Different Types Of Attacks Research Articles

A divide-and-conquer reconstruction method for defending against adversarial example attacks

In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However, existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks. Such a strategy may miss the nuances between different types of attacks, limiting the comprehensiveness and effectiveness of the defense strategy. To address this issue, we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks. The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks. Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification, which can hardly be achieved by existing adversarial example detection methods. In addition, we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack, ensuring optimal defense effectiveness. Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods.

Review of Knowledge on the Graphs of Cyber-Attacks by 2024 for IBIMA Publishing Journals

An attack graph is a visual tool used to represent potential attack paths, that a cybercriminal can use to gain access to a computer system or network. It consists of nodes and edges, where Nodes represent resources such as computers, servers, applications or data. Edges represent dependencies between nodes, such as network connections or data flows. Attack graphs are visual tools used to analyse and understand different types of attacks, including cyber-attacks, physical attacks and terrorist attacks. These visualisations help security professionals, analysts and decision makers to identify trends, assess risks and develop defence strategies. Attack graphs are a valuable tool that can help organisations identify, assess and mitigate cyber security risks. They are easy to use and can be customised to suit different needs.

An optimized and intelligent metaverse intrusion detection system based on rough sets

The convergence of the Internet of Things (IoT) and the Metaverse is revolutionizing the digital world by providing immersive, interactive environments as well as new data transmission opportunities. However, this rapid integration raises complex security issues, including an increased risk of unlawful access and data breaches. Strong cybersecurity measures are required to identify and prevent these attacks, preserving the security and confidentiality of users. Finding significant features for recognizing malicious attacks and enhancing the accuracy of network intrusion detection in general, and particularly in the virtual environment, are some of the significant research needs. This paper introduces an intelligent intrusion detection system (IIDS) based on rough set-based electric eel foraging optimization (RSEEFO) in conjunction with the AdaBoost-based classification algorithm. The main objective of this system is to detect and recognize different types of attacks on the interaction and connectivity between the IoT and the metaverse. The proposed IIDS-RSEEFO consists of three phases, which are data pre-processing, multi-IoT attack classification, and evaluation. The main problems associated with the adopted dataset are handled in data pre-processing. Then, in the second phase, a one-versus-all approach is employed along with RSEEFO and AdaBoost to handle the multi-class classification problem. Finally, several evaluation metrics are employed to assess the reliability and robustness of the proposed IIDS-RSEEFO. The proposed IIDS was tested on CIC-IoT-2023 and validated on UNSWNB-15. It achieved high accuracy across all attack types of CIC-IoT-2023, with accuracies of 99.7 %, 100 %, 100 %, 99.8 %, 100 %, 100 %, 99.8 %, and 100 % for benign traffic, DDoS, brute force, spoofing, DoS, Mirai, recon, and web-Based respectively, accompanied by robust sensitivity, F1-Score, Specificity, G-Mean, and crossover-error rate metrics demonstrating its effectiveness in accurately predicting each attack type. Additionally, it obtained high accuracy for all attack types of UNSWNB-15, with accuracies of 96.48 %, 99.12 %, 99.24 %, 93.78 %, 92.55 %, 92.55 %, 92.55 %, 94.73 %, 94.73 %, 98.09 %, 98.39 %, 99.50 %, and 99.95 % for analysis, backdoor, DoS, exploits, fuzzers, generic, normal, reconnaissance, shellcode, and worms, respectively. In addition, the results evaluated that the proposed model is superior compared to the existing intrusion detection systems.

Efficient detection of intra/inter-frame video copy-move forgery: A hierarchical coarse-to-fine method

With a simple forgery technique but a realistic result, video copy-move forgery has currently become one of the most popular tampering manners. In the last couple of years, various new techniques deriving from machine intelligence and pattern recognition have been widely proposed for image forensics. However, it still faces a very challenging task in the field of video copy-move forgery for four reasons: i) Low F1 score and high false-alarm; ii) Lack of a synthesis processing framework; iii) Weak detection robustness and accuracy; iv) Low efficiency. A novel Hierarchical Coarse-to-Fine framework for effective video copy-move forgery detection is proposed to overcome these challenges: i) In the coarse forgery frame-pair matching, the coarse copy-move frame-pairs matching algorithm with the newly proposed two-pass filters can locate real forgery frame-pairs (FFP) and also reduce false-alarm. ii) Through further analysis of the actual FFP, the detection of intra-frame and inter-frame copy-move forgeries can be accurately and simultaneously determined. iii) In the fine keypoint-pairs matching, our newly designed two-hierarchical keypoint-pair filtering can accurately localize the forgery region at pixel level under various adverse conditions. iv) The novel Hierarchical Coarse-to-Fine framework (together with the newly designed algorithms above) considers only the real FFP and true keypoint-pairs for computation, resulting in higher efficiency and accuracy. Finally, Delaunay Triangulation-based region filling is employed to indicate the forgery regions. Compared to the latest methods, our algorithm has been tested extensively and found to be the best at detecting forgeries, with a top score of F1=0.77 and no false-alarms, even under different types of attacks, as validated by the well-known GRIP dataset.

Convolutional Neural Networks Based Detection System for Cyber-attacks in Industrial Control Systems

Convolutional Neural Networks (CNNs) have emerged as a powerful tool for various pattern recognition tasks, including the detection of cyber-attacks in Industrial Control Systems (ICS). This paper presents a CNN-based detection system specifically designed to safeguard ICS from sophisticated cyber threats. The proposed system leverages the capability of CNNs to automatically learn and extract high-level features from raw data, enabling it to identify anomalies indicative of cyber-attacks with high accuracy. Utilizing a comprehensive cyber-attack dataset containing 59 different types of attacks, the system is trained to distinguish between normal and malicious traffic effectively. Based on the extensive dataset, this study demonstrates that the CNN-based detection system achieves a detection rate of 98.5% and a false-positive rate of 1.2%, significantly outperforming traditional methods. The high detection rate indicates the system's ability to accurately identify a wide range of attack types, while the low false-positive rate ensures minimal disruption to normal operations due to incorrect alerts. These results underscore the system's robustness and reliability in identifying subtle and complex attack patterns. Moreover, the CNN-based system is designed to adapt to new types of attacks as it continues to learn from updated datasets, making it a scalable and future-proof solution. Implementing this system can significantly enhance the cybersecurity posture of industrial environments by providing real-time monitoring and rapid response capabilities. The CNN-based detection system offers a significant advancement in ICS cybersecurity. Effectively identifying and mitigating cyber-attacks contributes to the resilience and reliability of critical infrastructure. The proposed approach improves security measures and ensures industrial processes' continuous and safe operation, which is vital for economic stability and public safety.

Federated Bayesian optimization XGBoost model for cyberattack detection in internet of medical things

BackgroundHospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality. AimThis study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks. MethodologyThe proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs). ResultsThe performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced. ConclusionThe findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.

Color Image Encryption Based on an Evolutionary Codebook and Chaotic Systems.

Encryption of images is an important method that can effectively improve the security and privacy of crucial image data. Existing methods generally encrypt an image with a combination of scrambling and encoding operations. Currently, many applications require highly secure results for image encryption. New methods that can achieve improved randomness for both the scrambling and encoding processes in encryption are thus needed to further enhance the security of a cipher image. This paper proposes a new method that can securely encrypt color images. As the first step of the proposed method, a complete bit-level operation is utilized to scramble the binary bits in a color image to a full extent. For the second step, the bits in the scrambled image are processed with a sweeping operation to improve the encryption security. In the final step of encryption, a codebook that varies with evolutionary operations based on several chaotic systems is utilized to encrypt the partially encrypted image obtained in the second step. Experimental results on benchmark color images suggest that this new approach can securely encrypt color images and generate cipher images that remain secure under different types of attacks. The proposed approach is compared with several other state-of-the-art encryption approaches and the results show that it can achieve improved encryption security for cipher images. Experimental results thus suggest that this new approach can possibly be utilized practically in applications where color images need to be encrypted for content protection.

Analysis of Attack Intensity on Autonomous Mobile Robots

Autonomous mobile robots (AMRs) combine a remarkable combination of mobility, adaptability, and an innate capacity for obstacle avoidance. They are exceptionally well-suited for a wide range of applications but usually operate in uncontrolled, non-deterministic environments, so the analysis and classification of security events are very important for their safe operation. In this regard, we considered the influence of different types of attacks on AMR navigation systems to subdivide them into classes and unified the effect of attacks on the system through their level of consequences and impact. Then, we built a model of an attack on a system, taking into account five methods of attack implementation and identified the unified response thresholds valid for any type of parameter, which allows for creating universal correlation rules and simplifies this process, as the trigger threshold is related to the degree of impact that the attack has on the finite subsystem. Also, we developed a methodology for classifying incidents and identifying key components of the system based on ontological models, which makes it possible to predict risks and select the optimal system configuration. The obtained results are important in the context of separating different types of destructive effects based on attack classes. Our study showed that it is sometimes difficult to divide spoofing attacks into classes by assessing only one parameter since the attacker can use a complex attack scenario, mixing the stages of the scenarios. We then showed how adding an attack intensity factor can make classification more flexible. The connections between subsystems and parameters, as well as the attack impact patterns, were determined. Finally, a set of unique rules was developed to classify destructive effects with uniform response thresholds for each parameter. In this case, we can increase the number of parameters as well as the type of parameter value.

A dual-head output network attack detection and classification approach for multi-energy systems

In today’s digital age, multi-energy systems (MES) have become an indispensable part of the social infrastructure, providing people with diversified energy support such as electricity, gas, water and so on. However, with the increasing popularity and networking of MES, the network security threats they face are becoming more and more serious, especially the threat of network attacks. This makes it essential to detect attacks on MES and precisely classify attack types in order to establish effective defense strategies. In this paper, a Dual-Head output network attack detection and classification method based on parallel CNN-BiLSTM network is proposed. The method adopts a parallel structure and can process different aspects of information at the same time, speeding up the training and inference process of the whole network, making the system respond more quickly to potential network attacks, and improving real-time and efficiency. The multi-model fusion structure can give full play to the advantages of CNN and BiLSTM in processing different types of data, so that the system can capture attack characteristics more comprehensively in many aspects, and improve the overall detection and classification performance. The dual-head output not only improves the system’s ability to accurately detect attacks, but also can effectively classify different types of attacks in detail, which helps to formulate more targeted defense strategies. In addition, in order to effectively evaluate our proposed method, the network traffic data required for the experiment were collected in an environment very similar to the actual operating environment of a multi-energy system. Finally, the experiment verifies that our method can not only realize effective detection of network attacks, but also accurately classify different types of attacks.

Risk and Security Attacks in Physical Layer of IoT Environment

The Internet of Things (IoT) is innovative, intensive technology and supports wide areas of application, such as Smart environment, Healthcare, Security, industrial environment, and massive technology that inaugurate associated with real things. It also improves the living standard of society. These smart things are revolutionary and helping human beings in different ways but on the other hand security threats are growing rapidly and attackers are influencing the life of a man. Whereas, when technologies become developed, it carries a weakness that are continuously exploited and targeted. Security risk and threats directly concern with IoT environment and one of the main challenges of today, numerous issues need to be addressed within a physical layer of IoT Environment. In this paper, we have discussed the risk assessments and specifically pointed out the physical layer. We proposed a detailed ontology and highlighted the challenges, threats and different types of attacks in the IoT environment. We briefly debate the importance of the physical layer and emphasis the challenges it faces in the IoT environment and how hackler can attack this layer using tapping and QoS attacks.

A frequency-injection backdoor attack against DNN-Based finger vein verification

Recent research has demonstrated that deep neural networks, typically used in finger vein recognition, are susceptible to different types of attacks, such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among the attacks, backdoor attacks occur in almost every stage of the deep learning pipeline. Finger vein recognition has extensively been used in real-world applications for personal identity authentication. To develop a secure finger vein recognition system, one must study possible backdoor attacks, which can embed hidden malicious behaviors into the system. Existing backdoor attacks are as easily perceptible as conspicuous spatial triggers and difficult-to-resist data augmentation. To address this issue, we propose a novel frequency-injection-based backdoor attack method capable of delivering attacks in finger vein recognition. Specifically, images are transformed from the spatial domain to the frequency domain by discrete wavelet transform (DWT), and the trigger injects several times in the high-frequency part in the vertical direction. Experimental results of public finger vein datasets validate the proposed method's effectiveness, showing good attack performance and bypassing backdoor defense.

Analysis and Improvement of an Authentication Scheme for Fog Computing Services

Fog computing utilizes devices in the edge network to transmit data with very low latency and supports high mobility. However, fog computing inherits security and privacy problems from cloud computing. Therefore, various privacy schemes for fog computing have been proposed to prevent different types of attacks. Recently, Weng et al proposed a fog computing authentication scheme; after analyzing, we found that Weng et al's scheme cannot resist user tracking attack and user impersonation attack. Then, we propose an improved scheme through adding a password, modifying the calculation method of Ei, and adding timestamps. In addition, we also compare the improved scheme with existing authentication schemes in terms of security and computational efficiency. The results show that the improved scheme is more secure and has less computation.

Robust biometric identity authentication scheme using quantum voice encryption and quantum secure direct communications for cybersecurity

A significant problem facing cybersecurity is that most client/server identity authentication schemes depend on computational complexity which is recently challenged by the super exponential computational abilities of quantum computers. Quantum cryptography with its quantum mechanics principles solves this problem and provides mathematically un-hackable secure communication for the quantum computers epoch. Biometric authentication has a big challenge related to its protection as it has to be submitted and stored in encrypted form. Unfortunately, most existing biometric cryptosystems rely on classical cryptography, so they are insecure in quantum era. This problem is solved in this paper by introducing a novel three-factor biometric quantum identity authentication scheme combining something you know (password), something you produce (client’s voice), and something you are (client’s face image) is proposed. The scheme consists of two phases: client enrollment and client login/authentication. The methodology is to use in the enrollment phase, two layers of quantum encryption for the client’s voice signal. Each layer consists of two simple quantum circuits of SWAP and C-NOT quantum gates respectively using a different pseudorandom key obtained from a cellular automata (CA) pseudorandom generator. The proposed scheme has many merits. Firstly, it introduces a new design of muti-factors quantum biometric authentication scheme as the biometric voice data is processed, transmitted, and stored at the server database in an encrypted unconditionally secure quantum form which provides parallel processing, real-time communications, inherent level of security against attacks, tamper-proof security and exponential powerful storage capability due to the quantum properties. Secondly, the proposed scheme achieves double-layers security by combing quantum voice encryption and quantum secure direct communication in one process such that the quantum channel eavesdropper cannot obtain any valuable information in case of information leakage. Thirdly, the distribution and management of encryption key problem is solved as there is no need for key exchange prior to communication. Fourthly, it achieves high communication efficiency as although it is a three-factor biometric identity authentication, only one biometric data is sent in quantum encrypted form to the server. Fifthly, the numerical simulations showed that the proposed scheme has large key space, high key sensitivity and more robust performance against different types of attacks compared to other existing classical and quantum voice encryption schemes. Finally, as an identity authentication method, the proposed scheme achieves identification accuracy of 100% with zero False Rejection Rate (FRR) and zero False acceptance rate (FAR) which is perfect.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

A novel approach detection for IIoT attacks via artificial intelligence

The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices.

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

Realization of Authenticated One-Pass Key Establishment on RISC-V Micro-Controller for IoT Applications

Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to guarantee authenticity, confidentiality, and data integrity. Unlike traditional computing systems, sensor node devices are often limited regarding memory and computing power. Lightweight communication protocols, such as LoRaWAN, were introduced to overcome these limitations. However, despite the lightweight feature, the protocol is vulnerable to different types of attacks. This proposal presents a highly secure key establishment protocol that combines two cryptography schemes: Elliptic Curve Qu–Vanstone and signcryption key encapsulation. The protocol provides a method to establish a secure channel that inherits the security properties of the two schemes. Also, it allows for fast rekeying with only one exchange message, significantly reducing the handshake complexity in low-bandwidth communication. In addition, the selected schemes complement each other and share the same mathematical operations in elliptic curve cryptography. Moreover, with the rise of a community-friendly platform like RISC-V, we implemented the protocol on a RISC-V system to evaluate its overheads regarding the cycle count and execution time.

Digital Image Watermarking

Digital watermarking has become an essential method for encrypting multimedia files by adding undetectable data. In this work, we present a novel approach to digital watermarking that makes use of the Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) methods. By combining these methods, robustness against different types of attacks can be achieved while preserving excellent perceptual quality. Our approach ensures resilience and invisibility by embedding the watermark in the singular values that are obtained from the decomposition of DWT subbands. The outcomes of the experiments show that the suggested strategy is more efficient and effective than the current techniques when it comes to maintaining image quality and being resistant to common image processing threats.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Hen maternal care inspired optimization framework for attack detection in wireless smart grid network

In the power grid, communication networks play an important role in exchanging smart grid-based information. In contrast to wired communication, wireless communication offers many benefits in terms of easy setup connections and low-cost high-speed links. Conversely, wireless communications are commonly more vulnerable to security threats than wired ones. All power equipment devices and appliances in the smart distribution grid (SDG) are communicated through wireless networks only. Most security research focuses on keeping the SDG network from different types of attacks. The denial-of-service (DoS) attack is consuming more energy in the network leads to a permanent breakdown of memory. This work proposes a new metaheuristic optimization inspired by maternal care of hen to their children called hen maternal care (HMCO) inspired optimization. The HMCO algorithm mimics the care shown by hen for their children in nature. The mother hen is always watchful and protects its chicks against predators. All chickens utilize different calls to designate flying predators like falcons and owls from ground seekers like foxes and coyotes, showing that they can both survey a danger and advise different chickens how to set themselves up. Our method shows greater performance among other standard algorithms.