Implementation of Port Knocking with Telegram Notifications to Protect Against Scanner Vulnerabilities

The opening of the service port on the Mikrotik router provides an opening for hackers to enter the Mikrotik service to access the router illegally. This research aimed to close certain ports that are gaps for hackers and uses port knocking and telegram bots. The Telegram bot was used as a message notification to managers in real-time to provide information that occurs when the vulnerability scanning process is carried out to find and map weaknesses in the network system. Searching for weaknesses also includes looking for open router service ports such as ports 22, 23, 80, and 8291. This research used the Network Development Life Cycle method, which started from analysis design and prototype simulation to implementation. The research results after testing were able to secure local network service ports against vulnerability scanners on routers using the port knocking method, and testing attack schemes carried out from each scheme could run well on the router’s local network and obtain notifications via telegram bots in real time to administrators. This research contributes to administrators’ ability to secure networks so irresponsible people do not easily infiltrate them

CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis

Intrusion Detection in Critical Infrastructures: A literature review

open access articlever the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review of the methods of network detection and cyber attacks that can take place in critical infrastructure. As it is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions

A new model for security analysis of network anomalies for IoT devices

In the era of IoT gaining traction, attacks on IoT-enabled devices are the order of the day that emanates the need for more protected IoT networks. IoT's key feature deals with massive amounts of data sensed by numerous heterogeneous IoT devices. Numerous machine learning techniques are used to collect data from different types of sensors on the objects and transform them into information relevant to the application. Furthermore, business and data analytics algorithms help in event prediction based on observed behavior and information. Routing information securely over the internet with limited resources in IoT applications is a key problem. The study proposes a model for detecting network anomalies in IoT devices to enhance the security of the devices. The study employed the IoT Botnet dataset, and K-fold cross-validation tests were used for validating the values of evaluation metrics. The average values of Accuracy, Precision, Recall, and F Score was 97.4

Design a secure IoT Architecture using Smart Wireless Networks

The Internet of Things (IOT) is a revolution in the technology world, and this field is continuously evolving. It has made life easier for people by providing consumers with more efficient and effective resources in faster and more convenient ways. The Internet of Things is one of the most exciting fields for the future by 2030. 90% of the planet will be connected and all devices in homes and businesses around us will be connected to the Internet making it more vulnerable to violations of privacy and protection. Due to the complexity of its environment, security and privacy are the most critical issues relevant to IoT. Without the reliable security of the devices, they will lose their importance and efficiency. Moreover, the security violation will outweigh any of its benefits. In this paper, an overview of various layered IoT architectures, a review of common security attacks from the perspective of the layer, and the best techniques against these attacks are provided. Moreover, an enhanced layered IoT architecture is proposed, which will be protected against several security attacks

IoT Security Evolution: Challenges and Countermeasures Review

Internet of Things (IoT) architecture, technologies, applications and security have been recently addressed by a number of researchers. Basically, IoT adds internet connectivity to a system of intelligent devices, machines, objects and/or people. Devices are allowed to automatically collect and transmit data over the Internet, which exposes them to serious attacks and threats. This paper provides an intensive review of IoT evolution with primary focusing on security issues together with the proposed countermeasures. Thus, it outlines the IoT security challenges as a future roadmap of research for new researchers in this domain

Mitigating Malicious Packets Attack via Vulnerability-aware Heterogeneous Network Devices Assignment

Due to high homogeneity of current network devices, a network is compromised if one node in the network is compromised by exploiting its vulnerability (e.g., malicious packets attack). Many existing works adopt heterogeneity philosophy to improve network survivability. For example, “diverse variants” are assigned to nodes in the network. However, these works assume that diverse variants do not have common vulnerabilities, which deem an invalid assumption in real networks. Therefore, existing diverse variants deployment schemes could not achieve optimal performance. This paper considers that some variants have common vulnerabilities, and proposes a novel solution called Vulnerability-aware Heterogeneous Network Devices Assignment (VHNDA). Firstly, we introduce a new metric named Expected Infected Ratio (EIR) to measure the impact of malicious packets’ attacks spread on the network. Secondly, we use EIR to model the vulnerability-aware diverse variants deployment problem as an integer-programming optimization problem with NP-hard complexity. Considering NP-hardness, we then design a heuristic algorithm named Simulated Annealing Vulnerability-aware Diverse Variants Deployment (SA-VDVD) to address the problem. Finally, we present a low complexity algorithm named Graph Segmentation-based Simulated Annealing Vulnerability-aware Diverse Variants Deployment (GSSA-VDVD) for large-scale networks named graph segmentation-based simulated annealing. The experimental results demonstrate that the proposed algorithms restrain effectively the spread of malicious packets attack with a reasonable computation cost when compared with baseline algorithms

Intrusion Detection in Critical Infrastructures: A Literature Review

Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions