Due to recent indiscriminate attacks of ransomware, damage cases including encryption of users’ important files are constantly increasing. The existing vaccine systems are vulnerable to attacks of new pattern ransomware because they can only detect the ransomware of existing patterns. More effective technique is required to prevent modified ransomware. In this paper, an effective method is proposed to prevent the attacks of modified ransomware on Android platform. The proposed technique specifies and intensively monitors processes and specific file directories using statistical methods based on Processor usage, Memory usage, and I/O rates so that the process with abnormal behaviors can be detected. If the process running a suspicious ransomware is detected, the proposed system will stop the process and take steps to confirm the deletion of programs associated with the process from users. The information of suspected and exceptional processes confirmed by users is stored in a database. The proposed technique can detect ransomware even if you do not save its patterns. Its speed of detection is very fast because it can be implemented in Android source code instead of mobile application. In addition, it can effectively determine modified patterns of ransomware and provide protection with minimum damage.

/pdf/the-effective-ransomware-prevention-technique-using-process-4dsz2p105h.pdf

The Effective Ransomware Prevention Technique Using Process Monitoring on Android Platform

Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.

/pdf/man-in-the-middle-and-denial-of-service-attacks-detection-zsxx1grr.pdf

Man-in-the-middle and denial of service attacks detection using machine learning algorithms

Objective. The purpose of the study is to develop a methodology for assessing the security of an automated control system of critical information infrastructure from DDoS attacks. The purpose of the methodology development is to provide the decision–maker with a scientifically sound tool for assessing the risk of implementing a DDoS attack.Method. To achieve the stated goal of the study, simulation modeling based on the Monte Carlo method was used.Result. The expediency of using Monte Carlo simulation to assess the probability of server failure under DDoS attacks is confirmed. It was concluded that the server can be considered as a queuing system, however, the flow of incoming applications under DDoS attacks is not Poisson, so the use of analytical expressions to assess the probability of failure is considered incorrect. The simulation results allow the decision-maker to assess the probability of server failure and make organizational and technical decisions to increase the level of security. Analysis of the simulation results showed the effectiveness of improving server performance by increasing service channels.Conclusion. Thus, the developed methodology will be useful in conducting an information security audit of an organization to justify the amount of its insurance premium in the framework of cyber risk insurance. A possible direction for further research is to study the issue of computer network security, taking into account the features of a specific topology.

/pdf/assessment-methodology-for-security-of-an-automated-control-5p260eme.pdf

Assessment methodology for security of an automated control system of critical information infrastructure against DDoS attacks based on Monte Carlo simulation

Received Aug 19, 2022 Revised Oct 1, 2022 Accepted Oct 20, 2022 Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.

pdf/bulletin-of-electrical-engineering-and-informatics-2q8audhh.pdf

Bulletin of Electrical Engineering and Informatics

The analysis of data streams requires methods which can cope with a very high volume of data points. Under the requirement that algorithms must have constant computational complexity and a fixed amount of memory, we develop a framework for detecting changes in data streams when the distributional form of the stream variables is unknown. We consider the general problem of detecting a change in the location and/or scale parameter of a stream of random variables, and adapt several nonparametric hypothesis tests to create a streaming change detection algorithm. This algorithm uses a test statistic with a null distribution independent of the data. This allows a desired rate of false alarms to be maintained for any stream even when its distribution is unknown. Our method is based on hypothesis tests which involve ranking data points, and we propose a method for calculating these ranks online in a manner which respects the constraints of data stream analysis.

Nonparametric monitoring of data strems for changes in location and scale

Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.

Nonparametric Detection Methods against DDoS Attack

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Nonparametric Detection Methods against DDoS Attack