Dynamic ransomware detection for windows platform using machine learning classifiers

In this world of growing technological advancements, ransomware attacks are also on the rise. This threat often affects the finance of individuals, organizations, and financial sectors. In order to effectively detect and block these ransomware threats, the dynamic analysis strategy was proposed and carried out as the approach of this research. This paper aims to detect ransomware attacks with dynamic analysis and classify the attacks using various machine learning classifiers namely: Random Forest, Naïve Bayes, J48, Decision Table and Hoeffding Tree. The TON IoT Datasets from the University of New South Wales' (UNSW) were used to capture ransomware attack features on Windows 7. During the experiment, a testbed was configured with numerous virtual Windows 7 machines and a single attacker host to carry out the ransomware attack. A total of 77 classification features are selected based on the changes before and after the attack. Random Forest and J48 classifiers outperformed other classifiers with the highest accuracy results of 99.74%. The confusion matrix highlights that both Random Forest and J48 classifiers are able to accurately classify the ransomware attacks with the AUC value of 0.997 respectively. Our experimental result also suggests that dynamic analysis with machine learning classifier is an effective solution to detect ransomware with the accuracy percentage exceeds 98%

Malware detection system using cloud sandbox, machine learning

Today's internet continues to move forward, and with it comes the development of many applications. Therefore, these applications are also directly accessible via the Internet, which makes it one of the important things these days. In addition to this, these applications are sometimes developed as software that can be installed on users computers, laptops and even smartphones, which often attracts many attackers to compromise their computers with malware that is unintentionally installed in the computer. Gadgets and even computer systems. computer background. Many solutions have been employed to detect if these malware are installed. This paper aims to evaluate and study the effectiveness of machine learning methods in detecting and classifying malware being installed. This paper employs heuristics and machine learning classifiers to identify malware attacks detected in each website or software application. The study compares 3 classifiers to find the best machine learning classifier for detecting malware attacks. Prove that the cloud sandbox can achieve a high detection accuracy of 99.8% true positive rate value when identifying malware attacks? Use website features. Results show that Cloud Sandbox is an effective classifier for detecting malware attacks

Trojan Detection System Using Machine Learning Approach

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers in order to steal their data. As a result, Trojan horse detection is essential to identify the Trojan and limit Trojan attacks. In this study, we proposed a Trojan detection system that employed machine learning algorithms to detect Trojan horses within the system. A public dataset of Trojan horses that contain 2001 samples comprises of 1041 Trojan horses and 960 of benign is used to train the machine learning classification. In this paper, the Trojan detection system is trained using four types of classifiers which are Random Forest, J48, Decision Table and Naïve Bayes. WEKA is used for the execution of the classification process and performance analysis. The results indicated that the detection system trained with the Random Forest and Decision Table algorithms obtained the maximum level of accuracy

Deep learning based hybrid analysis of malware detection and classification: A recent review

Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems

Android mobile malware detection system using fuzzy AHP

This research proposed a multi-criteria decision making based (MCDM) mobile malware detection system using a risk-based fuzzy analytical hierarchy process (AHP) approach to evaluate the Android mobile application. This study focuses on static analysis, that uses permission-based features to assess the mobile malware detection system approach

Evaluation of boruta algorithm in DDoS detection

Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning's detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers

Geofence alerts application with GPS tracking for children monitoring (CTS)

Geofence Alerts Application with GPS Tracking for Children Monitoring (CTS) is a mobile application that helps parents to track the location of their child. It provides the parents with the route and real-time location of the children. Parents often face difficulties in getting hold of the whereabouts of their children when they are not in sight. This situation increases the insecurity of parents toward the safety of their children. The first objective of this paper is to obtain a latitude, longitude, and time information of a child’s location in real-time using GPS tracker. The second objective is to develop a smartphone application that capable to track the location of children in real-time. The third objective is to evaluate the functionality of the developed smartphone application in tracking children’s location. Features, advantages, and disadvantages of three commercialized application are compared to collect requirements for the CTS application. The requirements are then used to design and develop the interface of CTS application using Rapid Application Design (RAD) framework. Three main modules, which are the View Current Location module, View History Route module and Setup Geofence module are proposed for the application. Additionally, a GPS tracker based on Arduino Uno board is developed to provide the longitude and latitude of children’s current location. The functionality of the CTS application and the GPS tracker is then evaluated to determined bugs and its usability. It was discovered that CTS is in helping parents to track the location of their child in real-time, view the past route taken by the child, set up geofence area, and receive notification when their child enters or leave the geofence area within the scheduled time

Software defined internet of things in smart city: A review

The concept of smart cities has gained traction to enhance citizens’ quality of life amidst rapid urbanization. Integration of the Internet of Things (IoT) is a key component that allows for gathering real-time data to inform decision-making and drive innovation in urban planning and management. However, managing the amount of data generated and the IoT devices rapid growth poses a challenge that leads to network management, interoperability, security, and scalability issues in smart cities. To overcome such problems, integrating Software Define Networking (SDN) in IoT provides a flexible, scalable, and efficient network architecture that can better support the unique demands of IoT devices and applications. Motivated by the extensive research efforts in the Software Defined Internet of Things (SDIoT), this paper aims to review SDIoT implementation in smart cities. It first introduces the underlying technology along with various practical applications of SDIoT. The comprehension of SDIoT in smart cities focus on IoT application requirements, including interoperability, scalability, low latency requirement, handling of big data, security, and privacy, energy consumption, Quality of Service (QoS), and task offloading. The paper concludes by discussing the future research directions that need to be examined in greater depth

Cybercrime behaviour in the context of youth development

The positive growth of youth is vital in the development of the particular country. Thus, it is crucial to acknowledge the factors that contribute to positive youth development. Using a bibliometric analysis approach, this study investigated the research contribution in the fields of positive youth development by finding the information related to the annual growth of publication, research areas, top 20 impactful journals, and highly cited articles and authors. This study aimed to present an extensive knowledge map of Malaysian youth development by accumulating a 10-year dataset from the Scopus database. Articles published between 2011 until 2021 were analysed using bibliometric approach. Searching the keyword “youth”, 3354 articles were found. After refining with the keywords of “Malaysia Youth”, 528 articles were then collected. In addition, this paper collected data from the Institute for Youth Research Malaysia, a leading centre in researching youth and their development in Malaysia. It is proven that health (stress-free, worry-free), social relations (relationship with parents), and safety (security while using the internet) had a significant relationship in cybercrime behaviour among Malaysian youth. Furthermore, this study discussed the contributions and significance of youth studies in the latest research studies together with the future direction

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learnin