Abstract

A persistent, targeted cyber attack is called an advanced persistent threat (APT) attack. The attack is mainly launched to gain sensitive information, take over the system, and for financial gain, which creates nowadays more hurdles and challenges for the organization in preventing, detecting, and recovering from such attacks. Due to the nature of APT attacks, it is difficult to detect them quickly. Therefore machine learning techniques come into these research areas. This study uses deep and machine learning models such as random forest, decision tree, convolutional neural network, multilayer perceptron and so forth to categorize and effectively detect APT attacks by utilizing publicly accessible datasets. The datasets used in this study are CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15. This study proposes the hybrid ensemble machine learning model, a mixed approach of random forest and XGBoost classifiers. It has obtained the maximum prediction accuracy of 98.92%, 99.91%, 99.24%, and 97.11% for datasets CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15, with a false positive rate of 0.52%, 0.12%, 0.62%, and 5.29% respectively. These results are compared to other closely related recent studies in the literature. Our experiment's findings show that our model has performed significantly better for all datasets.

Abstract

The most significant and critical infrastructures, such as the electricity utilities, clean water facilities, nuclear plants and manufacturing industries are controlled and supervised by the industrial control systems. These systems undergo through a metamorphosis as a result of the Industry 4.0 revolution, which emphasises enhanced connectivity and flexibility with the Internet of Things (IoT) and cloud computing technologies. As the data is transferred across the Internet, Industry 4.0 communication can be easily attacked by launching different potential attacks. As a consequence, we attempt to propose a novel certificate-based access control and key establishment scheme for securing Industry 4.0 communication, called ACKS-IA. It offers access control and key establishment between smart industrial devices, as well as between a smart device and its associated cloud server. A formal security analysis of ACKS-IA through the broadly-accepted Burrows–Abadi– Needham (BAN) logic is provided. It confirms that ACKS-IA is secured and provides secure mutual authentication among the communication entities. The detailed informal security analysis and comparative study with the existing related schemes reveal that the proposed ACKS-IA is secured and efficient in terms of communication cost, computation cost, and security and functionality features including anonymity and untraceability as compared to other competing schemes. Finally, a real testbed implementation of ACKS-IA is provided to measure its effect on important performance attributes.

Abstract

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol’s security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols.

Abstract

In recent years there has been rampant growth in the field of ML, AI, Big Data, IoT, cyber security, and cloud computing. These technologies have also integrated into the field of smart healthcare. The Internet of Medical Things (IoMT)-driven e-health is one such domain, which utilizes these modern technologies to provide cost-effective and secure healthcare care services to the patients [1]. It contain sensors, devices, actuators, etc. All the raw data is collected from these devices, which is prepossessed and refined to give useful insights. The medical professional can analyze and visualize critical health parameters such as ‘‘blood pressure (BP), heart rate, temperature, etc Based on the current status of the patient, alerts can be generated to doctors or the nursing staff in case of any eventuality. The integration of IoMT with e-health can enhance live tracking, monitoring and health services of patients [2], [3]. This data is regularly shared by the integrated devices and stored over the cloud server, where the healthcare professional can get all the diagnostics of patients. With the benefits of the technology, there are some security challenges. It can be architectural design flaws, a software bug, or a hardware backdoor that can lead to the compromise of the security of the systems. The cyber threat actor can gain authorized access to the system and paralyze all the networks at once. Weak control access, authentication and authorization protocols in the network may compromise the systems to the cyber attackers [4]. Attacks such as ransomware, rootkit, malware, denial of service (DoS), etc., can cause harm to the devices and tamper the healthcare data. Attackers can remotely access the smart healthcare devices and can use them as the botnet devices. With remote access, the attackers can redirect the network somewhere else and can huge network flooding [5], [6]. There have been a lot of high-impact cyber attacks happening to the e-health infrastructure. For example, a patient with the smart pacemaker monitors heart rate, temperature, and blood flow rate that can be critical to a heart alignment patient. Imagine this device gets compromised by a threat actor, it can create panic even worse and can cause death to the patient [7]. For mitigation of the cyber-attacks on all the critical infrastructure (i.e., e-health), cyber security comes into action. With threat modeling and hunting, we can actively search for any threat in the network, its source, and the severity of the threats. Once threats are found in the network, all the mitigation techniques can be used to secure the network from any intrusion. Cyber security provides a strong mechanism (i.e., authentication and key agreement) with all the tools and techniques to safeguard the e-health system [5], [6], [8]. Therefore, proper registration, authentication, authorization and intrusion detection schemes can be used to mitigate the various cyber attacks, i.e., ‘‘replay attempts, man-in-themiddle (MiTM) attacks, replay attacks, illegal session key computation, stolen verifier, etc., [8]

Abstract

Internet of Things (IoT)-based smart factories offer the manufacturing sectors a great opportunity to embrace the fourth industrial revolution (Industry 4.0). The real-time monitoring of manufacturing operations in an Industry 4.0 needs to be ensured by the deployed technologies, like Artificial Intelligence (AI) and Big Data analytics. The overall purpose is to improve the outcomes of the production process. However, Industry 4.0 becomes vulnerable to different potential attacks as the communication takes place via public environments. In this article, an authentication and key agreement method has been suggested to secure the communication that can occur in a Fog-based Industry 4.0 environment. The security proposal provides secure mutual authentication along with key establishment between various smart industrial devices and fog servers, as well as between fog servers and cloud servers. The security analysis and comparative study reveal that the proposed method can mitigate various potential attacks, and it also offers important security and functionality attributes as compared to those for other competing schemes.

Abstract

Denial-of-service (DoS) attacks are a major concern in the field of Internet technology, and they pose a difficult security challenge. Among the different types of DoS attacks, distributed denial-of-service (DDoS) attacks are especially concerning because they can quickly drain the resources of a target, disrupting their computational and communication capabilities without warning. In response to the severity of this problem, various defense strategies have been developed to defend against DDoS attacks. In this paper, a survey on the various DDoS attacks detection schemes is presented. An overview of the DDoS attack methodology is given. The information on different types of DDoS detection methods in SDN environment is also provided. Then a comparative study of different DDoS attack detection schemes is given, which contains the comparisons of different schemes in terms of their advantages, limitations, accuracy values, and time complexity.

Abstract

The healthcare sector is a very crucial and important sector of any society, and with the evolution of the various deployed technologies, like the Internet of Things (IoT), machine learning and blockchain it has numerous advantages. However, in this section, the data is much more vulnerable than others, because the data is strictly private and confidential, and it requires a highly secured framework for the transmission of data between entities. In this article, we aim to design a blockchain-envisioned authentication and key management mechanism for the IoMT-based smart healthcare applications (in short, we call it SBAKM-HS). We compare the various attributes of the proposed SBAKM-HS and other existing schemes to demonstrate that SBAKM-HS outperforms other existing schemes. The conducted security analysis

Abstract

With the increasing use of Internet of Things (IoT)- enabled drones for various purposes, including photography, de- livery, and surveillance, concerns related to privacy and security have arisen. Drones have the potential to capture sensitive infor- mation, invade privacy, and cause security breaches. Therefore, the need for advanced technology for the automated detection of drones has become crucial. In this paper, we propose an ensemble-based IoT-enabled drones detection scheme (in short, EDDSBS). The presented model is part of a computer vision- based module and uses transfer learning for improved perfor- mance. Transfer learning allows the reuse of pre-trained models and their knowledge in a different but related domain, enabling better performance with less training data. To evaluate the performance of the proposed EDDSBS, we test it on benchmark datasets, including the Drone–vs–Bird Dataset and the UAVDT dataset. The proposed EDDSBS outperforms the existing schemes of drone detection (i.e., in terms of accuracy). The results of the presented scheme demonstrate the potential of deep learning- based technology for automated drone detection in critical areas, such as airports, military bases, and other high-security areas. Thus the paper introduces a comprehensive process methodology for drone detection that can be applied in real-world settings for a sustainable and secure environment, which is required for a safe community

Abstract

In recent days, the application of cloud computing has been gaining significant popularity among people. A considerable amount of data are being stored in the cloud server. However, data owners outsource their encrypted data to the cloud for various security reasons. Unfortunately, encrypted data cannot be searched, like plaintext data. So how to search encrypted data is an interesting problem in this era. Many public key encryption with keyword search (PEKS) schemes have been designed in the literature. However, most of them cannot prevent keyword-guessing attacks. In this paper, we develop a provably secure PEKS scheme in the random oracle model. This scheme may be used for secure email access from an email server containing a list of encrypted keywords. The proposed scheme can resist keyword-guessing attacks, and offer ciphertext and trapdoor indistinguishability properties. We use the data owner’s private key during encryption to prevent keyword-guessing attacks. Using the data owner’s public key in the verification phase ensures the resilience of keyword-guessing attacks. Finally, the proposed scheme has been tested on a real testbed, and the results show that it can be used in the cloud computing scenario to search for keywords on encrypted data

Abstract

User authentication is a promising security solution in which an external user hav- ing his/her mobile device can securely access the real-time information directly from the deployed smart devices in an Internet of Things-enabled intelligent precision agri- cultural environment. To achieve this goal, we present a new signature-based three factor user authentication scheme. The established session key between a user and the accessed smart device is then used to make secure communication among them to fetch the real-time data of the device. A detailed security analysis including the random-oracle based formal security, formal security verification using the broadly recognized automated validation of internet security protocols and applications tool and nonmathematical informal security analysis show the robustness of the proposed scheme against a number of potential attacks. In addition, testbed experiments are performed for measuring computational time of various cryptographic primitives that are used for comparative study among the proposed scheme and other related exist- ing competing schemes. The detailed comparative analysis shows that the proposed scheme has a better trade-off among its offered security and functionality features, and communication and computational overheads as compared with those for other competing schemes.