Network Attacks Research Articles

Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.

Mitigating blackhole attacks in wireless body area network

<p>In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.</p>

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Application of hybrid strategies of complex network attack and defense games

Critical infrastructure networks serve as the backbone of modern society’s operations, and the application of game theory to safeguard these networks against deliberate attacks under resource constraints is of paramount importance. Regrettably, the existing body of research on hybrid attack and defense strategies for nodes and edges is notably scarce, despite the ubiquity of such strategies in practical contexts. Current understanding regarding the establishment of a cost constraint model within the framework of hybrid attack and defense strategies, the strategic inclinations of both adversarial and defensive parties under varying cost constraint coefficients, and the influence of resource allocation ratios on equilibrium outcomes remains limited. Consequently, this study constructs a game-theoretic model for the engagement of critical infrastructure networks under non-uniform cost constraints, incorporating hybrid attack and defense strategies for nodes and edges, and conducts an equilibrium analysis across a range of cost constraint coefficients and resource allocation ratios. The findings reveal that when resource availability is limited, both attackers and defenders are inclined towards a strategy of concentrated resource allocation; in contrast, under most circumstances, defenders exhibit a preference for an equitable distribution of resources.

Enhancing Smart Home Security and Privacy: Integrating Blockchain and Machine Learning

Abstract. A smart home is a type of home device connected through the Internet of Things (IoT) technology. With the rapid development of IoT applied to smart homes, this brings great convenience to people and also raises some privacy and security issues. As IoT collects privacy and a large amount of data about users that may be stolen by unscrupulous people through some cyberattacks, people are beginning to pay more and more attention to the security of the smart home. Blockchain is a distributed database technology that stores data in the form of blocks and uses cryptographic methods to ensure the security and integrity of the data. And mechanical learning can detect some network attacks. This study proposes a smart home architecture that integrates blockchain technology and mechanical learning. Combining blockchain technology, machine learning, and smart homes can effectively identify some cyber attacks while ensuring privacy and security so that people can take appropriate measures.

K-means Optimized Network Security Management in the Internet Context

The rapid development of the Internet has had a broad and profound impact on humanity, making information acquisition and dissemination more convenient. It has also brought significant opportunities and benefits to business and the economy. However, there are some issues, such as personal factors and data security concerns. In order to solve the above problems, the K-means algorithm is optimized from the perspectives of K-value validity index, feature weighting and three-branch decision making. First, the optimal clustering results are determined according to K-value validity index, and the influence of different dimensional features on clustering is considered for feature weighting, and the uncertain objects in the three-branch decision deadlock class are divided into the boundary domain. The delay decision of the boundary domain data is carried out, and the K-means clustering optimization algorithm is improved by combining the above three aspects, and the intelligent network security management system is developed on this basis. The results showed that the K-means optimization algorithm achieved the highest average accuracy rate, adjusted Morandi index, and adjusted mutual information across various datasets, with values of 96.01%, 0.866, and 0.869, respectively. In practical network attack scenarios, the K-means optimization algorithm attained an attack threat recognition accuracy of 94.38%. Under unknown network attack types, its detection rate and false alarm rate were 94.63% and 1.32%, respectively. Surveys conducted post-implementation of the intelligent network security management system indicated that over 90% of users were satisfied with their experience of the system. In summary, the proposed method accurately identifies potential network threats in network data, fulfilling performance requirements for network security management systems and ensuring the security of network resources.

A Cutting‐Edge Hybrid Deep Learning Technique with Low Rank Approximation for Attacks Classification on IoT Traffic Data

ABSTRACTNetwork security is experiencing huge challenges as network attacks on traffic data become more frequent and sophisticated. In this paper, we employ hybrid deep learning models and low‐rank approximation to present a novel method for multi‐label categorization of network assaults on traffic data. Our suggested solution, LR‐CNN‐MLP, consists of three models a multi‐layer perceptron (MLP), a hybrid convolutional neural network (CNN), and a low‐rank approximation model. While the CNN and MLP models extract features and categorize data, respectively, the low‐rank approximation model reduces the input's dimensionality. Overall, by combining hybrid models and low‐rank approximation, our proposed LR‐CNN‐MLP approach provides a promising solution for multi‐label categorization of network attacks on traffic data. LR‐CNN‐MLP achieves the highest results of performance metrics such as 0.944 precision, 0.979 recall, 0.961 F1‐score and 98.17 accuracy.

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks.

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model's effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications.

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.

Timely Detection of DDoS Attacks with Dimenstionality Reduction

Due to the interconnectedness and exponential proliferation of IoT devices, the technology is more susceptible to network attacks like Distributed Denial of Service (DDoS), which disrupt network resources. A growing threat to cloud computing systems is the Distributed Denial of Service (DDoS) attack, in which the attacker starts the attack by taking advantage of computers both inside and outside the cloud system. Real-time analysis of cloud network data is essential for preventing DDoS attacks. DDoS attacks interfere with the operation of Io- connected apps and services by taking advantage of the constrained resources on IoT devices. The impacts of DDoS attacks, which seriously damage current systems, are thoroughly examined in this article in the context of the Internet of Things. One of the most common network attacks is the distributed denial-of-service attack (DDoS). DDoS assaults intensified due to the quick development of computer and communication technologies. Therefore, investigating the detection of a DDoS attack is crucial. A single technique cannot offer adequate security due to the variety of DDoS attack techniques.

An Effective Prevention Approach against ARP Cache Poisoning Attacks in MikroTik-based Networks

Nowadays, leading manufacturers of enterprise-grade networking devices offer the dynamic ARP inspection (DAI) feature in their Ethernet Switches to detect and prevent ARP cache poisoning attacks from malicious hosts. However, MikroTik Ethernet switches do not yet support this feature. Within MikroTik-based networks, three potential approaches exist to prevent ARP cache poisoning attacks, each with drawbacks. This paper proposes an innovative approach called Gateway-controlled ARP (GCA) to prevent ARP cache poisoning attacks on a router-on-a-stick (RoaS) network using MikroTik networking devices, where a single router performs inter-VLAN routing through one physical interface. With this approach, all Ethernet switches are configured to forward ARP messages from hosts directly to the router for inspection and handling. A RouterOS script based on the GCA approach was implemented and executed on the router to handle all incoming ARP requests from any host in all VLANs, ensuring all hosts receive legitimate ARP responses from the router. This approach can effectively prevent spoofed ARP packets sent by malicious attackers. This approach was tested and evaluated on an actual RoaS network, focusing on processing time, CPU Load, and response time. The evaluation results show that the approach effectively prevents ARP cache poisoning attacks.

Quantitative Studies on Hacking Methods and Types of Cyber-Attackers of Electronic Banking Networks

Cyber-attacks on electronic banking networks are a growing concern in today's digital age. Cybercriminals try to penetrate the Electronic Banking infrastructure, by bypassing all protection mechanisms, and the media continue to report new bank cyberattacks and thefts. The study attempts to identify the impact of attacks and methods that threaten bank networks. To know the kinds and methods of the attackers of bank networks, a questionnaire was prepared and distributed to the selected banks and their branches, namely Omdurman National Bank, Bank of Khartoum, and Bank of Sudan. From the studied sample, competitors stood out as the most qualitative network attackers on the banks, while penetration through the Internet was one of the ways through which the attacks on the financial banks networks could be carried out. The sample of the study showed that the quality of bank attackers are the competitors, with a rate of 29.2%, which is considered the largest percentage of all assumptions and the highest network attacker’s method Through the internet with a rate of 52.7%. The study concluded that Competitors and Internet have highest impact per methods and kinds of attacks respectively. This conclusion supports the problem statement since the impact was known within the case study Banks.

High-performance BFT consensus for Metaverse through block linking and shortcut loop

In recent years, the Metaverse has captured increasing attention. As the foundational technologies for these digital realms, blockchain systems and their critical component – the Byzantine Fault Tolerance (BFT) consensus protocol – significantly influence the performance of Metaverse. Due to vulnerabilities to network attacks, synchronous and partially synchronous consensus protocols often face compromises in their liveness or security. Consequently, recent efforts in BFT consensus have shifted towards asynchronous consensus protocols, notably the Multi-valued Validated Binary Agreement (MVBA) protocols, with sMVBA being particularly prominent. Despite its advances, sMVBA struggles to meet the high-performance demands of Metaverse applications. Each sMVBA instance commits only one block, discarding all others, which severely restricts throughput. Moreover, if a leader in a given view crashes, nodes must rebroadcast blocks in the subsequent view, resulting in increased latency.To overcome these challenges, this paper introduces Mercury, a protocol designed to enhance throughput under various conditions and reduce latency in less favorable scenarios where leaders are crashed. Mercury incorporates a mechanism whereby each block contains hashes from blocks of a previous instance, linking blocks across instances. This structure ensures that once a block is committed, all its linked blocks are also committed, thereby boosting throughput. Additionally, Mercury integrates a ‘shortcut loop’ mechanism, allowing nodes to bypass the last phase of the current view and the block broadcasting in the next view, significantly decreasing latency. Our experimental evaluations of Mercury confirm its superior performance. Compared to the cutting-edge protocols, sMVBA, CKPS, and AMS, Mercury boosts throughput by 1.03X, 1.65X, and 2.51X, respectively.

High-performance network attack detection in unknown scenarios based on improved vertical model

High-performance network attack detection in unknown scenarios based on improved vertical model

Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset

Network security is a critical concern in today’s digital world, requiring efficient methods for the automatic detection and analysis of cyber attacks. This study uses the Kitsune Network Attack Dataset to explore network traffic behavior for IoT devices under various attack scenarios, including ARP MitM, SYN DoS, and Mirai Botnet. Utilizing Python-based data analysis tools, we preprocess and analyze millions of network packets to uncover patterns indicative of malicious activities. The study employs packet-level time-series analysis to visualize traffic patterns and detect anomalies specific to each attack type. Key findings include high packet volumes in attacks such as SSDP Flood and Mirai Botnet, with the Mirai Botnet attack involving multiple IP addresses and lasting over 2 hours. Notable attack-specific behaviors include high traffic on port -1 and targeted traffic on specific ports like 53195. The SYN DoS and Mirai Botnet attacks are characterized by their prolonged durations, suggesting significant disruption. Overall, the study highlights distinctive attack patterns and underscores the importance of understanding these characteristics to enhance detection and response mechanisms.

Detection of False Data Injection Attacks in Distribution Networks: A Vertical Federated Learning Approach

Detection of False Data Injection Attacks in Distribution Networks: A Vertical Federated Learning Approach

Network Attack Simulation and Defense System Optimization based on Big Data

Network Attack Simulation and Defense System Optimization based on Big Data

Design and Implementation of Internet of Things (IoT) Framework for Governing Modern Cyber Attacks in Computer Network

Design and Implementation of Internet of Things (IoT) Framework for Governing Modern Cyber Attacks in Computer Network

Blockchain-Enhanced Machine Learning for Robust Detection of APT Injection Attacks in the Cyber-Physical Systems

Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability to stealthy network attacks like ZDA and PDA, which can lead to unsafe states and system damage. Recent defense mechanisms for ZDA and PDA often rely on model-based observation techniques prone to false alarms. In this paper, we present an innovative approach to securing CPS against Advanced Persistent Threat (APT) injection attacks by integrating machine learning with blockchain technology. Our system leverages a robust ML model trained to detect APT injection attacks with high accuracy, achieving a detection rate of 99.89%. To address the limitations of current defense mechanisms and enhance the security and integrity of the detection process, we utilize blockchain technology to store and verify the predictions made by the ML model. We implemented a smart contract on the Ethereum blockchain using Solidity, which logs the input features and corresponding predictions. This immutable ledger ensures the integrity and traceability of the detection process, mitigating risks of data tampering and reducing false alarms, thereby enhancing trust in the system's outputs. The implementation includes a user-friendly interface for inputting features, a backend for data processing and model prediction, and a blockchain interaction module to store and verify predictions. The integration of blockchain with Machine learning enhances both the precision and resilience of APT detection while providing an additional layer of security by ensuring the transparency and immutability of the recorded data. This dual approach represents a substantial advancement in protecting CPS from sophisticated cyber threats.