Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

A Low Complexity ML-Based Methods for Malware Classification

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

Email Filtering Using Hybrid Feature Selection Model

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem.Undoubtedly due to a large number of attacks around the world,the concept of intrusion detection has become very important.This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm.Furthermore,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature selection.The algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature set.At layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority value.Modifications are done on standard GA to perform optimization and to fit the proposed model.The Optimized GA is used in the training phase to assign a priority value for each feature set.Also,the priority values are categorized into three categories:high,medium,and low.Besides,the Optimized GA is used in the testing phase to select a feature set based on its priority.The feature set with a high priority will be given a high priority to be selected.At the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated F-Measures.The proposed model can learn and modify feature sets priority,which will be reflected in selecting features.For evaluation purposes,two well-known datasets are used in these experiments.The first dataset is UNSW-NB15,the other dataset is the NSL-KDD.Several evaluation criteria are used,such as precision,recall,and F-Measure.The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

A Survey on the Role of Complex Networks in IoT and Brain Communication

Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this paper.The benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication technologies.Heavy data traffic,huge capacity,minimal level of dynamic latency,etc.are some of the future requirements in 5G+and 6G communication systems.In emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain communication.In this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain communication.Here,we measure the state of the complex system through observability.Using 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of neurons.When IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be lost.Similarly,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and connections.Therefore,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability conditions.In this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex network.To analyze the observability recovery,we can use a contraction detection algorithm with complex network properties.Our survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection algorithm.In addition,we discuss the scalability of IoT communication using 5G+/6G-based photonic technology.