FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems

Haider, Waqas; Moustafa, Nour; Keshk, Marwa; Fernandez, Amanda; Choo, Kim‐Kwang Raymond; Wahab, Abdul

doi:10.1016/j.cose.2020.101906

Cited by 25 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental analysis of GSLB-BDSR is conducted in CloudSim and Java programming language with IoT-based datasets ToNIoT [21], [22]. The results of GSLB-BDSR are compared with [1] and [2] for in-depth analysis of latency time, throughput, security response time, accuracy.…”

Section: Resultsmentioning

confidence: 99%

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Ayothi¹,

Duela²,

Ramesh³

et al. 2021

Preprint

View full text Add to dashboard Cite

Over the last decade, blockchain has been considered an encouraging solution to secure distributed ledgers. Moreover, with the introduction of a pseudonymous payment method without a centralized database or authoritative person, blockchain has also evolved as the future generation for online payment system. However, with the eruption of a large scale database, scalability has also become a demanding issue. In addition to the obstacle mentioned above, challenges like security and scalability stop accelerated adjustments for the development of smart cities. Without directing this essential scalability and privacy issue, such an encouraging method may not help develop smart cities. This paper bestows a measure to analyze both scalability and security aspects of existing blockchain methods with applications of smart city networks. The proposed method is known as Gradient Smart Load Balancer and Blockchain Dempster Shafer Reputation (GSLB-BDSR). Gradient Smart Load Balancer is designed so that even though with the increase in the number of participating sensors, the load is said to balance by applying gradient function, therefore ensuring scalability. Next, to cover the security aspect, with the aid of scalable blocks in the blockchain network, a Blockchain Dempster Shafer Reputation model is proposed. Evaluation outcomes of proposed security solutions outperform conventional solutions.

show abstract

Section: Resultsmentioning

confidence: 99%

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Ayothi¹,

Duela²,

Ramesh³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the GTBSS‐HDNN framework, the effectiveness of the proposed methodology is evaluated in terms of trust and two‐level privacy preservation. The ToN‐IoT dataset 48 and BoT‐IoT 47 datasets are evaluated with both the transformed and original dataset versions to evaluate the performance of the GTBSS‐HDNN model. Different attack and normal instances are identified using the GTBSS‐HDNN framework.…”

Section: Experimental Analysismentioning

confidence: 99%

An optimized hybrid deep neural network architecture for intrusion detection in real‐time IoT networks

Shobana

Shanmuganathan

Challa

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The Internet‐of‐Things (IoT) refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges like security, trustworthiness, reliability, confidentiality, and so on. To address those issues, we have proposed a novel GTBSS‐HDNN approach which hybridization of Group theory (GT), Binary Spring search (BSS) algorithm, and Hybrid deep neural network (HDNN). The proposed GTBSS‐HDNN approach effectively detects the intrusion in the IoT nodes. Initially, the privacy‐preserving technology was implemented using a Blockchain‐based methodology. Our proposed privacy‐preserving methods are divided into two parts. The first stage utilizes blockchain and the second stage involves Modified Independent Component Algorithm (MICA) to prevent intrusion attacks. The authentication of data is performed by blockchain‐based Enhanced Proof of Work (EPoW) and achieves better authentication. Furthermore, the experimental study is carried out using the ToN‐IoT dataset, which is used to evaluate the performance of our proposed work. To analyze the performance we have taken the performance metrics such as F1‐measure, Detection Rate, Precision, and Accuracy. The performance analyzes depict that the proposed method effectively preserves the accuracy and thereby avert the intrusion attacks. The proposed model achieved 95.3% accuracy, 96.54% precision, 95.23% recall, and 95.67% F‐score values on the ToN‐IoT dataset and 96.23% accuracy, 95.94% precision, 97.03% recall, and 96.70% F‐score results on the BoT‐IoT dataset.

show abstract

“…The obtained reduced features are used as an input to the CNN for the classification of normal and attack, and the blockchain is used for the authentication and privacy of the IIoT-based systems. Two prominent datasets namely ToN-IoT [32] and BoT-IoT [33,34] were used to evaluate the security mechanism since both datasets have various IoT-based attacks like DoS, Ransomware, normal vectors, MITM, and Theft as mentioned earlier, and are publicly available [35].…”

Section: Introductionmentioning

confidence: 99%

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Awotunde,

Gaber,

Prasad

et al. 2023

SCPE

View full text Add to dashboard Cite

The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.

show abstract

FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems

Cited by 25 publications

References 24 publications

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

An optimized hybrid deep neural network architecture for intrusion detection in real‐time IoT networks

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Contact Info

Product

Resources

About