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Deep Learning Approaches for Intrusion Detection with Emerging Cybersecurity Challenges

Deep Learning Approaches for Intrusion Detection with Emergi...

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Sustainable Communication networks and Application (ICSCNA), international conference on

作者： S. Hemalatha M. Mahalakshmi V. Vignesh M. Geethalakshmi D. Balasubramanian Anand A. Jose Department of Electrical and Electronics Engineering St. Joseph's Institute of Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore India Department of Mathematics KCG College of Technology Chennai India Department of MCA Jaya Engineering College Chennai India Department of ECE KCG College of Technology Chennai Tamil Nadu India

Intrusion Detection systems (IDS) play a pivotal role in preventing computer networks from an ever-evolving landscape of cybersecurity threats. As cyberattacks become increasingly sophisticated and diversified, the need for robust and adaptable intrusion detection models has never been more critical. This study explores the application of Deep Neural networks (DNNs), Convolutional Neural networks (CNNs), and Recurrent Neural networks (RNNs) in the context of intrusion detection, particularly in addressing emerging cybersecurity challenges. The performance of deep learning (DL)-based IDSs is highly dependent on the dataset used and no benchmark models for intrusion detection have been found. In this work, a recent dataset CIC-DDoS-2019 is used for the detection of DDOS (distributed deny of services). This work underscores the importance of adopting DL techniques, including DNNs, CNNs, and RNNs, for intrusion detection in the face of emerging cybersecurity challenges. It highlights the potential of these neural network architectures to enhance network security, adapt to evolving threats, and mitigate the risks posed by modern cyber adversaries. Moreover, it encourages further research and development in this field to stay ahead of the ever-changing cybersecurity landscape.

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A Layered Taxonomy of Internet of Things Attacks 22

A Layered Taxonomy of Internet of Things Attacks

引用

Proceedings of the 6th international conference on Future networks & distributed systems

作者： Farag Azzedin Ibraheem Alhejri Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9781450399050

Internet of Things (IoT) is heavily affecting our daily lives in many domains, ranging from tiny wearable devices to large industrial systems. However, ensuring the security of IoT systems is really challenging due to the heterogeneous nature of the IoT environments. IoT has experienced unprecedented attacks that target not only businesses but also have a probability of endangering human lives. Therefore, providing a comprehensive classification of IoT attacks and the available defenses against them is of great importance. In this paper, we conduct a survey of IoT security attacks and provide a taxonomy to classify them based on different aspects. This comprehensive survey would be helpful for industry and academic researchers working to design secure IoT systems by analyzing which attacks have been investigated and which have remained unexplored.

关键词： Attacks

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An Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs

An Associated Random Neural Network Detects Intrusions and E...

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international Symposium on Modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Mert Nakıp Erol Gelenbe Polish Academy of Sciences (IITIS-PAN) Inst. Theoretical & App. Informatics Gliwice PL CNRS I3S Université Côte d’Azur FR Yaşar University Bornova Izmir TR King’s College London UK

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Cyberattacks, especially Botnet distributed Denial of Service (DDoS), increasingly target networked systems, compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages, which includes stopping the spread of malware, it is vital to identify compromised nodes and successfully predict potential attack paths. To this end, this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system, ARNN is trained online to minimize problem-specific multi-task loss so that it identifies compromised network nodes, while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset, showing that the method achieves a recall rate above 0.95 in estimating the network attack graph, and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs, can pave the way for enhancing security measures, and stopping Botnet DDoS attacks from spreading in networked systems.

关键词： Training Botnet Heuristic algorithms Neural networks Traffic control Prediction algorithms Multitasking Malware Telecommunications computer crime

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A Survey on distributed Denial of Service Attack - Detection Mechanisms and Mitigation Mechanisms in the Cloud Environment

A Survey on Distributed Denial of Service Attack - Detection...

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IEEE UP Section conference on Electrical computer and Electronics (UPCON)

作者： Chris Harry Kandikattu Sam Sangeeth Panguluri Sandeep Kumar Suneetha Bulla Abdul Raheem Shaik Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundations Vijayawada India

It is evident that problems associated with vast amounts of information occur with complicated systems and processes that act collaboratively to provide services. Also, other related issues such as security problems, cost optimization and confinement, deficiency of Assets or expertise, governance or supervision, conformity, handling several clouds, and many others have left a serious threat within the cloud environment. It can be framed that the voluminous data transmitted between various organizations and cloud service providers is a channel to induce such a threat. These threats can mainly be estimated as unsolicited and malicious sensitive data leaks to countless third-party bodies. Also termed as distributed Denial-of-Service (DDoS), there are these situations wherein a targeted server's traffic is disturbed by creating some internet traffic within its surroundings. According to a survey conducted by Arbor networks, it is revealed that roughly 1200 DDoS attacks occur every other day. Therefore, this paper aims to offer a comprehensive analysis of the DDoS attack within the cloud setting, including strategies for identifying and mitigating it.

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A controllable cross-domain anonymous authentication system based on blockchain 6

A controllable cross-domain anonymous authentication system ...

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6th international conference on Futuristic Trends in networks and Computing Technologies, FTNCT 2024

作者： Cheng, Cheng Zhong, Chen Jiang, Weijian School of Cyberspace Security Hainan University Haikou570228 China

There are often multiple independent information systems within an enterprise or between different enterprises. However, due to the differences in security policies and data management methods among different management domains, they often do not trust each other. They are reluctant to share sensitive data, which forms information silos. In order to better solve these challenges and break down these information silos, we propose a blockchain-based controllable cross-domain authentication scheme. The system uses a three-tier CA-RA-user structure and leverages blockchain to establish distributed trust between multiple CA domains. In the process of cross-domain authentication, we combine pseudonym technology and PS signature technology to realize user anonymous authentication and identification. At the same time, users are allowed to selectively disclose some attribute information according to their own needs to ensure that their identity information will not be overexposed. In addition, we also use group signature technology to achieve controllable regulation. When malicious behavior is detected, the CA of the domain where the malicious user resides can monitor the anonymous certificate and identify the malicious user. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Information management

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Demo Abstract: Hands-On IoT Education with Mobile Devices 20

Demo Abstract: Hands-On IoT Education with Mobile Devices

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20th ACM/IEEE international conference on Information Processing in Sensor networks (IPSN)

作者： Jean, Devin Stein, Gordon Ledeczi, Akos Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781450380980

distributed computing, computer networking, cyber-physical systems and the Internet of Things (IoT) are all around us, yet very little of the underlying concepts are being taught in introductory computer science courses in K12. Teaching IoT is especially problematic since it requires hardware with its own cost and maintenance requirements that many under-resourced schools cannot afford. However, smartphones are commonplace networked devices that boast a wide range of sensors. With the right approach, they can be used to introduce many advanced computing concepts even to novices. We have created a mobile app, PhoneIoT, which makes all available sensors accessible to the user's computer through NetsBlox, a block-based educational programming environment specializing in networking and distributed computing. Accessing PhoneIoT uses the same simple networking primitives as other NetsBlox services, so no additional knowledge is required. PhoneIoT makes it possible to collect sensor data, build a custom graphical interface on the device and receive corresponding events, all without leaving your browser running NetsBlox. This demonstration will showcase a few simple projects such as an exercise tracking app and how to turn your phone into a game controller.

关键词： computer science education internet of things networking sensors

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Mind the Amplification: Cracking Content Delivery networks via DDoS Attacks 1

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16th international conference on Wireless Algorithms, systems, and Applications (WASA)

作者： Li, Zihao Meng, Weizhi Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (数字)9783030861308

ISBN: (纸本)9783030861308;9783030861292

A content delivery network (CDN) refers to a geographically distributed network of web servers, aiming to enhance the website performance and reliability, i.e., providing fast delivery of the Internet content. CDN is often part of a mature enterprise level network, but due to the distributed nature, CDN may be vulnerable to a distributed denial-of-service attack (DDoS), in which the cyber-attackers try to flood the victim with overloaded traffic or requests. In the literature, the impact of DDoS on CDN has not been widely studied. Motivated by this challenge, in this work, we emulate a CDN environment and investigate the effect of a particular DDoS attack on the environment. It is found that free CDN clusters could be destroyed by only several Gigabyte traffic. In the end, we also discuss some potential solutions to help defend against DDoS attacks.

关键词： Content delivery network distributed Denial-of-Service Attack Website server Network security Emulation and evaluation

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Analysis of security Problems in Groups of Intelligent Sensors 12th

Analysis of Security Problems in Groups of Intelligent Senso...

引用

12th international conference on Swarm Intelligence (ICSI)

作者： Grigoryan, Karen Olefirenko, Evgeniya Basan, Elena Lapina, Maria Mecella, Massimo Southern Fed Univ Chekova St 2 Taganrog 347922 Russia North Caucasus Fed Univ Pushkina St 1 Stavropol 355000 Russia Sapienza Univ Rome Via Ariosto 25 I-00185 Rome Italy

ISBN: (纸本)9783030787424;9783030787431

Today, the creation of intelligent sensors became possible due to the development of the hardware base, the use of small boards, where the processor, memory, and network interfaces can be placed. Examples of such cards can be Raspberry Pi, Arduino, and others. These devices can be used to connect various sensors to them depending on the tasks. Today, there are many protocols for the exchange of messages between such sender devices, which ultimately leads to the creation of distributed networks with distributed functionality. Such systems can be decision-making and are like swarm intelligence, where each device performs its functions, but together they are a single system. This study will examine the information security issues of such systems. An analysis of threats and vulnerabilities for intelligent sensor systems was carried out. Demonstrated an attack on the secure ZigBee protocol, which is often used to create a network between smart sensors. The use of lightweight cryptography to minimize risks is proposed.

关键词： Internet of Things (IoT) Cipher Cryptography LWC SPECK Tiny encryption algorithm

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On Large-Scale Qualitative Spatio-Temporal Constraint Redundancy Removal 16

On Large-Scale Qualitative Spatio-Temporal Constraint Redund...

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16th IEEE international conference on Intelligent systems and Knowledge Engineering, ISKE 2021

作者： Hu, Qiyuan Gao, Yang Long, Zhiguo Wang, Hongjun Li, Tianrui Sioutis, Michael Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China University of Bamberg Faculty of Information Systems and Applied Computer Sciences Bamberg Germany

ISBN: (纸本)9781665405539

Qualitative spatio-temporal representation and reasoning is an important research direction in the field of artificial intelligence, and qualitative constraint networks are a key tool for representing and reasoning with spatio-temporal knowledge. Previous studies have pointed out that qualitative constraint networks often contain redundant constraints. This not only results in a waste of storage and transmission resources, but can also be a bottleneck in various algorithms and applications that are based on qualitative constraint networks. Existing research proposed several algorithms to deal with redundant constraints, but their time complexities are relatively high, which is very unfavorable when dealing with large-scale data that are common nowadays. This paper proposes a new model based on distributed computation to deal with redundant constraints efficiently. Specifically, this paper theoretically proves the feasibility of the distributed model for this problem, proposes different optimization algorithms for distributed task allocation, and evaluates the performance of the distributed model through a variety of experiments on real-world data sets. The results show that the distributed model can significantly improve the processing efficiency of simplifying representations of qualitative spatio-temporal information on large-scale data sets, and that the distributed solution to the problem of simplifying representations can also improve data privacy, which can contribute to big data processing and data security protection in qualitative spatiotemporal reasoning research. © 2021 IEEE.

关键词： Redundancy

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The Markov Model of the Needham-Schroeder Protocol 25th

The Markov Model of the Needham-Schroeder Protocol

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25th international conference on distributed computer and Communication networks, DCCN 2022

作者： Shukmanova, A.A. Yermakov, A.S. Paltashev, T.T. Mamyrova, A.K. Ziro, A.A. Kazakh National Technical University Almaty Kazakhstan Caspian University Almaty Kazakhstan ITMO University Saint Petersburg Russia Turan University Almaty Kazakhstan

ISBN: (纸本)9783031306471

A user-friendly Markov model of procedures for the Needham-Schroeder information security algorithm is developed. To implement the development, auxiliary system models of three asynchronous execution algorithms are used, one of which considers systems of processing-synchronous input and processing-asynchro-nous output, while the other considers input-asynchronous processing and synchronous information output. A model of processing-asynchronous input and output is taken as the final model. Finally, the closed model of a three-phase queuing system is given. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： security of data

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