An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Almomani, Ammar; Alauthman, Mohammad; Albalas, Firas; Dorgham, Osama; Obeidat, Atef A.

doi:10.4018/978-1-7998-2460-2.ch053

Cited by 14 publications

(4 citation statements)

References 33 publications

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“…An attacker, by predicting the sequence numbers of a future packet in an active TCP session, can potentially inject malicious packets into the communication stream without either party's knowledge. This could allow unauthorized access to sensitive sessions, such as those for web applications or remote terminals [78], [79]. The vulnerability arises from the predictability of sequence numbers, which, in implementations where these numbers are not sufficiently randomized, can be guessed by an attacker through techniques like sniffing a few packets to infer the algorithm used for sequence number generation.…”

Section: Tcp Sequence Number Predictionmentioning

confidence: 99%

Performance, privacy and security issues at the TCP internet layer

Ogweno Jeremiah okeyo

2024

World J. Adv. Res. Rev.

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The rapid growth of internet-based services and applications has revolutionized the way we communicate, interact, and conduct business. However, this widespread connectivity also brings forth numerous challenges related to performance and privacy security at the internet layer. This paper aims to provide a comprehensive analysis of the various issues that arise in this context, highlighting their impact on user experience, data protection, and network efficiency, to address performance concerns; the research paper investigates the factors affecting internet Layer. Through extensive research studies, the paper examines the impact of network congestion, routing inefficiencies, and protocol limitations on the overall performance of internet-based services. This research delves into the vulnerabilities and risks associated with the internet layer. It investigates the potential threats posed by malicious actors, including hackers, data breaches, and unauthorized access to sensitive information. The findings of this research contribute to a comprehensive understanding of the interplay between performances and privacy security issues at the internet layer. By identifying the key challenges and potential solutions, this study provides valuable insights for network administrators, service providers, policymakers, and end-users to optimize performance while ensuring robust privacy and security measures. Ultimately, this research aims to foster a safer and more efficient internet ecosystem for all stakeholders.

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Section: Tcp Sequence Number Predictionmentioning

confidence: 99%

Performance, privacy and security issues at the TCP internet layer

Ogweno Jeremiah okeyo

2024

World J. Adv. Res. Rev.

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“…A S the backbone technology supporting modern intelligent mobile applications, Deep Neural Networks (DNNs) represent the most commonly adopted machine learning technique and have become increasingly popular. Benefited by the superior performance in feature extraction, DNN have witnessed widespread success in domains from computer vision [2], speech recognition [3] to natural language processing [4] and big data analysis [5]. Unfortunately, today's mobile devices generally fail to well support these DNN-based applications due to the tremendous amount of computation required by DNN-based applications.…”

Section: Introductionmentioning

confidence: 99%

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Zeng

Zhou

et al. 2020

IEEE Trans. Wireless Commun.

570

218

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As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What's worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent, a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in realworld deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regressionbased prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent's effectiveness in enabling ondemand low-latency edge intelligence.

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“…The intrusions that have occurred in any host machine of the local system are monitored by HIDS. When analyzing, the logging data of the host machine is utilized to test whether there are uncertainties from the normal behavior 8,9 . NIDS audits the network traffic over a network looking for a dubious activity that can be an attack or prohibited activity.…”

Section: Introductionmentioning

confidence: 99%

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

Bingu

Jothilakshmi

Srinivasu

2023

Concurrency and Computation

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SummaryThe intrusion detection process is considered an efficient mechanism that plays a prominent role in network security. Traditional approaches for attack detection, like signature‐based detection, are easily prone to a new variant of attacks. Hence, the most prominent solution for cloud security attacks is an intrusion detection system through deep learning (DL) techniques. The behavior‐based intrusion detection system has been constructed by depicting the cloud traffic as input attributes. The deep learning technique is preferred to extract and analyze the traffic attributes rather than machine learning. Hence, an unsupervised technique named deep autoencoder (AE) is used to extract a feature in this research work. The resultant features are then classified by three classifiers named convolutional neural network (CNN), recurrent neural network (RNN), and long‐short term memory (LSTM). The designed approach is trained and assessed using a real‐time cloud attack dataset, namely, CICIDS 2018. Experimental results are analyzed using quality metrics such as receiver operating characteristics (ROC) curve, precision, accuracy, recall, and F1‐measure. Also, the performance of the proposed method is analyzed with existing literature. Higher accuracy rates like 99.55%, 94.33%, and 99.08% are attained with CNN, RNN, and LSTM networks, which are comparatively higher than the existing approaches.

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An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Cited by 14 publications

References 33 publications

Performance, privacy and security issues at the TCP internet layer

Performance, privacy and security issues at the TCP internet layer

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

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