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IEEE international conference on Software Quality, Reliability and security (QRS)

作者： Shwetha Gowdanakatte Mahmoud Abdelgawad Indrakshi Ray Department of Systems Engineering Colorado State University Fort Collins Colorado USA Department of Computer Science Colorado State University Fort Collins Colorado USA

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

The increasing growth of threats to Industrial Control systems (ICS) in the energy sector puts this critical infrastructure at high risk. Consequently, holistic approaches are needed to assess the criticality of ICS assets, identify relevant security threats, and develop mitigation techniques to make the energy critical infrastructure cyber-resilient. This paper presents a methodology for analyzing the criticality and resiliency of ICS assets by assessing the impact caused by attacks on such assets. Our approach consists of modeling the ICS architecture in a form that is suitable for analysis. We use Coloured Petri Nets (CPN) for formal representation and analysis – CPN is supported by automated tools for analysis and it has been used for verification of real-world systems. We use Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege (STRIDE) for evaluating the threats in the ICS architecture. We use Microsoft Threat Modeling Tool (MTMT) for ICS threat modeling that classifies the threats into the categories defined in STRIDE. Based on the type of threat on each asset and its impact on the entire ICS, we rank the asset’s criticality. The threat modeling framework assesses the criticality and resiliency of tangible and intangible assets, thus addressing the gap in the current research. Threat models are also converted into CPN models. The CPN models of the ICS architecture and the threat model are then composed. The methodology then verifies the resulting CPN. This verification explores the system states where the ICS cannot resist the attacks and identifies the ICS’s critical assets that have been compromised. The methodology is applied to a wind farm system comprising many distributed subsystems connected via various networks. The result shows that the methodology is practical for the ICS verification and assets criticality assessment, providing recommended mitigations to construct a robust ICS.

关键词： Threat modeling Prevention and mitigation Industrial control Computational modeling Petri nets Wind farms Critical infrastructure

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BlockChain Based Chat Application

BlockChain Based Chat Application

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Emerging Technology (INCET), international conference for

作者： Ishan Lokhande Nilesh Deotale Bharat Mali Sneha Chauhan Jayesh Dhuri Depertment of Computer Engineering St. John College of Engineering and Management Palghar India

Peer-to-peer networks are used by decentralized applications to ensure that no network failure can be caused by a failing central node. Today's environment, where holding data on a centralized server might be unsafe and expensive, requires a decentralized application for communication and resource sharing. We developed a programme that uses blockchain technology and decentralized networks to guarantee user privacy and make it simple for blockchain administrators to track down the source of false rumors that circulate throughout the network. The workload for file synchronization and sharing is distributed among a service, a client and a provider. This paper focuses on how decentralization could assist in successfully overcoming such problems. Decentralization would result in an increase in workers who will transfer data from a client node to a server node. When sharing data, blockchain would guarantee security. The suggested model can be utilized to move large data in a comparatively faster time frame. Additionally, the study elaborates on the design implications and factors, a user must comprehend in order to use this model properly and to obtain better outcomes when deployed onto the systems.

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TOSA: Tolerating Stuck-At-Faults in Edge-based RRAM Inference Accelerators

TOSA: Tolerating Stuck-At-Faults in Edge-based RRAM Inferenc...

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international conference on Parallel and distributed systems (ICPADS)

作者： Shaofeng Zhao Bo Liu Fang Wang Dan Feng Xiang Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyberspace Security Zhengzhou University Zhengzhou China

Applications powered by Deep Neural networks (DNNs) are shifting from cloud to edge computing due to the promising performance efficiency of Resistive Random Access Memory (RRAM) devices. However, modern RRAM devices have various possible faults, including hard and soft faults. Existing RRAM-based deep learning systems lack a comprehensive fault tolerance approach, leading to safety risks in deploying edge-end DNN inference. In this paper, we propose an effective fault recovery framework called TOSA to safeguard edge-based DNN accelerators from Stuck-At-Faults (SAFs). Two novel ideas are presented to enhance reliability in edge-based RRAM inference accelerators: (1) SAFs-aware priority-based bit-memristor mapping algorithm to match integrated SAF variations and DNN’s defect tolerance, (2) a new retraining method called joint retraining to recover model accuracy from multiple faulty edge devices. We thoroughly evaluate TOSA on multiple DNN models, demonstrating its effectiveness in reducing costs and improving efficiencies. Experimental results show that TOSA achieves a 45.28× speedup when compared to the per-device retraining methods while preserving inference accuracy better than the latest competitor, making it a reliable architecture for accelerating DNN inference on edge devices.

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Detection and Prevention of Advanced Persistent Threat (APT) Activities in Heterogeneous networks using SIEM and Deep Learning

Detection and Prevention of Advanced Persistent Threat (APT)...

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international conference on Computing and Networking Technology (ICCNT)

作者： V S Tharunika Shridhar T K Veeresh Senthil Kumar Thangavel Karthik Srinivasan Sulakshan Vajipayajula Anjali Tibrewal Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India IBM Security International Business Machines India Pvt. Ltd.

distributed Denial of Service (DDOS) in IOT based systems has become a vital consideration especially since the quintessential development of IOT based gadgets including small Personal Digital Assistants (PDA) to large computing systems. The stupendous and prolonged presence of such innovations have attracted potential attackers, encouraging them to carry out cyber-attacks and data theft. The key objective of this research is to detect and mitigate botnet-based distributed denial of service (DDoS) attacks in IoT networks. Our proposed model addresses the issue concerning threats from bots. Multiple machine learning models such as K-Nearest Neighbour (KNN), K-Means clustering, Fuzzy clustering and Deep learning models such as Conventional Neural Network (CNN), Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) were used to arrive at the model, wherein the model is trained by Bot-Iot dataset. Various preprocessing methods were equipped and Performance-based selection was done to choose algorithms from a collection using the reference point, (i.e) accuracy percentage. Feature selection and Synthetic minority oversampling technique (SMOTE) were also incorporated alongside Machine Learning (ML) algorithms. The results of the model indicates that the proposed architecture can effectively detect botnet-based attacks and also can be extended with corresponding architectures for unfolding attacks.

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An Exploration of Evaluating the Performance of Malware Detection in the Cloud Environment

An Exploration of Evaluating the Performance of Malware Dete...

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international conference on computer Communication and Informatics (ICCCI)

作者： Charan Nivas Reddy Kaipu Gopu Karthik Konala Sree Umadevi Koneru Kamal Koushik T Pavan Kumar S. Kavitha Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

Numerous businesses now adopt cloud computing, putting their server, storage, and application resources on a network in the cloud somewhere online. Data from the user is stored on cloud at a low price, making cloud computing the most rapidly developing technologies. In a nutshell, cloud technology provides users with the access to massive infrastructures for low latency via a distinct middleware that is comparable to Grid and HPC computing, both of which are currently in use. Over the years, these kinds of systems have become more and more popular since they offer a setting in which to host scalable applications. But rather than offering customers a common platform, the cloud network is vulnerable to several security issues, including malware infections, DDoS assaults, SQL injection, and even more complex attacks like zombie botnets. We describe our research on security challenges and virus spread in cloud networks in this paper. However, the detection capabilities of conventional host-based antivirus software are constrained, and theseprograms frequently miss threats; recent assaults are becoming more complicated and evasive of conventional security measures. To understand cloud security and use cutting-edge malware detection techniques to safeguard cloud, malware detection and research on these approaches are described in this article. Because the resources in a cloud are delivered to the user in the form of VM’s, they are susceptible to distributed denial of service attacks, malware exploits,and VM Escape-based attacks. This paper discusses the various detection techniques and the most effectiveones. The discussion of each method will be covered from a broad perspective that also focuses on reliable solutions, future directions, etc. The details are outlined in our article

关键词： Cloud computing computer viruses SQL injection Software measurement Servers Reliability Middleware

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Internet of Things Protocols (IoTp) Literature Review 23

Internet of Things Protocols (IoTp) Literature Review

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Ali Alamery Mohanad Abd Shehab Omar Abdulkareem Mahmood Yousif I Hammadi Ahmed Aziz Mohammed Saleh Ali Muthanna Balad Al-hadeth group company Balad Al-hadeth group company Iraq Electrical Engineering Department Mustansiriyah University Iraq Department of Communications Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Institute of Computer Technologies and Information Security Southern Federal University Russia

ISBN: (纸本)9798400709036

Considering today's variety of technologies and devices connected to the network, a huge number of manufacturers, there are many problems of their interaction and the need to create and adopt specialized communication standards and protocols. This article is devoted to the description of various protocols proposed by the IEEE, IETF and ITU for use in the framework of the concept of the Internet of Things. The article includes a description of the protocols of the application, transport, network and data link layers of the TCP / IP model. It also discusses protocols designed specifically to meet IoT requirements. A brief description of the protocols and the principle of operation of each of them are given. This review article contributes to a more correct choice of protocols when building a network, detecting and managing devices, and establishing interaction between objects of the Internet of Things.

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Guest Editorial: Softwarized Networking for Next Generation Industrial Cyber-Physical systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第8期17卷 5506-5510页

作者： Garg, Sahil Wang, Honggang Granelli, Fabrizio Li, Hongwei Univ Quebec Ecole Technol Super Montreal PQ H3C 1K3 Canada Univ Massachusetts Dartmouth N Dartmouth MA 02747 USA Univ Trento I-38122 Trento Italy Univ Elect Sci & Technol China Chengdu 610054 Peoples R China

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

关键词： Special issues and sections Cyber-physical systems Informatics Data analysis Deep learning Resource management Industrial Internet of Things computer architecture Blockchain Real-time systems Cloud computing

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A design of dynamic rate aware classified key for network security in wireless sensor network through optimized distributed secure routing protocol

A design of dynamic rate aware classified key for network se...

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Artificial Intelligence and Smart Communication (AISC), international conference on

作者： A. Shameem Surendra Kumar Shukla Mohit Tiwari Dharam Buddhi Yashpal Singh Ushasree R AMET Business School AMET University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand Department of Computer Science andEngineering Jain Deemed to be University Bengaluru Karnataka India Department of MCA Dayanad Sagar Academy of Technology and Management Bangalore India

Data must be transferred in a secure manner in order to transmit information through an industrial network, creating a high security root. A lot of study has been done on wireless internet network routing security issues. Routing security in wireless communication systems (WMN) have attracted a lot of interest. Although there are several solutions to this issue, they all have shortcomings that make it difficult to update WMN security. This problem's suggested solution is a dynamically frequency knowing categorized core differential safe routing (DRCKDS). The sensor nodes of this system keep an eye on a variety of environmental variables, including energy, transmission speed, success rate, and others. To determine the most direct routes between locations, this is employed. The method is used to calculate each route's secure route score (SRM). The optimal transmission path is chosen based on the Predictive control value. Similar to that, the technique creates a large number of encryption data and transmits them across the chosen transmission line. By using the same code that was used to send the packet over the chosen route, the data was encrypted. Both cybersecurity quality and WMN customer satisfaction are enhanced by the technique.

关键词： Wireless communication Wireless sensor networks Power transmission lines Heuristic algorithms Customer satisfaction Network security Routing

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Real-Time Anomaly Detection in 5G networks Through Edge Computing

Real-Time Anomaly Detection in 5G Networks Through Edge Comp...

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IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Riaz Shaik Dara Raju Prakash Chandra Behera Ravindra Changala S. Suma Christal Mary A. Balakumar Department of CSE KL University Vaddeswaram Andhra Pradesh India Dept. of Computer Science & Engineering Vignana Bharathi Institute and Technology Aushapur Ghatkesar India Department of Science St. Claret College Bangalore India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology Panimalar Engineering College Poonamalle Chennai Department of ECE K. Ramakrishnan College Of Engineering samayapuram Trichy India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Strong anomaly detection techniques are becoming more and more necessary as 5G networks develop in order to maintain network performance, security, and dependability. This study leverages the capabilities of Mobile Edge Computing (MEC) to present a novel method for anomaly detection in 5G networks. By processing data closer to the network edge, the integration of MEC offers an efficient and decentralized architecture that lowers latency and improves real-time detection capabilities. The distributed module takes advantage of its close proximity to network devices by using sophisticated algorithms for anomaly detection, which are implemented at the mobile edge. The system can quickly detect abnormalities from typical network activity by utilizing capabilities including Flow Collection, Anomaly Symptom Detection, and Network Anomaly Detection. The distributed module provides anomalous information to the centralized decision-making module for thorough examination. It takes into account variables like resource use and network traffic and integrates this data with metrics gathered from monitoring modules. Because of its adaptive characteristics, the system may expand anomaly detection components, enhance detection functions, and modify virtualized resources in response to shifting network circumstances. The evaluation findings reveal that the suggested anomaly detection method performs well in 5G networks, with decreased false positives, increased responsiveness, and better flexibility to changing network conditions. Using MEC not only makes anomaly detection more effective, but it also fits in with the 5G design, which makes it a viable option for protecting the upcoming generation of communication networks. It obtains 95% accuracy in classification. The suggested approach has proven to be resilient in handling security issues by producing outcomes that are either comparable to or better than those attained by other techniques that have been previously presented in the s

关键词： Multi-access edge computing 5G mobile communication Image edge detection Signal processing algorithms Telecommunication traffic Signal processing Real-time systems

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Ensuring Privacy and security of IoT networks Utilizing Blockchain and Federated Learning

Ensuring Privacy and Security of IoT Networks Utilizing Bloc...

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international conference on Future Internet of Things and Cloud (FiCloud)

作者： Md Mamunur Rashid Piljoo Choi Suk-Hwan Lee Jan Platos Young Huh Ki-Ryong Kwon Department of Artificial Intelligence Convergence Pukyong National University Busan Republic of Korea Department of Computer Engineering Donga University Busan Republic of Korea Department of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Korea Smart Medical Device Industry Promotion Foundation Seoul Republic of Korea

The Internet of Things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. The other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. The experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks.

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