A Hybrid CNN-Brown-Bear Optimization Framework for Enhanced Detection of URL Phishing Attacks

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial services.After the first reported incident in 1995,its impact keeps on increasing.Also,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing attacks.Many deep learning and machine learning techniques are available to detect phishing attacks.However,most of the techniques did not use efficient optimization techniques.In this context,our proposed model used random forest-based techniques to select the best features,and then the Brown-Bear optimization algorithm(BBOA)was used to fine-tune the hyper-parameters of the convolutional neural network(CNN)model.To test our model,we used a dataset from Kaggle comprising 11,000+websites.In addition to that,the dataset also consists of the 30 features that are extracted from the website uniform resource locator(URL).The target variable has two classes:“Safe”and“Phishing.”Due to the use of BBOA,our proposed model detects malicious URLs with an accuracy of 93%and a precision of 92%.In addition,comparing our model with standard techniques,such as GRU(Gated Recurrent Unit),LSTM(Long Short-Term Memory),RNN(Recurrent Neural Network),ANN(Artificial Neural Network),SVM(Support Vector Machine),and LR(Logistic Regression),presents the effectiveness of our proposed model.Also,the comparison with past literature showcases the contribution and novelty of our proposed model.

Optimal Spatial Allocation of Initial Attack Resources for Firefighting in the Republic of Korea Using a Scenario Optimization Model

【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.

Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems(NCS),wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses,thus changing the physical system dynamics.First,the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow.Next,an adaptive observer is proposed to generate the detection residual,which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold,followed by an estimation scheme for the signal injected by the attacker.A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics.The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator.The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived.Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

Optimization of projectile state and trajectory of reentry body considering attainment of carrying aircraft

As an improvement of traditional maximum range trajectory optimization, both the projectile state and control sequence of reentry body are optimized to achieve the maximum range. To ensure the optimal projectile state is in the reachable domain of the carrying aircraft, the climb of the carrying aircraft and the flight of reentry body are optimized as a whole based on the hp-adaptive pseudospectral method. Meanwhile, the path constraints, control constraints and initial/terminal states constraints are considered in the optimization as well. In face of the fact that the range of the optimal control solution resulted from the pseudospectral method may be unsmooth, virtual control variables (second derivatives of the angle of attack and bank angle) are proposed in the optimization, while the angle of attack and bank angle are treated as the virtual state variables to guarantee the smoothness of the optimal control solution. The validity of the virtual control variables in smoothing the optimal control solution is verified by simulation, and the optimality of the resulting projectile state and control sequence for reaching the maximum range is confirmed using the indirect approach. © 1990-2011 Beijing Institute of Aerospace Information.

An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

The Deep Neural Networks(DNN)training process is widely affected by backdoor attacks.The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers.The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger.To overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)algorithm.In the ICNN-GBO model,we are injecting the triggers via a steganography and regularization technique.We are generating triggers using a single-pixel,irregular shape,and different sizes.The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and functionality.When the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target label.The proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M dataset.The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

UAVs cooperative task assignment and trajectory optimization with safety and time constraints

This paper proposes new methods and strategies for Multi-UAVs cooperative attacks with safety and time constraints in a complex environment.Delaunay triangle is designed to construct a map of the complex flight environment for aerial vehicles.Delaunay-Map,Safe Flight Corridor(SFC),and Relative Safe Flight Corridor(RSFC)are applied to ensure each UAV flight trajectory's safety.By using such techniques,it is possible to avoid the collision with obstacles and collision between UAVs.Bezier-curve is further developed to ensure that multi-UAVs can simultaneously reach the target at the specified time,and the trajectory is within the flight corridor.The trajectory tracking controller is also designed based on model predictive control to track the planned trajectory accurately.The simulation and experiment results are presented to verifying developed strategies of Multi-UAV cooperative attacks.

Security of quantum key distribution using two-mode squeezed states against optimal beam splitter attack

For the beam splitter attack strategy against quantum key distribution using two-mode squeezed states, the analytical expression of the optimal beam splitter parameter is provided in this paper by applying the Shannon information theory. The theoretical secret information rate after error correction and privacy amplification is given in terms of the squeezed parameter and channel parameters. The results show that the two-mode squeezed state quantum key distribution is secure against an optimal beam splitter attack.

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm（CBOA） is proposed according to the characteristics of the function independence,conformity and supplementary between the electronic countermeasure（ECM） and the firepower attack systems.The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome.Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations,but also maintain the population multiplicity,and enhance the algorithm performance.The simulation result confirms the algorithm validity.

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service(DDoS)attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security.Existing detection methods cannot effectively detect early attacks.In this paper,we propose a detection method of DDoS attacks based on generalized multiple kernel learning(GMKL)combining with the constructed parameter R.The super-fusion feature value(SFV)and comprehensive degree of feature(CDF)are defined to describe the characteristic of attack flow and normal flow.A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm.A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter.The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection,and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.

Optimal Hybrid Deep Learning Enabled Attack Detection and Classificationin IoT Environment

The Internet of Things (IoT) paradigm enables end users to accessnetworking services amongst diverse kinds of electronic devices. IoT securitymechanism is a technology that concentrates on safeguarding the devicesand networks connected in the IoT environment. In recent years, False DataInjection Attacks (FDIAs) have gained considerable interest in the IoT environment.Cybercriminals compromise the devices connected to the networkand inject the data. Such attacks on the IoT environment can result in a considerableloss and interrupt normal activities among the IoT network devices.The FDI attacks have been effectively overcome so far by conventional threatdetection techniques. The current research article develops a Hybrid DeepLearning to Combat Sophisticated False Data Injection Attacks detection(HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD modelmajorly recognizes the presence of FDI attacks in the IoT environment.The HDL-FDIAD model exploits the Equilibrium Optimizer-based FeatureSelection (EO-FS) technique to select the optimal subset of the features.Moreover, the Long Short Term Memory with Recurrent Neural Network(LSTM-RNN) model is also utilized for the purpose of classification. At last,the Bayesian Optimization (BO) algorithm is employed as a hyperparameteroptimizer in this study. To validate the enhanced performance of the HDLFDIADmodel, a wide range of simulations was conducted, and the resultswere investigated in detail. A comparative study was conducted between theproposed model and the existing models. The outcomes revealed that theproposed HDL-FDIAD model is superior to other models.

Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

DDoS Attack Detection Scheme Based on Entropy and PSO-BP Neural Network in SDN

SDN (Software Defined Network) has many security problems, and DDoS attack is undoubtedly the most serious harm to SDN architecture network. How to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security research. Based on the characteristics of SDN, a DDoS attack detection method combining generalized entropy and PSOBP neural network is proposed. The traffic is pre-detected by the generalized entropy method deployed on the switch, and the detection result is divided into normal and abnormal. Locate the switch that issued the abnormal alarm. The controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal switch. Experiments show that compared with other methods, the detection accurate rate is guaranteed while the CPU load of the controller is reduced, and the detection capability is better.

Cyber Attack Protection and Control of Microgrids

Recently, the smart grid has been considered as a next-generation power system to modernize the traditional grid to improve its security, connectivity, efficiency and sustainability.Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economical, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional(RSC) code and Kalman filter(KF) based method in the context of smart grids.Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information, which is affected by random noises and cyber attacks. Once the estimated states are obtained by KF algorithm, a semidefinite programming based optimal feedback controller is proposed to regulate the system states, so that the power system can operate properly. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,etc.Although Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber criminals.Distributed Denial of Service(DDoS)is the most common type of attack on cloud computing.Consequently,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks.Since DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks.Further,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent.In this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD dataset.In Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction error.This deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM model.Moreover,it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks.The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

ACHIEVING OPTIMAL ADVERSARIAL ACCURACY FOR ADVERSARIAL DEEP LEARNING USING STACKELBERG GAMES

The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks,and this is one of the major research focuses of deep learning.Game theory has been used to answer some of the basic questions about adversarial deep learning,such as those regarding the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers.In most previous works,adversarial deep learning was formulated as a simultaneous game and the strategy spaces were assumed to be certain probability distributions in order for the Nash equilibrium to exist.However,this assumption is not applicable to practical situations.In this paper,we give answers to these basic questions for the practical case where the classifiers are DNNs with a given structure;we do that by formulating adversarial deep learning in the form of Stackelberg games.The existence of Stackelberg equilibria for these games is proven.Furthermore,it is shown that the equilibrium DNN has the largest adversarial accuracy among all DNNs with the same structure,when Carlini-Wagner s margin loss is used.The trade-off between robustness and accuracy in adversarial deep learning is also studied from a game theoretical perspective.