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named O2DI, that provides the following services: i) using O2DI, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly;ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique;iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method;iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of O2DI. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches. © 2023, CC BY.

关键词： Inspection

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A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective

arXiv

引用

arXiv 2023年

作者： Seyedi, Zahra Rahmati, Farhad Ali, Mohammad Liu, Ximeng Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme’s security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. © 2023, CC0.

关键词： Game theory

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Comparative Analysis between Supervised and Anomaly Detectors Against Electricity Theft Zero-Day Attacks

Comparative Analysis between Supervised and Anomaly Detector...

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Telecommunications Conference (ITC-Egypt), International

作者： Mahmoud M. Badr Mohamed Baza Amar Rasheed Hisham Kholidy Sherif Abdelfattah Tarannum Shaila Zaman Department of Network and Computer Security SUNY Polytechnic Institute Utica NY USA Department of Computer Science College of Charleston Charleston SC USA Department of Computer Science Sam Houston State University Huntsville TX USA Department of Computer Science and Information Systems Bradley University IL USA Department of Computer and Information Science SUNY Polytechnic Institute Utica NY USA

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Smart power grids are vulnerable to electricity theft cyber-attacks, where malicious consumers hack their smart meters (SMs) to down-scale electricity usage readings before reporting them to electric utility companies (EUCs). This serious problem causes billions of dollars in losses to the EUCs worldwide and threatens the power grid's stability. Several machine learning (ML)-based solutions have been advised in the literature for electricity theft detection. However, most existing works propose supervised detection approaches and only a few works propose anomaly detection approaches. Therefore, in this paper, we investigate the effectiveness of the two approaches in detecting electricity theft utilizing a dataset of real electricity usage readings. Specifically, to the best of our knowledge, this work represents the first attempt to implement a comparative analysis between the performance of supervised deep learning (DL) models and anomaly detection models against electricity theft zero-day cyber-attacks. Our experimental results indicate that while the supervised detectors have proven successful against known attacks, they fail to detect new attacks. Moreover, our results demonstrate the superior performance of the anomaly detectors compared to the supervised detectors in defending against electricity theft zero-day attacks.

关键词： Analytical models Electricity Detectors Power system stability Stability analysis Smart meters Telecommunications

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A Compositional Resilience Index for Computationally Efficient Safety Analysis of Interconnected systems 62

A Compositional Resilience Index for Computationally Efficie...

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62nd IEEE Conference on Decision and Control, CDC 2023

作者： Niu, Luyao Maruf, Abdullah Al Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA98195-2500 United States California State University Department of Electrical and Computer Engineering Los AngelesCA90032 United States McKelvey School of Engineering Washington University in St. Louis Electrical and Systems Engineering Department St. LouisMO63130 United States School of Cybersecurity and Privacy Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798350301243

Interconnected systems such as power systems and chemical processes are often required to satisfy safety properties in the presence of faults and attacks. Verifying safety of these systems, however, is computationally challenging due to nonlinear dynamics, high dimensionality, and combinatorial number of possible faults and attacks that can be incurred by the subsystems interconnected within the network. In this paper, we develop a compositional resilience index to verify safety properties of interconnected systems under faults and attacks. The resilience index is a tuple serving the following two purposes. First, it quantifies how a safety property is impacted when a subsystem is compromised by faults and attacks. Second, the resilience index characterizes the needed behavior of a subsystem during normal operations to ensure safety violations will not occur when future adverse events occur. We develop a set of sufficient conditions on the dynamics of each subsystem to satisfy its safety constraint, and leverage these conditions to formulate an optimization program to compute the resilience index. When multiple subsystems are interconnected and their resilience indices are given, we show that the safety constraints of the interconnected system can be efficiently verified by solving a system of linear inequalities. We demonstrate our developed resilience index using a numerical case study on chemical reactors connected in series. © 2023 IEEE.

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