Security Related Attacks Research Articles

IoMT: A COVID-19 Healthcare System driven by Federated Learning and Blockchain.

Internet of medical things (IoMT) has made it possible to collect applications and medical devices for improving healthcare information technology. Today, limitations in technology enable COVID-19 call centers to restrict the number of calls per day. To this end, the unprecedented virality of COVID-19 makes call centers to be likely overstressed. Thus, people who are tested for COVID-19 virus may not get adequate guidance to manage and minimize both its risk and transmission. In addition, lack of patients privacy has restricted the sharing of COVID-19 data among health institutions. To resolve the above mentioned limitations, this paper proposes privacy infrastructure based on federated learning and blockchain technology. The proposed infrastructure has the potentials to enhance public communication and deliver alternative methods to disseminate COVID-19 information. Also, the proposed infrastructure can effectively resolve the issue of large data silos and provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security related attacks.

Ethical hacking for IoT: Security issues, challenges, solutions and recommendations

In recent years, attacks against various Internet-of-Things systems, networks, servers, devices, and applications witnessed a sharp increase, especially with the presence of 35.82 billion IoT devices since 2021; a number that could reach up to 75.44 billion by 2025. As a result, security-related attacks against the IoT domain are expected to increase further and their impact risks to seriously affect the underlying IoT systems, networks, devices, and applications. The adoption of standard security (counter) measures is not always effective, especially with the presence of resource-constrained IoT devices. Hence, there is a need to conduct penetration testing at the level of IoT systems. However, the main issue is the fact that IoT consists of a large variety of IoT devices, firmware, hardware, software, application/web-servers, networks, and communication protocols. Therefore, to reduce the effect of these attacks on IoT systems, periodic penetration testing and ethical hacking simulations are highly recommended at different levels (end-devices, infrastructure, and users) for IoT, and can be considered as a suitable solution. Therefore, the focus of this paper is to explain, analyze and assess both technical and non-technical aspects of security vulnerabilities within IoT systems via ethical hacking methods and tools. This would offer practical security solutions that can be adopted based on the assessed risks. This process can be considered as a simulated attack(s) with the goal of identifying any exploitable vulnerability or/and a security gap in any IoT entity (end devices, gateway, or servers) or firmware.

An efficient node placement scheme to mitigate routing attacks in Internet of Battlefield Things

Internet of Battlefield Things (IoBT) can be considered as the network of sensors, wearable devices, which use cloud computing to create a cohesive fighting force, which retaliates according to the activities of the enemies with negligible human involvement. However, for its proper functioning we need to deploy various types of smart devices, which have issues, such as fast battery draining, security, link failures. It consists of a small number of powerful mobile nodes, called the cluster heads (CHs) and a large number of resource-constrained static smart devices (sensor nodes). CHs can be deployed to mitigate the security related attacks and link failure issues. In this paper, we propose a routing attack mitigation scheme for wireless sensor networks integrated into Internet of Battlefield Things. Furthermore, it has been observed that the performance of proposed scheme is better than the other exiting schemes.

EACS: expressible access control scheme for secure

ABSTRACT Recent technological advancements in health care delivery have paved the way for collaborative monitoring, diagnosis, and treatment of patients, which involve real-time monitoring of patients and remote storage of their health information in the cloud. However, controlling access to the stored health information in the cloud is not only difficult but also susceptible to different privacy- and security-related attacks. Besides, existing access control schemes for e-health do not support delegation of responsibilities, a common phenomenon in health care delivery, making complete adoption of e-health impracticable. In this paper, we propose a security scheme that provides fine-grained expressive access control on patients’ health information for secure data and responsibilities delegation among health officers in collaborative e-health systems. The scheme takes care of the key distribution and attribute or user revocation problems of CP-ABE and supports secure delegation of responsibilities. With this scheme, a health officer can treat patients and securely delegate responsibilities to other health officers within the team without jeopardizing the security and privacy of the patients. Security and performance analysis show that our proposed scheme is efficient, secure, and expressive.

HsIrisNet: Histogram Based Iris Recognition to Allay Replay and Template Attack Using Deep Learning Perspective

Iris biometric is a widely deployed tool for biometric based user authentication. Its success has paved path for several security related attacks on this biometric. In this paper, we propose a deep learning perspective to solve two of these attacks—replay attack and database attack simultaneously. The proposed architecture HsIrisNet, a convolutional neural network (CNN), is trained and tested on two publicly available databases Casia-Iris-Interval v4 DB and IIT Delhi DB. The proposed approach HsIrisNet uses histograms of the selective robust regions followed by image upsampling to authenticate the user. These robust regions have been found to be sufficient enough to authenticate the user, enabling non-deterministic approach. The use of these selective regions for authentication enables the system to mitigate replay attack. While, the CNN model does not require saving any biometric data to the database, which mitigates database attack from the system. The comparison of proposed approach with existing state-of-art techniques show better performance of the proposed approach.

Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network

From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition and many more. WSN node exposes itself to various security related attacks due to low battery power supply, low bandwidth support, data transmission over multi hop node, dependency on intermediate or other nodes, distributed in nature and self-organization. The WSN attacks observe in all layers of OSI model. Wireless sensor nodes has various issues because of that, it experiences number problem related to its functionalities and some malfunction due to attacks. It is require to build defence and network monitoring system for identifying attacks and prevent them. Intrusion detection system (IDS) plays an important role to detect threads inside the system and generate the alert related to the attack. In this work, supervised classification models for intrusion detection are built using such as Random Forest classifier, Support Vector Machine, Decision Tree Classifier, LGBM Classifier, Extra Tree Classifier, Gradient Boosting Classifier, Ada Boost Classifier, K Nearest Neighbour Classifier, MLP Classifier, Gaussian Naive Bayes Classifier and Logistic Regression Classifier. The NSLKDD, i.e. Modified version of the KDD99 Data Set on which we checks these algorithms. Experimental results how the highest accuracy relative to other classification systems in the support vector machine.

A Cluster-Based Mitigation Strategy Against Security Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) applications range across distinct application comprising of event detection at real-time. WSNs can be deployed for not only mobile nodes but also for static sensor nodes (SNs) for various applications which may include health care system, smart parking, environmental monitoring etc. Sensor nodes in WSN are constrained in terms of energy contents of each node and can be accessible by other nodes in a wireless medium are more likely to be susceptible to various categories of attacks. Wireless Network are more likely prone to various kinds of security attacks, one such type of attack caused by a malicious attacker, which can result to decay in the lifetime of the network and an adverse scenario can even lead to congestion in the entire network. This paper presents the overview of various attacks and their consequences on different layers and evaluates defense strategy used to mitigate the various categories of attacks on Wireless Sensor Networks. This study proposes a cluster-based approach for each node of a WSN where the nodes of network constrained by energy can organize and perform network duties as per the network performance for this one node performs the role of cluster head (CH) which is elected on the basis of the "Reputation" of a node which is an indicator of nodes individual behavior in the network and "Net_Credit_Score" which determines the cooperating behavior of sensor node in the cluster. Further, this study highlights few parameters which can be implemented to further enhance the defense strategy by taking into account the factors such as Cluster count, Stability factor of both the Cluster and Cluster Head and Intra-Cluster topology which can be crucial. This will result in formulating a road map for designing a secure and resistant reputation-based system for WSN to overcome the various security related attacks.

Identifying critical nodes’ group in complex networks

Recently, network vulnerability or security has attracted much attention in various networked systems, and especially in security related attacks or protections, there are a set of influential nodes that can remarkably break the network connectivity. In this work, we firstly present eight attack mechanisms including target attack, random failure, betweenness based attack, closeness based attack, PageRank based attack, k-shell based attack, greedy algorithm, and low-degree attack. Secondly, inspired by the dynamic node removal process, we propose to recalculate the metrics for every node removal strategy, and evaluate the network robustness against all these heuristic attack strategies with and without recalculations in scale-free networks, random networks, and many real network models. The simulations indicate that most of the attack strategies with recalculations appear to imperil the network structure security more. Furthermore, considering that key node set mining is very critical for network structure protections, we employ minimum number of key nodes (MNKN) metric to further discuss the network vulnerability against all the attack strategies with or without recalculations. The results show that the critical nodes’ group can be more efficiently found under the PageRank based attack with recalculations than under other attack disciplines with or without recalculations in most of the classic and real network models. This work investigates network structure vulnerability and security from a new perspective, and has potential applications into network structure protection or planning.

STUDY ON SECURITY AND QUALITY OF SERVICE IMPLEMENTATIONS IN P2P OVERLAY NETWORK FOR EFFICIENT CONTENT DISTRIBUTION

Peer-to-peer (P2P) is a distributed system without any centralized control or hierarchical organization nodes organize itself in a dynamic way. It allows different applications to achieve efficient and simple file sharing. Without any authentication adversary nodes can spoof the identity and spoil the integrity by falsifying the messages in the overlay. This enables malicious nodes to launch man-inthe-middle or denial-of-service attacks and many security related attacks. P2P systems require a sufficient amount of trust from their nodes which are all participates in the network for AAA (Authentication, Authorization and Availability). From the analysis it aims to provide the prediction of optimized path in the network for efficient file sharing and the identification of necessary security methods in the network to provide security to decentralized p2p network.

안전한 원격사용자 인증스킴에 대한 취약성 분석

In 2011, C.-T. Li et al. proposed a secure user authentication scheme, which is an improvement over Kim et al.’s scheme to resolve several security flaws such as off-line password guessing attack and masquerading attack. C.-T. Li et al. claimed that their scheme prevents smart card security related attacks. Moreover, it provides mutual authentication and session key establishment. However, we found that their scheme is vulnerable to password guessing attack through password change phase, smart card forgery attack and stolen verifier attack. Moreover, C.-T. Li et al.’s scheme is not secure against password guessing attack as they claimed. In this paper, we also point out that their scheme is not practical to use.