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Sarasjati, Wendy, Rustad, Supriadi, Purwanto, Santoso, Heru Agus, Muljono, Syukur, Abdul, Rafrastara, Fauzi Adi, Ignatius Moses Setiadi, De Rosal.  2022.  Comparative Study of Classification Algorithms for Website Phishing Detection on Multiple Datasets. 2022 International Seminar on Application for Technology of Information and Communication (iSemantic). :448–452.
Phishing has become a prominent method of data theft among hackers, and it continues to develop. In recent years, many strategies have been developed to identify phishing website attempts using machine learning particularly. However, the algorithms and classification criteria that have been used are highly different from the real issues and need to be compared. This paper provides a detailed comparison and evaluation of the performance of several machine learning algorithms across multiple datasets. Two phishing website datasets were used for the experiments: the Phishing Websites Dataset from UCI (2016) and the Phishing Websites Dataset from Mendeley (2018). Because these datasets include different types of class labels, the comparison algorithms can be applied in a variety of situations. The tests showed that Random Forest was better than other classification methods, with an accuracy of 88.92% for the UCI dataset and 97.50% for the Mendeley dataset.
Alkawaz, Mohammed Hazim, Joanne Steven, Stephanie, Mohammad, Omar Farook, Gapar Md Johar, Md.  2022.  Identification and Analysis of Phishing Website based on Machine Learning Methods. 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE). :246–251.
People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree.
Syafiq Rohmat Rose, M. Amir, Basir, Nurlida, Nabila Rafie Heng, Nur Fatin, Juana Mohd Zaizi, Nurzi, Saudi, Madihah Mohd.  2022.  Phishing Detection and Prevention using Chrome Extension. 2022 10th International Symposium on Digital Forensics and Security (ISDFS). :1–6.
During pandemic COVID-19 outbreaks, number of cyber-attacks including phishing activities have increased tremendously. Nowadays many technical solutions on phishing detection were developed, however these approaches were either unsuccessful or unable to identify phishing pages and detect malicious codes efficiently. One of the downside is due to poor detection accuracy and low adaptability to new phishing connections. Another reason behind the unsuccessful anti-phishing solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. Therefore, in this work, an intelligent phishing detection and prevention model is designed. The proposed model employs a self-destruct detection algorithm in which, machine learning, especially supervised learning algorithm was used. All employed rules in algorithm will focus on URL-based web characteristic, which attackers rely upon to redirect the victims to the simulated sites. A dataset from various sources such as Phish Tank and UCI Machine Learning repository were used and the testing was conducted in a controlled lab environment. As a result, a chrome extension phishing detection were developed based on the proposed model to help in preventing phishing attacks with an appropriate countermeasure and keep users aware of phishing while visiting illegitimate websites. It is believed that this smart phishing detection and prevention model able to prevent fraud and spam websites and lessen the cyber-crime and cyber-crisis that arise from year to year.
Samuel, Henry D, Kumar, M Santhanam, Aishwarya, R., Mathivanan, G..  2022.  Automation Detection of Malware and Stenographical Content using Machine Learning. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). :889–894.
In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.
Ashlam, Ahmed Abadulla, Badii, Atta, Stahl, Frederic.  2022.  A Novel Approach Exploiting Machine Learning to Detect SQLi Attacks. 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC\_ASET). :513–517.
The increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. There have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. The proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. The model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. The detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.
Al Rahbani, Rani, Khalife, Jawad.  2022.  IoT DDoS Traffic Detection Using Adaptive Heuristics Assisted With Machine Learning. 2022 10th International Symposium on Digital Forensics and Security (ISDFS). :1—6.
DDoS is a major issue in network security and a threat to service providers that renders a service inaccessible for a period of time. The number of Internet of Things (IoT) devices has developed rapidly. Nevertheless, it is proven that security on these devices is frequently disregarded. Many detection methods exist and are mostly focused on Machine Learning. However, the best method has not been defined yet. The aim of this paper is to find the optimal volumetric DDoS attack detection method by first comparing different existing machine learning methods, and second, by building an adaptive lightweight heuristics model relying on few traffic attributes and simple DDoS detection rules. With this new simple model, our goal is to decrease the classification time. Finally, we compare machine learning methods with our adaptive new heuristics method which shows promising results both on the accuracy and performance levels.
Chen, Ye, Lai, Yingxu, Zhang, Zhaoyi, Li, Hanmei, Wang, Yuhang.  2022.  Malicious attack detection based on traffic-flow information fusion. 2022 IFIP Networking Conference (IFIP Networking). :1–9.
While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network.
Bouchiba, Nouha, Kaddouri, Azeddine.  2022.  Fault detection and localization based on Decision Tree and Support vector machine algorithms in electrical power transmission network. 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE). :1—6.
This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases.
Kumar, Marri Ranjith, K.Malathi, Prof..  2022.  An Innovative Method in Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing Decision Tree with Support Vector Machine. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). :1—6.
Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.
Kumar, Marri Ranjith, Malathi, K..  2022.  An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). :1—6.
Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.
Fargose, Rehan, Gaonkar, Samarth, Jadhav, Paras, Jadiya, Harshit, Lopes, Minal.  2022.  Browser Extension For A Safe Browsing Experience. 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). :1–6.
Due to the rise of the internet a business model known as online advertising has seen unprecedented success. However, it has also become a prime method through which criminals can scam people. Often times even legitimate websites contain advertisements that are linked to scam websites since they are not verified by the website’s owners. Scammers have become quite creative with their attacks, using various unorthodox and inconspicuous methods such as I-frames, Favicons, Proxy servers, Domains, etc. Many modern Anti-viruses are paid services and hence not a feasible option for most users in 3rd world countries. Often people don’t possess devices that have enough RAM to even run such software efficiently leaving them without any options. This project aims to create a Browser extension that will be able to distinguish between safe and unsafe websites by utilizing Machine Learning algorithms. This system is lightweight and free thus fulfilling the needs of most people looking for a cheap and reliable security solution and allowing people to surf the internet easily and safely. The system will scan all the intermittent URL clicks as well, not just the main website thus providing an even greater degree of security.
Fakhartousi, Amin, Meacham, Sofia, Phalp, Keith.  2022.  Autonomic Dominant Resource Fairness (A-DRF) in Cloud Computing. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). :1626—1631.
In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).
Mode, Gautam Raj, Calyam, Prasad, Hoque, Khaza Anuarul.  2020.  Impact of False Data Injection Attacks on Deep Learning Enabled Predictive Analytics. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1–7.
Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the- art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and resiliency to FDIA. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.
Liu, Wenyuan, Wang, Jian.  2021.  Research on image steganography information detection based on support vector machine. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). :631—635.
With the rapid development of the internet of things and cloud computing, users can instantly transmit a large amount of data to various fields, with the development of communication technology providing convenience for people's life, information security is becoming more and more important. Therefore, it is of great significance to study the technology of image hiding information detection. This paper mainly uses the support vector machine learning algorithm to detect the hidden information of the image, based on a standard image library, randomly selecting images for embedding secret information. According to the bit-plane correlation and the gradient energy change of a single bit-plane after encryption of an image LSB matching algorithm, gradient energy change is selected as characteristic change, and the gradient energy change is innovatively applied to a support vector machine classifier algorithm, and has very good detection effect and good stability on the dense image with the embedding rate of more than 40 percent.
Ding, Xiong, Liu, Baoxu, Jiang, Zhengwei, Wang, Qiuyun, Xin, Liling.  2021.  Spear Phishing Emails Detection Based on Machine Learning. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :354—359.
Spear phishing emails target to specific individual or organization, they are more elaborated, targeted, and harmful than phishing emails. The attackers usually harvest information about the recipient in any available ways, then create a carefully camouflaged email and lure the recipient to perform dangerous actions. In this paper we present a new effective approach to detect spear phishing emails based on machine learning. Firstly we extracted 21 Stylometric features from email, 3 forwarding features from Email Forwarding Relationship Graph Database(EFRGD), and 3 reputation features from two third-party threat intelligence platforms, Virus Total(VT) and Phish Tank(PT). Then we made an improvement on Synthetic Minority Oversampling Technique(SMOTE) algorithm named KM-SMOTE to reduce the impact of unbalanced data. Finally we applied 4 machine learning algorithms to distinguish spear phishing emails from non-spear phishing emails. Our dataset consists of 417 spear phishing emails and 13916 non-spear phishing emails. We were able to achieve a maximum recall of 95.56%, precision of 98.85% and 97.16% of F1-score with the help of forwarding features, reputation features and KM-SMOTE algorithm.
BOUIJIJ, Habiba, BERQIA, Amine.  2021.  Machine Learning Algorithms Evaluation for Phishing URLs Classification. 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). :01—05.
Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that Machine Learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Neural Network (NN), Ex-tra\_Tree (ET), Ada\_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features.
Alqurashi, Saja, Shirazi, Hossein, Ray, Indrakshi.  2021.  On the Performance of Isolation Forest and Multi Layer Perceptron for Anomaly Detection in Industrial Control Systems Networks. 2021 8th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :1–6.
With an increasing number of adversarial attacks against Industrial Control Systems (ICS) networks, enhancing the security of such systems is invaluable. Although attack prevention strategies are often in place, protecting against all attacks, especially zero-day attacks, is becoming impossible. Intrusion Detection Systems (IDS) are needed to detect such attacks promptly. Machine learning-based detection systems, especially deep learning algorithms, have shown promising results and outperformed other approaches. In this paper, we study the efficacy of a deep learning approach, namely, Multi Layer Perceptron (MLP), in detecting abnormal behaviors in ICS network traffic. We focus on very common reconnaissance attacks in ICS networks. In such attacks, the adversary focuses on gathering information about the targeted network. To evaluate our approach, we compare MLP with isolation Forest (i Forest), a statistical machine learning approach. Our proposed deep learning approach achieves an accuracy of more than 99% while i Forest achieves only 75%. This helps to reinforce the promise of using deep learning techniques for anomaly detection.
Baptiste, Millot, Julien, Francq, Franck, Sicard.  2021.  Systematic and Efficient Anomaly Detection Framework using Machine Learning on Public ICS Datasets. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :292–297.
Industrial Control Systems (ICSs) are used in several domains such as Transportation, Manufacturing, Defense and Power Generation and Distribution. ICSs deal with complex physical systems in order to achieve an industrial purpose with operational safety. Security has not been taken into account by design in these systems that makes them vulnerable to cyberattacks.In this paper, we rely on existing public ICS datasets as well as on the existing literature of Machine Learning (ML) applications for anomaly detection in ICSs in order to improve detection scores. To perform this purpose, we propose a systematic framework, relying on established ML algorithms and suitable data preprocessing methods, which allows us to quickly get efficient, and surprisingly, better results than the literature. Finally, some recommendations for future public ICS dataset generations end this paper, which would be fruitful for improving future attack detection models and then protect new ICSs designed in the next future.
Afzal-Houshmand, Sam, Homayoun, Sajad, Giannetsos, Thanassis.  2021.  A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems. 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). :258—264.
The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safety-critical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.
Abuah, Chike, Silence, Alex, Darais, David, Near, Joseph P..  2021.  DDUO: General-Purpose Dynamic Analysis for Differential Privacy. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1—15.
Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming languages and libraries. However, many existing programming languages designed for compositional verification of differential privacy impose significant burden on the programmer (in the form of complex type annotations). Supplementary library support for privacy analysis built on top of existing general-purpose languages has been more usable, but incapable of pervasive end-to-end enforcement of sensitivity analysis and privacy composition. We introduce DDuo, a dynamic analysis for enforcing differential privacy. DDuo is usable by non-experts: its analysis is automatic and it requires no additional type annotations. DDuo can be implemented as a library for existing programming languages; we present a reference implementation in Python which features moderate runtime overheads on realistic workloads. We include support for several data types, distance metrics and operations which are commonly used in modern machine learning programs. We also provide initial support for tracking the sensitivity of data transformations in popular Python libraries for data analysis. We formalize the novel core of the DDuo system and prove it sound for sensitivity analysis via a logical relation for metric preservation. We also illustrate DDuo's usability and flexibility through various case studies which implement state-of-the-art machine learning algorithms.
Cardaioli, Matteo, Conti, Mauro, Sorbo, Andrea Di, Fabrizio, Enrico, Laudanna, Sonia, Visaggio, Corrado A..  2021.  It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency. 2021 International Conference on Computer Communications and Networks (ICCCN). :1—9.
Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.
Zhao, Yue, Shen, Yang, Qi, Yuanbo.  2021.  A Security Analysis of Chinese Robot Supply Chain Based on Open-Source Intelligence. 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). :219—222.

This paper argues that the security management of the robot supply chain would preferably focus on Sino-US relations and technical bottlenecks based on a comprehensive security analysis through open-source intelligence and data mining of associated discourses. Through the lens of the newsboy model and game theory, this study reconstructs the risk appraisal model of the robot supply chain and rebalances the process of the Sino-US competition game, leading to the prediction of China's strategic movements under the supply risks. Ultimately, this paper offers a threefold suggestion: increasing the overall revenue through cost control and scaled expansion, resilience enhancement and risk prevention, and outreach of a third party's cooperation for confrontation capabilities reinforcement.

Kang, Dong Mug, Yoon, Sang Hun, Shin, Dae Kyo, Yoon, Young, Kim, Hyeon Min, Jang, Soo Hyun.  2021.  A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :291–294.
The CAN (Controller Area Network) bus, which transmits and receives ECU control information in vehicle, has a critical risk of external intrusion because there is no standardized security system. Recently, the need for IDS (Intrusion Detection System) to detect external intrusion of CAN bus is increasing, and high accuracy and real-time processing for intrusion detection are required. In this paper, we propose Hybrid MR (Machine learning and Ruleset) -IDS based on machine learning and ruleset to improve IDS performance. For high accuracy and detection rate, feature engineering was conducted based on the characteristics of the CAN bus, and the generated features were used in detection step. The proposed Hybrid MR-IDS can cope to various attack patterns that have not been learned in previous, as well as the learned attack patterns by using both advantages of rule set and machine learning. In addition, by collecting CAN data from an actual vehicle in driving and stop state, five attack scenarios including physical effects during all driving cycle are generated. Finally, the Hybrid MR-IDS proposed in this paper shows an average of 99% performance based on F1-score.
Knesek, Kolten, Wlazlo, Patrick, Huang, Hao, Sahu, Abhijeet, Goulart, Ana, Davis, Kate.  2021.  Detecting Attacks on Synchrophasor Protocol Using Machine Learning Algorithms. 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :102—107.
Phasor measurement units (PMUs) are used in power grids across North America to measure the amplitude, phase, and frequency of an alternating voltage or current. PMU's use the IEEE C37.118 protocol to send telemetry to phasor data collectors (PDC) and human machine interface (HMI) workstations in a control center. However, the C37.118 protocol utilizes the internet protocol stack without any authentication mechanism. This means that the protocol is vulnerable to false data injection (FDI) and false command injection (FCI). In order to study different scenarios in which C37.118 protocol's integrity and confidentiality can be compromised, we created a testbed that emulates a C37.118 communication network. In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this platform, we generated C37.118 FCI and FDI datasets which are processed by multi-label machine learning classifier algorithms, such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Naive Bayes (NB), to find out how effective machine learning can be at detecting such attacks. Our results show that the DT classifier had the best precision and recall rate.
Kozhemyak, Olesya A., Stukach, Oleg V..  2021.  Reducing the Root-Mean-Square Error at Signal Restoration using Discrete and Random Changes in the Sampling Rate for the Compressed Sensing Problem. 2021 International Siberian Conference on Control and Communications (SIBCON). :1—3.
The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.