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Simsek, Ozlem Imik, Alagoz, Baris Baykant.  2021.  A Computational Intelligent Analysis Scheme for Optimal Engine Behavior by Using Artificial Neural Network Learning Models and Harris Hawk Optimization. 2021 International Conference on Information Technology (ICIT). :361—365.
Application of computational intelligence methods in data analysis and optimization problems can allow feasible and optimal solutions of complicated engineering problems. This study demonstrates an intelligent analysis scheme for determination of optimal operating condition of an internal combustion engine. For this purpose, an artificial neural network learning model is used to represent engine behavior based on engine data, and a metaheuristic optimization method is implemented to figure out optimal operating states of the engine according to the neural network learning model. This data analysis scheme is used for adjustment of optimal engine speed and fuel rate parameters to provide a maximum torque under Nitrous oxide emission constraint. Harris hawks optimization method is implemented to solve the proposed optimization problem. The solution of this optimization problem addresses eco-friendly enhancement of vehicle performance. Results indicate that this computational intelligent analysis scheme can find optimal operating regimes of an engine.
Tartaglione, Enzo, Grangetto, Marco, Cavagnino, Davide, Botta, Marco.  2021.  Delving in the loss landscape to embed robust watermarks into neural networks. 2020 25th International Conference on Pattern Recognition (ICPR). :1243—1250.
In the last decade the use of artificial neural networks (ANNs) in many fields like image processing or speech recognition has become a common practice because of their effectiveness to solve complex tasks. However, in such a rush, very little attention has been paid to security aspects. In this work we explore the possibility to embed a watermark into the ANN parameters. We exploit model redundancy and adaptation capacity to lock a subset of its parameters to carry the watermark sequence. The watermark can be extracted in a simple way to claim copyright on models but can be very easily attacked with model fine-tuning. To tackle this culprit we devise a novel watermark aware training strategy. We aim at delving into the loss landscape to find an optimal configuration of the parameters such that we are robust to fine-tuning attacks towards the watermarked parameters. Our experimental results on classical ANN models trained on well-known MNIST and CIFAR-10 datasets show that the proposed approach makes the embedded watermark robust to fine-tuning and compression attacks.
Liu, Xian.  2021.  A Primitive Cipher with Machine Learning. 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1—6.
Multi-access edge computing (MEC) equipped with artificial intelligence is a promising technology in B5G wireless systems. Due to outsourcing and other transactions, some primitive security modules need to be introduced. In this paper, we design a primitive cipher based on double discrete exponentiation and double discrete logarithm. The machine learning methodology is incorporated in the development. Several interesting results are obtained. It reveals that the number of key-rounds is critically important.
Mbanaso, U. M., Makinde, J. A..  2021.  Conceptual Modelling of Criticality of Critical Infrastructure Nth Order Dependency Effect Using Neural Networks. 2020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA). :127—131.
This paper presents conceptual modelling of the criticality of critical infrastructure (CI) nth order dependency effect using neural networks. Incidentally, critical infrastructures are usually not stand-alone, they are mostly interconnected in some way thereby creating a complex network of infrastructures that depend on each other. The relationships between these infrastructures can be either unidirectional or bidirectional with possible cascading or escalating effect. Moreover, the dependency relationships can take an nth order, meaning that a failure or disruption in one infrastructure can cascade to nth interconnected infrastructure. The nth-order dependency and criticality problems depict a sequential characteristic, which can result in chronological cyber effects. Consequently, quantifying the criticality of infrastructure demands that the impact of its failure or disruption on other interconnected infrastructures be measured effectively. To understand the complex relational behaviour of nth order relationships between infrastructures, we model the behaviour of nth order dependency using Neural Network (NN) to analyse the degree of dependency and criticality of the dependent infrastructure. The outcome, which is to quantify the Criticality Index Factor (CIF) of a particular infrastructure as a measure of its risk factor can facilitate a collective response in the event of failure or disruption. Using our novel NN approach, a comparative view of CIFs of infrastructures or organisations can provide an efficient mechanism for Critical Information Infrastructure Protection and resilience (CIIPR) in a more coordinated and harmonised way nationally. Our model demonstrates the capability to measure and establish the degree of dependency (or interdependency) and criticality of CIs as a criterion for a proactive CIIPR.
ERÇİN, Mehmet Serhan, YOLAÇAN, Esra Nergis.  2021.  A system for redicting SQLi and XSS Attacks. 2021 International Conference on Information Security and Cryptology (ISCTURKEY). :155—160.
In this study, it is aimed to reduce False-Alarm levels and increase the correct detection rate in order to reduce this uncertainty. Within the scope of the study, 13157 SQLi and XSS type malicious and 10000 normal HTTP Requests were used. All HTTP requests were received from the same web server, and it was observed that normal requests and malicious requests were close to each other. In this study, a novel approach is presented via both digitization and expressing the data with words in the data preprocessing stages. LSTM, MLP, CNN, GNB, SVM, KNN, DT, RF algorithms were used for classification and the results were evaluated with accuracy, precision, recall and F1-score metrics. As a contribution of this study, we can clearly express the following inferences. Each payload even if it seems different which has the same impact maybe that we can clearly view after the preprocessing phase. After preprocessing we are calculating euclidean distances which brings and gives us the relativity between expressions. When we put this relativity as an entry data to machine learning and/or deep learning models, perhaps we can understand the benign request or the attack vector difference.
Manoj, B. R., Sadeghi, Meysam, Larsson, Erik G..  2021.  Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network. ICC 2021 - IEEE International Conference on Communications. :1–6.
Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.
Huong, Truong Thu, Bac, Ta Phuong, Long, Dao Minh, Thang, Bui Doan, Luong, Tran Duc, Binh, Nguyen Thanh.  2021.  An Efficient Low Complexity Edge-Cloud Framework for Security in IoT Networks. 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). :533—539.

Internet of Things (IoT) and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge-cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the cloud's workload. We also propose a multi-attack detection mechanism called LCHA (Low-Complexity detection solution with High Accuracy) , which has low complexity for deployment at the edge zone while still maintaining high accuracy. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LCHA outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.

Dalvi, Jai, Sharma, Vyomesh, Shetty, Ruchika, Kulkarni, Sujata.  2021.  DDoS Attack Detection using Artificial Neural Network. 2021 International Conference on Industrial Electronics Research and Applications (ICIERA). :1—5.
Distributed denial of service (DDoS) attacks is one of the most evolving threats in the current Internet situation and yet there is no effective mechanism to curb it. In the field of DDoS attacks, as in all other areas of cybersecurity, attackers are increasingly using sophisticated methods. The work in this paper focuses on using Artificial Neural Network to detect various types of DDOS attacks(UDP-Flood, Smurf, HTTP-Flood and SiDDoS). We would be mainly focusing on the network and transport layer DDoS attacks. Additionally, the time and space complexity is also calculated to further improve the efficiency of the model implemented and overcome the limitations found in the research gap. The results obtained from our analysis on the dataset show that our proposed methods can better detect the DDoS attack.
Bernardi, Simona, Javierre, Raúl, Merseguer, José, Requeno, José Ignacio.  2021.  Detectors of Smart Grid Integrity Attacks: an Experimental Assessment. 2021 17th European Dependable Computing Conference (EDCC). :75–82.
Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.
Cristescu, Mihai-Corneliu, Bob, Cristian.  2021.  Flexible Framework for Stimuli Redundancy Reduction in Functional Verification Using Artificial Neural Networks. 2021 International Symposium on Signals, Circuits and Systems (ISSCS). :1—4.
Within the ASIC development process, the phase of functional verification is a major bottleneck that affects the product time to market. A technique that decreases the time cost for reaching functional coverage closure is reducing the stimuli redundancy during the test regressions. This paper addresses such a solution and presents a novel, efficient, and scalable implementation that harnesses the power of artificial neural networks. This article outlines the concept strategy, highlights the framework structure, lists the experimental results, and underlines future research directions.
Aldossary, Lina Abdulaziz, Ali, Mazen, Alasaadi, Abdulla.  2021.  Securing SCADA Systems against Cyber-Attacks using Artificial Intelligence. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). :739—745.
Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems
Kim, Won-Jae, Kim, Sang-Hoon.  2021.  Multiple Open-Switch Fault Diagnosis Using ANNs for Three-Phase PWM Converters. 2021 24th International Conference on Electrical Machines and Systems (ICEMS). :2436–2439.
In this paper, a multiple switches open-fault diagnostic method using ANNs (Artificial Neural Networks) for three-phase PWM (Pulse Width Modulation) converters is proposed. When an open-fault occurs on switches in the converter, the stator currents can include dc and harmonic components. Since these abnormal currents cannot be easily cut off by protection circuits, secondary faults can occur in peripherals. Therefore, a method of diagnosing the open-fault is required. For open-faults for single switch and double switches, there are 21 types of fault modes depending on faulty switches. In this paper, these fault modes are localized by using the dc component and THD (Total Harmonics Distortion) in fault currents. For obtaining the dc component and THD in the currents, an ADALINE (Adaptive Linear Neuron) is used. For localizing fault modes, two ANNs are used in series; the 21 fault modes are categorized into six sectors by the first ANN of using the dc components, and then the second ANN localizes fault modes by using both the dc and THDs of the d-q axes current in each sector. Simulations and experiments confirm the validity of the proposed method.
Liu, Yongchao, Zhu, Qidan.  2021.  Adaptive Neural Network Asymptotic Tracking for Nonstrict-Feedback Switched Nonlinear Systems. 2021 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC). :25–30.
This paper develops an adaptive neural network (NN) asymptotic tracking control scheme for nonstrict-feedback switched nonlinear systems with unknown nonlinearities. The NNs are used to dispose the unknown nonlinearities. Different from the published results, the asymptotic convergence character is achieved based on the bound estimation method. By combining some smooth functions with the adaptive backstepping scheme, the asymptotic tracking control strategy is presented. It is proved that the fabricated scheme can guarantee that the system output can asymptotically follow the desired signal, and also that all signals of the entire system are bounded. The validity of the devised scheme is evaluated by a simulation example.
Roy, Debaleena, Guha, Tanaya, Sanchez, Victor.  2021.  Graph Based Transforms based on Graph Neural Networks for Predictive Transform Coding. 2021 Data Compression Conference (DCC). :367–367.
This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NNis constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping functionis to design a GBT that performs as well as the KLT without requiring to explicitly com-pute the covariance matrix for each residual block to be transformed. To avoid signallingany additional information required to compute the inverse GBT-NN, we also introduce acoding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentageof preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT.
Sapre, Suchet, Islam, Khondkar, Ahmadi, Pouyan.  2021.  A Comprehensive Data Sampling Analysis Applied to the Classification of Rare IoT Network Intrusion Types. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1–2.
With the rapid growth of Internet of Things (IoT) network intrusion attacks, there is a critical need for sophisticated and comprehensive intrusion detection systems (IDSs). Classifying infrequent intrusion types such as root-to-local (R2L) and user-to-root (U2R) attacks is a reoccurring problem for IDSs. In this study, various data sampling and class balancing techniques-Generative Adversarial Network (GAN)-based oversampling, k-nearest-neighbor (kNN) oversampling, NearMiss-1 undersampling, and class weights-were used to resolve the severe class imbalance affecting U2R and R2L attacks in the NSL-KDD intrusion detection dataset. Artificial Neural Networks (ANNs) were trained on the adjusted datasets, and their performances were evaluated with a multitude of classification metrics. Here, we show that using no data sampling technique (baseline), GAN-based oversampling, and NearMiss-l undersampling, all with class weights, displayed high performances in identifying R2L and U2R attacks. Of these, the baseline with class weights had the highest overall performance with an F1-score of 0.11 and 0.22 for the identification of U2R and R2L attacks, respectively.
Yedukondalu, G., Bindu, G. Hima, Pavan, J., Venkatesh, G., SaiTeja, A..  2021.  Intrusion Detection System Framework Using Machine Learning. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). :1224–1230.
Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.
Yao, Chunxing, Sun, Zhenyao, Xu, Shuai, Zhang, Han, Ren, Guanzhou, Ma, Guangtong.  2021.  Optimal Parameters Design for Model Predictive Control using an Artificial Neural Network Optimized by Genetic Algorithm. 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA). :1–6.
Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error (ferr) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and ferr under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and ferr can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model.
Al-Ameer, Ali, AL-Sunni, Fouad.  2021.  A Methodology for Securities and Cryptocurrency Trading Using Exploratory Data Analysis and Artificial Intelligence. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). :54–61.
This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.
Kalinin, Maxim O., Krundyshev, Vasiliy M..  2021.  Computational Intelligence Technologies Stack for Protecting the Critical Digital Infrastructures against Security Intrusions. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :118–122.
Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.
Schrenk, Bernhard.  2021.  Simplified Synaptic Receptor for Coherent Optical Neural Networks. 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM). :1–2.
Advancing artificial neural networks to the coherent optical domain offers several advantages, such as a filterless synaptic interconnect with increased routing flexibility. Towards this direction, a coherent synaptic receptor with integrated multiplication function will be experimentally evaluated for a 1-GHz train of 130-ps spikes.
Viktoriia, Hrechko, Hnatienko, Hrygorii, Babenko, Tetiana.  2021.  An Intelligent Model to Assess Information Systems Security Level. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :128–133.
This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.
Paul, Avishek, Islam, Md Rabiul.  2021.  An Artificial Neural Network Based Anomaly Detection Method in CAN Bus Messages in Vehicles. 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). :1–5.
Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).
Sallam, Youssef F., Ahmed, Hossam El-din H., Saleeb, Adel, El-Bahnasawy, Nirmeen A., El-Samie, Fathi E. Abd.  2021.  Implementation of Network Attack Detection Using Convolutional Neural Network. 2021 International Conference on Electronic Engineering (ICEEM). :1–6.
The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.
Jianhua, Xing, Jing, Si, Yongjing, Zhang, Wei, Li, Yuning, Zheng.  2021.  Research on Malware Variant Detection Method Based on Deep Neural Network. 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). :144–147.
To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.
Abdullah, Rezhna M., Abdullah, Syamnd M., Abdullah, Saman M..  2021.  Neighborhood Component Analysis and Artificial Neural Network for DDoS Attack Detection over IoT Networks. 2021 7th International Engineering Conference ``Research Innovation amid Global Pandemic" (IEC). :1–6.
Recently, modern networks have been made up of connections of small devices that have less memory, small CPU capability, and limited resources. Such networks apparently known as Internet of Things networks. Devices in such network promising high standards of live for human, however, they increase the size of threats lead to bring more risks to network security. One of the most popular threats against such networks is known as Distributed Denial of Service (DDoS). Reports from security solution providers show that number of such attacks are in increase considerably. Therefore, more researches on detecting the DDoS attacks are necessary. Such works need monitoring network packets that move over Internet and networks and, through some intelligent techniques, monitored packets could be classified as benign or as DDoS attack. This work focuses on combining Neighborhood Component Analysis and Artificial Neural Network-Backpropagation to classify and identify packets as forward by attackers or as come from authorized and illegible users. This work utilized the activities of four type of the network protocols to distinguish five types of attacks from benign packets. The proposed model shows the ability of classifying packets to normal or to attack classes with an accuracy of 99.4%.