Automated Security Assessment Framework for Wearable BLE-enabled Health Monitoring Devices

The growth of IoT technology, increasing prevalence of embedded devices, and advancements in biomedical technology have led to the emergence of numerous wearable health monitoring devices (WHMDs) in clinical settings and in the community. The majority of these devices are Bluetooth Low Energy (BLE) enabled. Though the advantages offered by BLE-enabled WHMDs in tracking, diagnosing, and intervening with patients are substantial, the risk of cyberattacks on these devices is likely to increase with device complexity and new communication protocols. Furthermore, vendors face risk and financial tradeoffs between speed to market and ensuring device security in all situations. Previous research has explored the security and privacy of such devices by manually testing popular BLE-enabled WHMDs in the market and generally discussed categories of possible attacks, while mostly focused on IP devices. In this work, we propose a new semi-automated framework that can be used to identify and discover both known and unknown vulnerabilities in WHMDs. To demonstrate its implementation, we validate it with a number of commercially available BLE-enabled enabled wearable devices. Our results show that the devices are vulnerable to a number of attacks, including eavesdropping, data manipulation, and denial of service attacks. The proposed framework could therefore be used to evaluate potential devices before adoption into a secure network or, ideally, during the design and implementation of new devices.

A Survey of Intrusion Detection Using Deep Learning in Internet of Things

The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.

Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.

An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

Secure Physical Layer Network Coding versus Secure Network Coding

When a network has relay nodes, there is a risk that a part of the information is leaked to an untrusted relay. Secure network coding (secure NC) is known as a method to resolve this problem, which enables the secrecy of the message when the message is transmitted over a noiseless network and a part of the edges or a part of the intermediate (untrusted) nodes are eavesdropped. If the channels on the network are noisy, the error correction is applied to noisy channels before the application of secure NC on an upper layer. In contrast, secure physical layer network coding (secure PLNC) is a method to securely transmit a message by a combination of coding operation on nodes when the network is composed of set of noisy channels. Since secure NC is a protocol on an upper layer, secure PLNC can be considered as a cross-layer protocol. In this paper, we compare secure PLNC with a simple combination of secure NC and error correction over several typical network models studied in secure NC.

Model Pemantauan Keamanan Jaringan Melalui Aplikasi Telegram Dengan Snort

Network security is the main of the development of today's technology. The ease in accessing the internet also requires protection on users is required. The ease of accessing the internet by people can also cause the occurrence of cyber crime. Cyber crime can be done by all internet users, without exception, to earn a profit. Security monitoring system server through the app messenger Telegram can help administrators in the work because always be on standby in front of the server computer. Notice of Snort as IDS via Telegram also quicked and can be accepted anywhere. In taking action when the server something happened not too late. Target cyber crime also can attack anyone without exception. A system should be a strength, with the protection of a secure network that will be difficult to hack by hackers. The server is the main target in the conduct of cyber crime. The use of the server must maintain to secure all the data is not misused by persons who are not responsible. Each server is a system that should be an administrator as a guard on duty watching and taking action when something happens on the server. To monitor a server, an administrator should always standby in front of the server computer so as not to late take action when the server is about to happen something.

DESIGNING A SECURE NETWORK SOLUTION AGAINST DHCP ATTACKS

DHCP is an important aspect in small and large networks, since it facilitates the IP configuration of computers. However, DHCP is vulnerable to different attacks; therefore, the essential objective of this paper is to propose solutions against DHCP attacks. The paper gives an explanation about how DHCP works and understand the handshake mechanism and give a brief summary about DHCP attack, how they occur and how they affect the security of the enterprise since a leakage of sensitive Information could happen, which threatens the enterprise's security or a denial of service that immobilizes the network. Three effective countermeasures are looked up and tested against DHCP attacks, and each one successfully prevented the attack.

BUILDING SECURE INFORMATIONAL COMMUNICATION BETWEEN AUTOMATED CRITICAL INFRASTRUCTURE SYSTEMS THROUGH AN OPEN INTERNET NETWORK

The article provides the basic principles of building a secure network interaction of automated critical infrastructure systems (ACSIs) in a secure execution and solving the problem of safe communication of local networks through the open Internet network via Intranet technology.

Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier

Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results.