Fares Abdulhafidh Dael^1,*, Ömer Çağrı Yavuz², Uğur Yavuz¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903

Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an interesting area. The purpose of… More >

Aiping Xu^1,2, Xuan Zou³, Chao Wang^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645

Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into… More >

Mengxiao Wang^1,2, Jing Huang^1,2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569

Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm.… More >

Yea-Sul Kim¹, Ye-Eun Kim¹, Hwankuk Kim^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550

Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to… More >

Zhen Zhen¹, Jian Gao^1,2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 955-973, 2023, DOI:10.32604/csse.2023.039525

Abstract Telecommunication fraud has run rampant recently worldwide. However, previous studies depend highly on expert knowledge-based feature engineering to extract behavior information, which cannot adapt to the fast-changing modes of fraudulent subscribers. Therefore, we propose a new taxonomy that needs no hand-designed features but directly takes raw Call Detail Records (CDR) data as input for the classifier. Concretely, we proposed a fraud detection method using a convolutional neural network (CNN) by taking CDR data as images and applying computer vision techniques like image augmentation. Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed… More >

Daniyal Amankeldin¹, Lyailya Kurmangaziyeva², Ayman Mailybayeva², Natalya Glazyrina¹, Ainur Zhumadillayeva^1,*, Nurzhamal Karasheva³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1091-1108, 2023, DOI:10.32604/csse.2023.039503

Abstract This paper proposes a deep neural network (DNN) approach for detecting fake profiles in social networks. The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles. In addition, the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks, which has been developed using 16 features based on content-based and profile-based features. The results demonstrated that… More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 593-611, 2023, DOI:10.32604/csse.2023.039479

Abstract Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation results. This study introduces a… More >

Zhihan Liu¹, Xiang Li¹, Siqi Liu², Wei Li^1,*, Xiangxu Meng¹, Jing Jia³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 937-954, 2023, DOI:10.32604/csse.2023.039417

Abstract The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network… More >

Amr Munshi^1,2,*, Majid Alotaibi^1,2, Saud Alotaibi^2,3, Wesam Al-Sabban^2,3, Nasser Allheeib⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 891-902, 2023, DOI:10.32604/csse.2023.039374

Abstract The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden services provided by darknets, is… More >

Muhammad Yasir Khan¹, Muhammad Adnan^1,2, Jawaid Iqbal³, Noor ul Amin¹, Byeong-Hee Roh⁴, Jehad Ali^4,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1185-1208, 2023, DOI:10.32604/csse.2023.039352

Abstract An Information-Centric Network (ICN) provides a promising paradigm for the upcoming internet architecture, which will struggle with steady growth in data and changes in access models. Various ICN architectures have been designed, including Named Data Networking (NDN), which is designed around content delivery instead of hosts. As data is the central part of the network. Therefore, NDN was developed to get rid of the dependency on IP addresses and provide content effectively. Mobility is one of the major research dimensions for this upcoming internet architecture. Some research has been carried out to solve the mobility issues, but it still has… More >