Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments
Abstract
:1. Introduction
Ref. | Description | Threat Considered | Impact of the Threat | Analysis of the Threat Model Considered | Remarks |
---|---|---|---|---|---|
[10] | Secure data analytics in edge computing | ✓ | × | ✓ | Propose key requirements for secure data analytics and identify pros and cons of existing works on data analytics. |
[11] | Data security in edge computing | × | × | ✓ | Review different cryptography-based solutions to address data security issues in edge computing. |
[21] | Security issues during authentication schemes for data integrity | × | × | ✓ | Evaluate existing methods to preserve data integrity in fog and cloud computing and identify their limitations. |
[7] | Security issues in edge computing | × | × | ✓ | Review security issues in terms of access control, key management, privacy, attack mitigation, and anomaly detection. |
[22] | Security-as-a-Service in multi-access edge computing | ✓ | × | ✓ | Evaluate IDS, secure communication, and access control mechanisms, and propose a secure service deployment framework. |
[23] | Security issues that are caused by adopting virtualization in edge computing | ✓ | × | ✓ | Discuss the advantages of adopting virtualization, containers, Uni kernels, and real-time OS in edge computing. Security issues and attacks on these technologies with different use case scenarios are addressed. |
[24] | Security and prevention mechanisms in fog computing | × | × | ✓ | Comparative analysis of different techniques to address common security issues in edge computing. |
[25] | Security threats in mobile edge computing | ✓ | × | ✓ | Review the advantages of using machine learning techniques to improve network efficiency and handle malicious attacks. |
[26] | Security aspects in fog computing | × | × | × | Discuss security issues in edge computing caused due to its operations in the physical environment and the need for interoperability between edge nodes and IoT devices with various solutions. |
[27] | Security issues in edge, fog, and IoT applications | × | × | × | Identify security issues and evaluate authentication and encryption schemes to address these issues. |
[28] | Review of fog-based applications’ architecture and security issues at the architectural level | × | × | × | Discuss four edge-based applications and security concerns to prevent malicious access and data modification in these applications. |
[29] | Security issues due to fog infrastructure in various applications | × | × | ✓ | The present data analytics taxonomy discusses the complexity during data processing with research challenges. |
[17] | Discuss how to improve security issues and protocols in fog computing | × | × | ✓ | Present a comprehensive survey on overall issues in edge computing. Analyze security models that address location and data privacy, secure communication, and various intrusion systems. |
[11] | Analyze fog computing architecture, security, and trust issues | × | ✓ | ✓ | Discuss security issues, various mechanisms, and different technologies to handle data security and privacy in edge computing. |
[16] | A comprehensive review of edge computing security issues with a few proposed solutions | δ | δ | ✓ | Identify the challenges of the existing security models to handle threats in edge computing and suggest a few solutions that can be applied to a similar edge computing paradigm. |
[8] | Security and privacy issues due to fog computing architecture | × | × | × | Identify the threats in the edge computing platform. |
[30] | Challenges due to data security and privacy | δ | ✓ | × | Justify how cloud data security solutions cannot be applied to edge computing and highlight the importance of addressing this issue in edge computing. |
[31] | Layer-wise security and threat issues | × | × | ✓ | Identify the threats in each layer and propose a risk-based trust model to secure the decision-making process and secure data in the edge layer. |
[14] | Review of security and privacy issues to secure fog-based IoT application | δ | ✓ | × | Identify the threats and security issues related to data storage, computation, and data sharing in the fog layer. |
[13] | Potential security issues in the fog-based application | ✓ | ✓ | × | Various edge computing solutions are analyzed, and security models related to privacy-preserving, insider attacks, resource management, encryption, and authentication schemes are discussed. |
[32] | Address all the common security and privacy issues in fog computing and identify gaps in the existing security solutions | × | × | ✓ | Propose solution toward establishing trust, secure communication channels, and privacy-preserving schemes. |
[33] | Concerning security and resilience edge and fog computing architectures are analyzed | × | × | × | Address issues related to virtualized infrastructure and software-driven communication. |
[12] | Using fog computing, how to secure healthcare data is discussed | ✓ | × | × | Propose encryption algorithms to secure data on the edge layer. |
[19] | MITM attacks are studied exclusively by CPU and memory consumption on fog devices | × | ✓ | × | Present authentication and authorization techniques to protect edge nodes from an MITM attack. |
[34] | Security threats when adopting edge computing in IoT applications | × | × | × | Review existing security models that address MITM, intrusion detection, malicious nodes, and data protection models. |
[18] | Security threats that affect the confidentiality, integrity, and availability of the architecture | × | ✓ | × | Discuss the advantages of adopting edge computing in IoT applications. Recommend a few solutions to address the vulnerabilities and threats due to adoption. |
Current study | Security issues on edge nodes that affect decision-making and analytics of the applications | ✓ | ✓ | ✓ | Review potential threats that affect edge nodes and disturb the normal functioning of applications. Identify research gaps in existing security models. |
2. Edge Data Analytics
Decision-Making in Edge Data Analytics
3. Security Threats during Edge Data Analytics
Types | Ref. | Threats | Definition | Impact on the Edge Nodes and Networks |
---|---|---|---|---|
Insider or Malicious Attack | [68] | Data Breach | Illegal data access and data leak | Disclosure of confidential and sensitive data to an unauthorized person |
[69] | Hacking | Illegitimate users modifying or altering the edge and user data | Loss of data integrity, manipulation of decision-making, and disturb the normal functioning of the application | |
[70] | Identity and Password Leak | Illegally hacked username and password to gain access to the application | Gain unrestricted access to the application and misuse of sensitive information | |
[63] | Malicious Insider | Illegally access the network and control all the nodes | Behave legitimately and take advantage of the services | |
[71] | Forgery | Forge the identities and profiles | Generate fake information and mislead other users. Consume more bandwidth, storage, and energy | |
Hardware Attack | [72] | Jamming | Blocking communication channel | Loss of data or increased data transmission rate |
[73] | Side- Channel Attack | Deliberately block communication channel | Falsification of data and increased computation time | |
[74] | Resource Depletion | Flood traffic and saturated storage or network resources | Affects data processing and delays decision-making due to a lack of resources | |
[75] | Equipment Sabotage | Deliberately create resource deficiency | Damage resources and disturb real-time services | |
[76] | DoS attack | Disruption of edge nodes, hardware devices, or software applications | Consume more node resources, disrupt network operations, and generate false messages | |
Software Attack | [77] | SQL Injection | Inject code to access sensitive data | Modify sender data or fabricate new malicious data to affect data confidentiality |
[78] | Impersonation | Claim to be an alternative user by using a forged character | Acquire illegitimate benefits and access confidential data with malicious intentions | |
[14] | Tampering | Unauthorized entities intentionally modifying data | Causes privacy leakage, hijacks services, or creates other attacks | |
[79] | Eavesdropping | Illegally gain access to the network and listen to the network communication | Hack users’ data and intercept communication channels to degrade efficiency | |
Network Attack | [80] | Message Replay | Illegitimate user sending authorized messages in the network | Compromises other nodes and exposes sensitive data |
[81] | Spoofing | Fake users repetitively requesting services | Divert communication channel toward attackers’ destination. Consumes more bandwidth and increases processing times | |
[14] | Man-In-The-Middle | An illegitimate insider in the network with malicious intention | Steal users’ credentials, attack communication channels, or alter data | |
[82] | Flooding | Generate enormous illegitimate messages and increase network traffic | Disrupt the network and prevent legitimate users from accessing the network | |
[77] | Pattern Analyses | Intercepting and examining the data flow and network pattern in the communication channels | Gain unauthorized access to the network and steal data | |
[83] | Spamming | Send spontaneous messages to all the nodes requesting services | Collect user credentials and gain access to the network | |
[84] | Sybil | Create a fake identity and gain access to the network | Acquire privileged access to the services | |
[85] | Sinkhole attack | The malicious node sends a fake message and establishes a connection with a legitimate node | Creates maximum traffic flow and makes adjoining nodes collide. Increases bandwidth, leading to resource contention and message destruction |
4. Motivating Use Case Applications
4.1. Healthcare Applications
4.2. Traffic Management Applications
4.3. Smart City Applications
Use Case | Ref. | Working Model | Decision Making Node | Evaluation | Insider Attack | Software Attack | Hardware Attack | Network Attack | Effect of Threats on the Model |
---|---|---|---|---|---|---|---|---|---|
Health Care Applications | [90] | Emergency alert message for COVID-19 infection | Artificial intelligence-based fog node | Generate medical report and alert message to caregivers and doctors | Data breach | - | Equipment malfunction | - | Hack data or may degrade alert message efficiency |
[96] | FAST—Fall detection system for stroke patients | Back-end module server on the cloud | Detects if the stroke patient is about to fall and triggers message to the emergency phone number | Forgery, MITM | Tampering | - | - | Causes false predictions, degrades efficiency, and maliciously drops or delays information | |
[95] | Fall detection or electrocardiography monitoring | Edge gateway—Fall detection system | Notification and alert message to caregivers | Forgery, MITM | Tampering | - | - | May degrade notification efficiency | |
[98] | eWall—Home management for senior citizens | eCloud or ePSOS | Track daily activities of an elderly patient. Alert message from eWall cloud to relatives or hospital | MITM, malicious insider | - | Resource depletion | - | Affect confidentiality, breach privacy, tamper with hardware devices, and disturb normal data flow | |
[99] | Activity monitoring | Cloud Access Security Broker | Activity detection and calories burnt are sent to hospitals and nutritionists | MITM, insider, hacking | Impersonation | - | - | Affect confidentiality, privacy, and reliability of the decision | |
[119] | Healthcare and Assisted Living (AAL) in Smart ambient | Fog Accelerator Nodes | Aggregate data from IoT sensors and monitor patients’ fall or cardiovascular issues. In case of emergency, informs caretakers | - | SQL Injection | Equipment sabotage | - | Affect confidentiality, leak sensitive information, and destroy hardware devices | |
[120] | Smart e-Healt hcare system | Gateway nodes | Gather medical information of patients from sensors, aggregate in edge layer, and generate EWS in case of emergency for doctors or caretakers | Malicious insider | Impersonation, jamming | - | - | Malicious insider can watch the activities, illegitimately communicate with other users, falsify data, or send a false alarm | |
[92] | Chikungunya virus diagnosis solutions | Alert generation component in fog layer | Alert message is sent to government and healthcare to control outbreak of virus | - | - | Equipment sabotage | - | May not create an alert message or causes a delay in generating the alert message | |
[93] | Detect cancer and monitor patients | Smart gateway nodes in fog layer | Send e-report to patients, send ambulance in case of emergency, and monitor patients until they recover | Data breach | - | - | Eavesdropping | Intruder may hack patients’ personal data or may be a silent spectator | |
Traffic Management Application | [103] | Traffic Management Scheme | Cloudlets | Minimize response delay for traffic management by load balancing | Data breach, malicious insider | - | - | - | Breach data privacy |
[105] | Vehicular Network collaboration | Fog Controller Node | Accident notification and avoid road congestion Traffic prioritization in case of emergency and directs fast rescue route | Location privacy | - | Fault tolerance | Sinkhole, sniffing, spoofing | Track users’ location or deprive them from the network | |
[106] | Smart Traffic Control | Traffic Control Node | Identifies road congestion and avoids traffic jams | Location privacy | - | Fault tolerance | Sinkhole, sniffing, spoofing | Track users’ location or deprive them from the network | |
[109] | 5G-based Intelligent Transport System | Transportation authority at the edge layer | Sends traffic violation report (TVR) based on the vehicle’s speed sensors | - | - | Equipment sabotage, side channel attack | Physical damage to sensor nodes, blocks communication channels, and increases waiting time. | ||
[121] | Smart Car Parking system | Microcontroller device generates parking status | Identifies traffic jam and shows parking spots | Location privacy | - | Jamming | Track users and vehicle information, cause traffic congestion | ||
Smart City Applications | [122] | Surveillance videos for smart cities | Fog Aggregate Nodes | Send compressed video data to the cloud | Side channel attack | Tampering | Equipment sabotage | Eavesdropping, Sybil, DDoS, pattern analysis | Maliciously drop or delay information, block the resource or request from the users, hack user privacy |
[123] | Smart things to machine interaction | Fog Controller Node | Intelligent lighting—sensor identifies when to turn the switch on/off | - | Tampering | - | - | Device tampering | |
[118] | Smart pipeline monitoring system | Fog Controller Node | Closes gas pipeline in case of gas leakage or fire detection | - | Tampering | - | - | Device tampering | |
[114] | Powerline communication for smart meters | Fog Computing Nodes | Summary of electric power consumption data is sent to the cloud | Data alteration | - | - | Eavesdropping, pattern analysis, jamming, DoS | Device tampering | |
[124] | Forest Fire management systems | Prediction system | Identifies and generates an alert message to forest authorities | - | Tampering | - | - | Alters the decision with malicious intentions |
5. Analyses of Existing Security Threat Models
5.1. Intrusion Detection System
5.2. Combination of an Intrusion Detection and Intrusion Prevention System
5.3. Automated Intrusion Detcetion System
5.4. Machine Learning-Based Intrusion Detection System
5.5. Cryptography-Based Systems
5.6. Authentication Scheme in the Edge Computing Layer
5.7. Hybrid Models
5.8. Application-Specific Security Models
5.9. Container and Consensus Protocols in Edge-Based Security Models
5.10. Bridging Gap with Cloud Security
5.11. Impact of Threats on Edge Data Analytics
6. Future Research Directions
- Adopting federated learning (FL) algorithms for edge data analytics—Following observation #1, the integration of AI in edge computing is widely adopted, especially in healthcare applications. It remarkably enhances the scope and computational efficiency of edge nodes [90]. However, the challenging aspects of AI models are their short battery life, power-hungry, delay-intolerant portable devices, vulnerable to security threats, and a loss of their reliability [91]. These limitations can be resolved by adopting the federated learning framework in AI models. Federated learning is an ML technique used to train data across decentralized edge devices without exchanging them with other devices. This reduces the amount of data in wireless uplinks, adapts well with heterogeneous cellular networks, and preserves privacy. Pace steering in FL is a flow control mechanism that controls data uplinks by regulating the device connection pattern [162]. FL deploys secure data aggregation mechanism, where data remains secure even in the memory to protect additional security in data centers [163]. Therefore, FL can be best applied for applications such as edge computing, where device data are more relevant, for better data transmission and to provide security.
- Enhancing IEEE communication standards in edge-based healthcare applications—The sensors in healthcare applications are connected through BAN or WPAN. As noted in observation #2, the network may not offer necessary bitrates for biomedical signals’ transmission. This will delay communication or reduce the quality of a link within body devices, especially when many body sensors are interconnected [97]. Currently, IEEE 802.15 technical standards are used in BAN or WPAN, which results in low-rate data transmission in edge data analytics, but this standard was designed for Zigbee or 6LoWPAN, whereas IEEE 802.15.6 is a standard for WBANs that helps healthcare service providers to monitor patients at any time and location. It provides human body communication with a data rate of more than 2 Mbps (Mega Bytes Per Second) and an operation band of 27 MHz (Mega Hertz). These operation bands are valid in the major European countries. Apart from that, it also provides secure communication with three different security levels through authentication and encryption. This provides solutions for integrity, reply defense, confidentiality, privacy protection, and message authentication problems. Therefore, adopting IEEE 802.15.6 in healthcare applications can enhance the reliability, service quality, low power, data rate, and non-interference. This standard also deals with particular BAN requirements, such as security, energy consumption, range of communication, scale of the network, and data rate [164].
- Developing a robust and efficient data dissemination technique in VANET for a node selection strategy—As noted in observation #3, in VANET it is challenging to maintain a specific topology for every vehicle due to the high mobility and uneven distribution of vehicles. Conventional routing protocols use a street-centric divide-and-conquer approach. This approach can be efficient if a succession of vehicles between the source and destination is determined in advance [165]. However, it may not be possible in a real-time scenario, as it results in unavoidable collision problems. Therefore, a robust and efficient data dissemination technique is required that considers selecting efficient relaying nodes to forward packets even when the source and destination of the vehicles are not known in advance [166]. The data dissemination technique should be aware of the vehicle topology within its coverage and monitor the changes in topology so that the data transmission between edge nodes and devices can be scheduled and secured with the assigned frame. This approach can greatly reduce the data transmission delay for edge analytics and secure the transmitted data.
- Employing energy harvesting techniques to preserve longevity and processing capabilities of edge nodes in smart city applications—In an efficient smart city application, integrating energy harvesting techniques into edge computing for smart city applications offers a robust solution to safeguarding against data threats, ensuring the integrity, confidentiality, and authenticity of critical information. By harnessing renewable energy sources, edge devices can maintain continuous operation, facilitating real-time data analysis and threat detection. This uninterrupted surveillance capability is pivotal in detecting and mitigating potential security breaches. As noted in observation #5, high battery consumption is the most common problem in crowdsensing when actively collecting data, and this may affect the quality of data collected and the processing capabilities of edge nodes [167]. Moreover, with decentralized processing at the edge, sensitive data can be processed closer to its source, minimizing the risk of exposure during transit to centralized servers [168]. Additionally, energy harvesting supports the implementation of advanced encryption protocols and authentication mechanisms, further fortifying data security measures [102]. By combining energy harvesting with edge computing, smart city infrastructures can establish resilient defenses against evolving data threats, ensuring the trustworthiness and reliability of their systems in safeguarding citizen safety and critical infrastructure.
- Enhancing network infrastructure in the edge layer—Different technologies, such as SD, NFV, 5G, or virtualization, can significantly bolster security measures against threats and attacks in edge data analytics. SDN and NFV enable centralized management and orchestration of network resources, allowing for dynamic and granular control over security policies and access permissions [169]. The 5G networks provide higher bandwidth, lower latency, and greater reliability, facilitating secure and real-time communication between edge devices and centralized servers [170]. Virtualization techniques enable the isolation of critical network functions and applications, limiting the potential impact of security breaches or attacks [171]. By leveraging these technologies collectively, organizations can establish resilient and adaptive network infrastructures capable of mitigating risks and ensuring the integrity, confidentiality, and availability of data in edge-based IoT environments.
- Adopting fine-grained access control mechanisms in the edge layer—It can be noted from observation #6 that when data are stored in the edge layer for a long time before transferring them to the cloud, it can lead to any catastrophic events. This can result in data authentication and integrity issues, affecting the decision-making capabilities of the edge nodes. It is also observed that hybrid models and encryption techniques are used to address these issues in the existing security model. However, as stated in observation #7, complex keys due to these techniques can result in network congestion in communication channels. Therefore, adopting access control mechanisms between data owners and the edge layer, which is a straightforward approach, can overcome these issues. This approach has proved to be efficient in cloud computing [172]. However, in edge computing, the access control mechanism has to be fine-grained, which supports secure collaboration, interoperability between heterogeneous devices, and enhances data tracking. At the same time, the design goals and resource constraints of edge nodes have to be considered so that it provides a lightweight and secure data analytics scheme.
- Designing trust management models in an edge computing framework—The decentralized edge computing has a huge obstacle of collecting and managing information from various edge nodes to perform data analytics. These criteria can be distinct to various applications and services [32]. Further, edge nodes might frequently move from one area to another [10]. This movement causes challenges in establishing trust among edge nodes during data processing. Thus, designing a trust model that supports mobility and scalability is required in an edge computing framework. The trust models can be third-party models used to decrease the computation overload of the edge nodes and should manage interregional trust values through historical data to track the mobility of edge nodes.
- Isolating the infected edge nodes in the edge computing layer—In the currently available edge threat models, malicious nodes are the common threats that affect the decision-making process. Malicious nodes can always compromise other nodes and create other attacks in the edge layer, such as DoS, repeated storage/processing requests, spoofing, or leakage of confidential data [158]. This induces security and trust risks, spreading among the edge nodes and to the whole edge layer. Therefore, a strategy needs to be developed to identify the malicious node and isolate it from the other nodes to reduce the risk of malicious nodes gaining control on the edge layer.
- Enhancing security with emerging technologies, such as AI and blockchain—AI algorithms can play a crucial role in real-time threat detection and anomaly detection at the edge layer, continuously monitoring device behavior and network traffic to identify potential security threats. Additionally, AI-based techniques can leverage historical data to improve the accuracy and effectiveness of security measures in edge data analytics systems [173]. Furthermore, blockchain technology offers promising solutions for ensuring data integrity and enhancing trust in edge data analytics. By providing a decentralized and immutable ledger, blockchain can create tamper-proof records of data transactions, ensuring the authenticity and transparency of data collected and processed at the edge layer [174]. Moreover, blockchain facilitates secure and transparent data sharing among multiple parties in edge computing environments, preserving data privacy and confidentiality while enabling efficient collaboration [175]. Combining AI and blockchain technologies presents an exciting avenue for future research in enhancing security in edge data analytics. By integrating AI algorithms for threat detection with blockchain for secure data transactions, edge data analytics systems can achieve a higher level of security, trustworthiness, and resilience against security threats [176]. Exploring innovative approaches that leverage the synergies between AI and blockchain holds great potential for advancing the security capabilities of edge data analytics systems and addressing evolving security challenges in edge computing environments.
7. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref | Solution Approach | Performance | Findings |
---|---|---|---|
[125] | Artificial neural network-based IDS | Detects malicious edge nodes based on the node’s profile features. Identifies DoS, flooding, and replay attacks | High accuracy and low false alarm rate. Efficient to maintain the edge network’s resilience by discarding the intruders |
[126] | Identifies insider attacks using random Gossip Consensus algorithm | Detects insider attacks using edge node’s state information without any supervision | Extensive resource consumption |
[127] | Hierarchical Identity-Based Encryption Scheme to achieve data security | Four hierarchical layered security keys are used to secure data from the attack | Escrow key problem |
[128] | Data privacy-preserving scheme based on data load forecasting | Smart meters are used to calculate the workload using the Oblivious Multivariate Polynomial valuation (OMPE) protocol and protect data from unauthorized access | Reduces computational overheads and data load to the cloud |
[129] | Password-based secure communication protocol for data transmission between cloud and edge devices | Establishes secure communication based on pivotal agreement between user and edge devices. Eavesdropping, data alteration, MITM, impersonation, and malicious insider attacks are restricted | Most of the threats are addressed, and communication channels are secured. However, phishing can be used to easily hack the password |
[130] | Gaussian Naive Bayesian theorem is used to analyze the packets and identify an intruder | Analyze the network using the Markov model and lure attackers using the Virtual Honeypot method | Attacker can act legitimately and gain access to the Honeypot method |
[78] | Q-Learning-based reinforcement learning technique to identify impersonation attacks | Detects attack accurately in edge layer. False alarm rate, misdetection rate, and the average error rate are identified using channel state information | Channel state information can be considered to study further attacks, such as DoS, spoofing, jamming, authentication, etc. However, it is not considered in this approach |
[122] | Intrusion detection and intrusion prevention system | Identifies MITM attack. Interrogates communication channel using Advanced Symmetric Encryption, and exchanges keys using the Diffie–Hellman method | Not suitable for multi-hop attacks |
[131] | Automated validation of Internet Security Protocols to secure Intelligent Edge-based Transport System | Generates a 64-bit symmetric key or 512-bit asymmetric key to secure communication. It is very complicated for attackers to break this key | It is impractical to assume that all vehicles are legitimate |
[132] | Fault diagnosis of the hardware components | Case-based reasoning model to classify the fault type for a hydropower plant using storm-based architecture | System-specific application |
[133] | Anonymous and Secure authentication scheme | Secure cryptographic algorithms are used to establish confidentially, privacy, and mutual authentication among edge nodes | Cryptographic algorithms may increase computational time |
[134] | Cybersecurity framework to identify a malicious node | Identifies malicious node through Markov model and shifts that node to a Virtual Honeypot device | Efficiently traps the malicious node, but the attacker can act legitimately and gain access to the Honeypot method |
[135] | Container-based map reduction protocol to secure computation | Hardware-assisted remote attestation mechanism is used to establish trusted containers | Linux containers encapsulate the application and establish trust during execution |
[136] | The DDoS attack traffic system | Identifies spoofing or infinite false requests and mitigates to avoid power wastage | Challenging to implement during peak traffic |
[12] | Privacy-preserving model in healthcare applications | Hybrid user profiling is used to identify the attacker and direct toward a decoy message to trap the attacker | The focus is only on multimedia data. It cannot be applied to other data |
[137] | User profiling to handle data theft | Prototype-based web patterns validate the effectiveness of decoy messages in the edge layer | Decoy data generation is time-consuming |
[138] | Snort-based Field Programmable Array Intrusion model | Signature-based detection through network traffic monitoring and generates an alert message | Edge networks accelerate at the generic level |
[139] | A hybrid approach using machine learning | Two-stage detection: (a) identify intrusion using binary detection, and (b) detect and confirm attacks | High precision and recovery rate. Cannot classify the attack precisely |
[140] | Fully automated IDS using multi-layered recurrent neural network | Detect attacks using traffic analyses engine and multi-layered recurrent neural network | Accurately identifies DoS attacks and works efficiently in real time |
[106] | Multi-attack IDS | Identifies abnormality using the backpropagation neural network and detects using the radial basis function | Mobile edge nodes assist to achieve high accuracy. Identifies combinations of mixed attacks |
[81] | Spoofing detection using multichannel attribute | Creates clusters at edge servers using a local heuristics algorithm and identifies spoofing attacks | Clusters are created at close optimal solutions |
[141] | Live data analytics with collaborative edge and cloud processing | Integrates edge computing and cloud computing to leverage their respective advantages and address the challenges of processing massive amounts of data generated by IoT devices | Resource optimization and efficient data analytics to address the challenges of handling large volumes of data and enhance network performance |
[142] | Secure IoT service with an efficient balance dynamic based on cloud and edge computing | Creates new parsing templates, prioritize services with stringent demands, and ensures the reliability of IoT data transfer | Enhances trust evaluation mechanisms and collaborative strategies |
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Mahadevappa, P.; Al-amri, R.; Alkawsi, G.; Alkahtani, A.A.; Alghenaim, M.F.; Alsamman, M. Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments. IoT 2024, 5, 123-154. https://doi.org/10.3390/iot5010007
Mahadevappa P, Al-amri R, Alkawsi G, Alkahtani AA, Alghenaim MF, Alsamman M. Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments. IoT. 2024; 5(1):123-154. https://doi.org/10.3390/iot5010007
Chicago/Turabian StyleMahadevappa, Poornima, Redhwan Al-amri, Gamal Alkawsi, Ammar Ahmed Alkahtani, Mohammed Fahad Alghenaim, and Mohammed Alsamman. 2024. "Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments" IoT 5, no. 1: 123-154. https://doi.org/10.3390/iot5010007