Bigdata and IoT technologies are developing rapidly. Accordingly, consideration of network security is also emphasized, and efficient intrusion detection technology is required for detecting increasingly sophisticated network attacks. In this study, we propose an efficient network anomaly detection method based on ensemble and unsupervised learning. The proposed model is built by training an autoencoder, a representative unsupervised deep learning model, using only normal network traffic data. The anomaly score of the detection target data is derived by ensemble the reconstruction loss and the Mahalanobis distances for each layer output of the trained autoencoder. By applying a threshold to this score, network anomaly traffic can be efficiently detected. To evaluate the proposed model, we applied our method to UNSW-NB15 dataset. The results show that the overall performance of the proposed method is superior to those of the model using only the reconstruction loss of the autoencoder and the model applying the Mahalanobis distance to the raw data.

It is suggested in this paper that an LSIM model be used to find DDoS attacks, which usually involve patterns of bad traffic that happen over time. The idea for the model comes from the fact that bad IoTdevices often leave traces in network traffic data that can be used to find them. This is what the LSIM model needs to be done before it can spot attacks in real-time. An IoTattack dataset was used to test how well the suggested method works. What the test showed was that the suggested method worked well to find attacks. The suggested method can likely be used to find attacks on the Internet of Things. It s simple to set up and can stop many types of break-ins. This method will only work, though, if the training data are correct.LSIMmodel could be used to find attack detection who are breaking into the Internet of Things. Long short-term memory (LSIM) models are a type of AI that can find trends in data that have been collected over time. The LSIM model learns the difference patterns in network traffic data that are normal and patterns that show an attack. The proposed method to see how well it worked and found that it could achieve a precision of 99.4\%.

The traditional port smart gate ground scale line pressure detection system employs a centralized data training method that carries the risk of privacy leakage. Federated Learning offers an effective solution to this issue by enabling each port gate to locally train data, sharing only model parameters, without the need to transmit raw data to a central server. This is particularly crucial for ground scale line pressure detection systems dealing with sensitive data. However, researchers have identified potential risks of backdoor attacks when applying Federated Learning. Currently, most existing backdoor attacks are directed towards image classification and centralized object detection. However, backdoor attacks for Federated Learning-based object detection tasks have not been explored. In this paper, we reveal that these threats may also manifest in this task. To analyze the impact of backdoor attacks on this task, we designed three backdoor attack triggers and proposed three trigger attack operations. To assess backdoor attacks on this task, we developed corresponding metrics and conducted experiments on local datasets from three port gates. The experimental results indicate that Federated Learning-based object detection tasks are susceptible to backdoor threats.

The Internet of Things (IoT) heralds a innovative generation in communication via enabling regular gadgets to supply, receive, and percentage records easily. IoT applications, which prioritise venture automation, aim to present inanimate items autonomy; they promise increased consolation, productivity, and automation. However, strong safety, privateness, authentication, and recuperation methods are required to understand this goal. In order to assemble give up-to-quit secure IoT environments, this newsletter meticulously evaluations the security troubles and risks inherent to IoT applications. It emphasises the vital necessity for architectural changes.The paper starts by conducting an examination of security worries before exploring emerging and advanced technologies aimed at nurturing a sense of trust, in Internet of Things (IoT) applications. The primary focus of the discussion revolves around how these technologies aid in overcoming security challenges and fostering an ecosystem for IoT.

Nowadays, anomaly-based network intrusion detection system (NIDS) still have limited real-world applications; this is particularly due to false alarms, a lack of datasets, and a lack of confidence. In this paper, we propose to use explainable artificial intelligence (XAI) methods for tackling these issues. In our experimentation, we train a random forest (RF) model on the NSL-KDD dataset, and use SHAP to generate global explanations. We find that these explanations deviate substantially from domain expertise. To shed light on the potential causes, we analyze the structural composition of the attack classes. There, we observe severe imbalances in the number of records per attack type subsumed in the attack classes of the NSL-KDD dataset, which could lead to generalization and overfitting regarding classification. Hence, we train a new RF classifier and SHAP explainer directly on the attack types. Classification performance is considerably improved, and the new explanations are matching the expectations based on domain knowledge better. Thus, we conclude that the imbalances in the dataset bias classification and consequently also the results of XAI methods like SHAP. However, the XAI methods can also be employed to find and debug issues and biases in the data and the applied model. Furthermore, the debugging results in higher trustworthiness of anomaly-based NIDS.

With the increasing complexity of network attacks, traditional firewall technologies are facing challenges in effectively detecting and preventing these attacks. As a result, AI technology has emerged as a promising approach to enhance the capabilities of firewalls in detecting and mitigating network attacks. This paper aims to investigate the application of AI firewalls in network attack detection and proposes a testing method to evaluate their performance. An experiment was conducted to verify the feasibility of the proposed testing method. The results demonstrate that AI firewalls exhibit higher accuracy in detecting network attacks, thereby highlighting their effectiveness. Furthermore, the testing method can be utilized to compare different AI firewalls.

DDoS is considered as the most dangerous attack and threat to software defined network (SDN). The existing mitigation technologies include flow capacity method, entropy method and flow analysis method. They rely on traffic sampling to achieve true real-time inline DDoS detection accuracy. However, the cost of the method based on traffic sampling is very high. Early detection of DDoS attacks in the controller is very important, which requires highly adaptive and accurate methods. Therefore, this paper proposes an effective and accurate real-time DDoS attack detection technology based on hurst index. The main detection methods of DDoS attacks and the traffic characteristics when DDoS attacks occur are briefly analyzed. The Hurst exponent estimation method and its application in real-time detection (RTD) of DDoS attacks are discussed. Finally, the simulation experiment test analysis is improved to verify the effectiveness and feasibility of RTD of DDoS attacks based on hurst index.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

Healthcare systems have recently utilized the Internet of Medical Things (IoMT) to assist intelligent data collection and decision-making. However, the volume of malicious threats, particularly new variants of malware attacks to the connected medical devices and their connected system, has risen significantly in recent years, which poses a critical threat to patients’ confidential data and the safety of the healthcare systems. To address the high complexity of conventional software-based detection techniques, Hardware-supported Malware Detection (HMD) has proved to be efficient for detecting malware at the processors’ micro-architecture level with the aid of Machine Learning (ML) techniques applied to Hardware Performance Counter (HPC) data. In this work, we examine the suitability of various standard ML classifiers for zero-day malware detection on new data streams in the real-world operation of IoMT devices and demonstrate that such methods are not capable of detecting unknown malware signatures with a high detection rate. In response, we propose a hybrid and adaptive image-based framework based on Deep Learning and Deep Reinforcement Learning (DRL) for online hardware-assisted zero-day malware detection in IoMT devices. Our proposed method dynamically selects the best DNN-based malware detector at run-time customized for each device from a pool of highly efficient models continuously trained on all stream data. It first converts tabular hardware-based data (HPC events) into small-size images and then leverages a transfer learning technique to retrain and enhance the Deep Neural Network (DNN) based model’s performance for unknown malware detection. Multiple DNN models are trained on various stream data continuously to form an inclusive model pool. Next, a DRL-based agent constructed with two Multi-Layer Perceptrons (MLPs) is trained (one acts as an Actor and another acts as a Critic) to align the decision of selecting the most optimal DNN model for highly accurate zero-day malware detection at run-time using a limited number of hardware events. The experimental results demonstrate that our proposed AI-enabled method achieves 99\% detection rate in both F1-score and AUC, with only 0.01\% false positive rate and 1\% false negative rate.

Increasing automation in vehicles enabled by increased connectivity to the outside world has exposed vulnerabilities in previously siloed automotive networks like controller area networks (CAN). Attributes of CAN such as broadcast-based communication among electronic control units (ECUs) that lowered deployment costs are now being exploited to carry out active injection attacks like denial of service (DoS), fuzzing, and spoofing attacks. Research literature has proposed multiple supervised machine learning models deployed as Intrusion detection systems (IDSs) to detect such malicious activity; however, these are largely limited to identifying previously known attack vectors. With the ever-increasing complexity of active injection attacks, detecting zero-day (novel) attacks in these networks in real-time (to prevent propagation) becomes a problem of particular interest. This paper presents an unsupervised-learning-based convolutional autoencoder architecture for detecting zero-day attacks, which is trained only on benign (attack-free) CAN messages. We quantise the model using Vitis-AI tools from AMD/Xilinx targeting a resource-constrained Zynq Ultrascale platform as our IDS-ECU system for integration. The proposed model successfully achieves equal or higher classification accuracy (\textgreater 99.5\%) on unseen DoS, fuzzing, and spoofing attacks from a publicly available attack dataset when compared to the state-of-the-art unsupervised learning-based IDSs. Additionally, by cleverly overlapping IDS operation on a window of CAN messages with the reception, the model is able to meet line-rate detection (0.43 ms per window) of high-speed CAN, which when coupled with the low energy consumption per inference, makes this architecture ideally suited for detecting zero-day attacks on critical CAN networks.

In the evolving landscape of Internet of Things (IoT) security, the need for continuous adaptation of defenses is critical. Class Incremental Learning (CIL) can provide a viable solution by enabling Machine Learning (ML) and Deep Learning (DL) models to ( i) learn and adapt to new attack types (0-day attacks), ( ii) retain their ability to detect known threats, (iii) safeguard computational efficiency (i.e. no full re-training). In IoT security, where novel attacks frequently emerge, CIL offers an effective tool to enhance Intrusion Detection Systems (IDS) and secure network environments. In this study, we explore how CIL approaches empower DL-based IDS in IoT networks, using the publicly-available IoT-23 dataset. Our evaluation focuses on two essential aspects of an IDS: ( a) attack classification and ( b) misuse detection. A thorough comparison against a fully-retrained IDS, namely starting from scratch, is carried out. Finally, we place emphasis on interpreting the predictions made by incremental IDS models through eXplainable AI (XAI) tools, offering insights into potential avenues for improvement.

Automated Internet of Things (IoT) devices generate a considerable amount of data continuously. However, an IoT network can be vulnerable to botnet attacks, where a group of IoT devices can be infected by malware and form a botnet. Recently, Artificial Intelligence (AI) algorithms have been introduced to detect and resist such botnet attacks in IoT networks. However, most of the existing Deep Learning-based algorithms are designed and implemented in a centralized manner. Therefore, these approaches can be sub-optimal in detecting zero-day botnet attacks against a group of IoT devices. Besides, a centralized AI approach requires sharing of data traces from the IoT devices for training purposes, which jeopardizes user privacy. To tackle these issues in this paper, we propose a federated learning based framework for a zero-day botnet attack detection model, where a new aggregation algorithm for the IoT devices is developed so that a better model aggregation can be achieved without compromising user privacy. Evaluations are conducted on an open dataset, i.e., the N-BaIoT. The evaluation results demonstrate that the proposed learning framework with the new aggregation algorithm outperforms the existing baseline aggregation algorithms in federated learning for zero-day botnet attack detection in IoT networks.

Significant progress has been made towards developing Deep Learning (DL) in Artificial Intelligence (AI) models that can make independent decisions. However, this progress has also highlighted the emergence of malicious entities that aim to manipulate the outcomes generated by these models. Due to increasing complexity, this is a concerning issue in various fields, such as medical image classification, autonomous vehicle systems, malware detection, and criminal justice. Recent research advancements have highlighted the vulnerability of these classifiers to both conventional and adversarial assaults, which may skew their results in both the training and testing stages. The Systematic Literature Review (SLR) aims to analyse traditional and adversarial attacks comprehensively. It evaluates 45 published works from 2017 to 2023 to better understand adversarial attacks, including their impact, causes, and standard mitigation approaches.

This study presents a novel approach for fortifying network security systems, crucial for ensuring network reliability and survivability against evolving cyber threats. Our approach integrates Explainable Artificial Intelligence (XAI) with an en-semble of autoencoders and Linear Discriminant Analysis (LDA) to create a robust framework for detecting both known and elusive zero-day attacks. We refer to this integrated method as AE- LDA. Our method stands out in its ability to effectively detect both known and previously unidentified network intrusions. By employing XAI for feature selection, we ensure improved inter-pretability and precision in identifying key patterns indicative of network anomalies. The autoencoder ensemble, trained on benign data, is adept at recognising a broad spectrum of network behaviours, thereby significantly enhancing the detection of zero-day attacks. Simultaneously, LDA aids in the identification of known threats, ensuring a comprehensive coverage of potential network vulnerabilities. This hybrid model demonstrates superior performance in anomaly detection accuracy and complexity management. Our results highlight a substantial advancement in network intrusion detection capabilities, showcasing an effective strategy for bolstering network reliability and resilience against a diverse range of cyber threats.

The last decade has shown that networked cyber-physical systems (NCPS) are the future of critical infrastructure such as transportation systems and energy production. However, they have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness and the deeply integrated physical and cyber spaces. On the other hand, relying on manual analysis of intrusion detection alarms might be effective in stopping run-of-the-mill automated probes but remain useless against the growing number of targeted, persistent, and often AI-enabled attacks on large-scale NCPS. Hence, there is a pressing need for new research directions to provide advanced protection. This paper introduces a novel security paradigm for emerging NCPS, namely Autonomous Cyber-Physical Defense (ACPD). We lay out the theoretical foundations and describe the methods for building autonomous and stealthy cyber-physical defense agents that are able to dynamically hunt, detect, and respond to intelligent and sophisticated adversaries in real time without human intervention. By leveraging the power of game theory and multi-agent reinforcement learning, these self-learning agents will be able to deploy complex cyber-physical deception scenarios on the fly, generate optimal and adaptive security policies without prior knowledge of potential threats, and defend themselves against adversarial learning. Nonetheless, serious challenges including trustworthiness, scalability, and transfer learning are yet to be addressed for these autonomous agents to become the next-generation tools of cyber-physical defense.

Attacks against computer system are viewed to be the most serious threat in the modern world. A zero-day vulnerability is an unknown vulnerability to the vendor of the system. Deep learning techniques are widely used for anomaly-based intrusion detection. The technique gives a satisfactory result for known attacks but for zero-day attacks the models give contradictory results. In this work, at first, two separate environments were setup to collect training and test data for zero-day attack. Zero-day attack data were generated by simulating real-time zero-day attacks. Ranking of the features from the train and test data was generated using explainable AI (XAI) interface. From the collected training data more attack data were generated by applying time series generative adversarial network (TGAN) for top 12 features. The train data was concatenated with the AWID dataset. A hybrid deep learning model using Long short-term memory (LSTM) and Convolutional neural network (CNN) was developed to test the zero-day data against the GAN generated concatenated dataset and the original AWID dataset. Finally, it was found that the result using the concatenated dataset gives better performance with 93.53\% accuracy, where the result from only AWID dataset gives 84.29\% accuracy.

Zero Day Threats (ZDT) are novel methods used by malicious actors to attack and exploit information technology (IT) networks or infrastructure. In the past few years, the number of these threats has been increasing at an alarming rate and have been costing organizations millions of dollars to remediate. The increasing expansion of network attack surfaces and the exponentially growing number of assets on these networks necessitate the need for a robust AI-based Zero Day Threat detection model that can quickly analyze petabyte-scale data for potentially malicious and novel activity. In this paper, the authors introduce a deep learning based approach to Zero Day Threat detection that can generalize, scale, and effectively identify threats in near real-time. The methodology utilizes network flow telemetry augmented with asset-level graph features, which are passed through a dual-autoencoder structure for anomaly and novelty detection respectively. The models have been trained and tested on four large scale datasets that are representative of real-world organizational networks and they produce strong results with high precision and recall values. The models provide a novel methodology to detect complex threats with low false positive rates that allow security operators to avoid alert fatigue while drastically reducing their mean time to response with near-real-time detection. Furthermore, the authors also provide a novel, labelled, cyber attack dataset generated from adversarial activity that can be used for validation or training of other models. With this paper, the authors’ overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.

Zero-day attacks, which are defined by their abrupt appearance without any previous detection mechanisms, present a substantial obstacle in the field of network security. To address this difficulty, a wide variety of machine learning and deep learning models have been used to identify and minimize zeroday assaults. The models have been assessed for both binary and multi-class classification situations, The objective of this work is to do a thorough comparison and analysis of these models, including the impact of class imbalance and utilizing SHAP (SHapley Additive exPlanations) explainability approaches. Class imbalance is a prevalent problem in cybersecurity datasets, characterized by a considerable disparity between the number of attack cases and non-attack instances. By equalizing the dataset, we guarantee equitable depiction of both categories, so preventing prejudice towards the dominant category throughout the training and assessment of the model. Moreover, the application of SHAP XAI facilitates a more profound comprehension of model predictions, empowering analysts to analyze the fundamental aspects that contribute to the detection of zero-day attacks.

The advent of the Internet of Things (IoT) has ushered in the concept of smart cities – urban environments where everything from traffic lights to waste management is interconnected and digitally managed. While this transformation offers unparalleled efficiency and innovation, it opens the door to myriad cyber-attacks. Threats range from data breaches to infrastructure disruptions, with one subtle yet potent risk emerging: fake clients. These seemingly benign entities have the potential to carry out a multitude of cyber attacks, leveraging their deceptive appearance to infiltrate and compromise systems. This research presents a novel simulation model for a smart city based on the Internet of Things using the Netsim program. This city consists of several sectors, each of which consists of several clients that connect to produce the best performance, comfort and energy savings for this city. Fake clients are added to this simulation, who are they disguise themselves as benign clients while, in reality, they are exploiting this trust to carry out cyber attacks on these cities, then after preparing the simulation perfectly, the data flow of this system is captured and stored in a CSV file and classified into fake and normal, then this data set is subjected to several experiments using the Machine Learning using the MATLAB program. Each of them shows good results, based on the detection results shown by Model Machine Learning. The highest detection accuracy was in the third experiment using the k-nearest neighbors classifier and was 98.77\%. Concluding, the research unveils a robust prevention model.

Patient’s data security is critical and cannot be undermined. The patient data must always be kept confidential. Any compromise of patient data not only results in loss of trust but can also lead to legal action. To understand data security measures and to prevent data theft, this study evaluates the cyber security position of electronic medical records using Systematic Literature Review (SLR). It primarily studies the various threats the EMRs are exposed to, more specifically in the cloud environment. It also discusses the possible ways to lower the possibility of EMR data breach. The value addition of this study is the proposition of a Risk Assessment Framework (RAF) to make the EMR software secure and safe from cyber-attacks. The cyclic RAF is proposed to manage and mitigate the risks involved in medical data storage and access.

A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.

The world has seen a quick transition from hard devices for local storage to massive virtual data centers, all possible because of cloud storage technology. Businesses have grown to be scalable, meeting consumer demands on every turn. Cloud computing has transforming the way we do business making IT more efficient and cost effective that leads to new types of cybercrimes. Securing the data in cloud is a challenging task. Cloud security is a mixture of art and science. Art is to create your own technique and technologies in such a way that the user should be authenticated. Science is because you have to come up with ways of securing your application. Data security refers to a broad set of policies, technologies and controls deployed to protect data application and the associated infrastructure of cloud computing. It ensures that the data has not been accessed by any unauthorized person. Cloud storage systems are considered to be a network of distributed data centers which typically uses cloud computing technologies like virtualization and offers some kind of interface for storing data. Virtualization is the process of grouping the physical storage from multiple network storage devices so that it looks like a single storage device.Storing the important data in the cloud has become an essential argument in the computer territory. The cloud enables the user to store the data efficiently and access the data securely. It avoids the basic expenditure on hardware, software and maintenance. Protecting the cloud data has become one of the burdensome tasks in today’s environment. Our proposed scheme "Certificateless Compressed Data Sharing in Cloud through Partial Decryption" (CCDSPD) makes use of Shared Secret Session (3S) key for encryption and double decryption process to secure the information in the cloud. CC does not use pairing concept to solve the key escrow problem. Our scheme provides an efficient secure way of sharing data to the cloud and reduces the time consumption nearly by 50 percent as compared to the existing mCL-PKE scheme in encryption and decryption process.Distributed Cloud Environment (DCE) has the ability to store the da-ta and share it with others. One of the main issues arises during this is, how safe the data in the cloud while storing and sharing. Therefore, the communication media should be safe from any intruders residing between the two entities. What if the key generator compromises with intruders and shares the keys used for both communication and data? Therefore, the proposed system makes use of the Station-to-Station (STS) protocol to make the channel safer. The concept of encrypting the secret key confuses the intruders. Duplicate File Detector (DFD) checks for any existence of the same file before uploading. The scheduler as-signs the work of generating keys to the key manager who has less task to complete or free of any task. By these techniques, the proposed system makes time-efficient, cost-efficient, and resource efficient compared to the existing system. The performance is analysed in terms of time, cost and resources. It is necessary to safeguard the communication channel between the entities before sharing the data. In this process of sharing, what if the key manager’s compromises with intruders and reveal the information of the user’s key that is used for encryption. The process of securing the key by using the user’s phrase is the key concept used in the proposed system "Secure Storing and Sharing of Data in Cloud Environment using User Phrase" (S3DCE). It does not rely on any key managers to generate the key instead the user himself generates the key. In order to provide double security, the encryption key is also encrypted by the public key derived from the user’s phrase. S3DCE guarantees privacy, confidentiality and integrity of the user data while storing and sharing. The proposed method S3DCE is more efficient in terms of time, cost and resource utilization compared to the existing algorithm DaSCE (Data Security for Cloud Environment with Semi Trusted Third Party) and DACESM (Data Security for Cloud Environment with Scheduled Key Managers).For a cloud to be secure, all of the participating entities must be secure. The security of the assets does not solely depend on an individual s security measures. The neighbouring entities may provide an opportunity to an attacker to bypass the user s defences. The data may compromise due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. Cloudsim allows to create a network that contains a set of Intelligent Sense Point (ISP) spread across an area. Each ISPs will have its own unique position and will be different from other ISPs. Cloud is a cost-efficient solution for the distribution of data but has the challenge of a data breach. The data can be compromised of attacks of ISPs. Therefore, in OSNQSC (Optimized Selection of Nodes for Enhanced in Cloud Environment), an optimized method is proposed to find the best ISPs to place the data fragments that considers the channel quality, distance and the remaining energy of the ISPs. The fragments are encrypted before storing. OSNQSC is more efficient in terms of total upload time, total download time, throughput, storage and memory consumption of the node with the existing Betweenness centrality, Eccentricity and Closeness centrality methods of DROPS (Division and Replication of Data in the Cloud for Optimal Performance and Security).

Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest.

Recent advancements in technology have transformed conventional mechanical vehicles into sophisticated computer systems on wheels. This transition has elevated their intelligence and facilitated seamless connectivity. However, such development has also escalated the possibility of compromising the vehicle’s cyber security expanding the overall cyber threat landscape. This necessitates an increased demand for security measures that manifest flexibility and adaptability instead of static threshold-based measures. Context-awareness techniques can provide a promising direction for such security solutions. Integration of context-awareness in security analysis helps in analysing the behaviour of the environment where IoT devices are deployed, enabling adaptive decision-making that aligns with the current situation. While the incorporation of context-awareness into adaptive systems has been explored extensively, its application to support the cyber security of vehicular ecosystem is relatively new. In this paper, we proposed a context-aware conceptual framework for automotive vehicle security that allows us to analyse real-time situations thereby identifying security threats. The usability of the framework is demonstrated considering an Electric Vehicle(EV) Charging case study.

Cloud computing is a nascent paradigm in the field of data technology and computer science which is predicated on the use of the Internet, often known as the World Wide Web. One of the prominent concerns within this field is the security aspects of cloud computing. Contrarily, ensuring the preservation of access to the protection of sensitive and confidential information inside financial organizations, banks and other pertinent enterprises holds significant significance. This holds significant relevance. The efficacy of the security measures in providing assurance is not infallible and can be compromised by malevolent entities. In the current study, our objective is to examine the study about the security measures through the use of a novel methodology. The primary objective of this research is to investigate the subject of data access in the realm of cloud computing, with a particular emphasis on its ramifications for corporations and other pertinent organizations. The implementation of locationbased encryption facilitates the determination of accurate geographical coordinates. In experiment apply Integrated Location Based Security using Multi objective Optimization (ILBS-MOO) on different workflows and improve performance metrics significantly. Time delay averagely approximates improvement 6-7\%, storage 10-12\% and security 8-10\%.