Biblio

Securing communication between any two wireless devices or users is challenging without compromising sensitive/personal data. To address this problem, we have developed an artificial intelligence (AI) algorithm to secure communication on virtualized wireless networks. To detect cyberattacks in a virtualized environment is challenging compared to traditional wireless networks setting. However, we successfully investigate an efficient cyberattack detection algorithm using an AI algorithm in a Bayesian learning model for detecting cyberattacks on the fly. We have studied the results of Random Forest and deep neural network (DNN) models to detect the cyberattacks on a virtualized wireless network, having considered the required transmission power as a threshold value to classify suspicious activities in our model. We present both formal mathematical analysis and numerical results to support our claims. The numerical results show our accuracy in detecting cyberattacks in the proposed Bayesian model is better than Random Forest and DNN models. We have also compared both models in terms of detection errors. The performance comparison results show our proposed approach outperforms existing approaches in detection accuracy, precision, and recall.

From the beginning of technology and Wi-Fi based systems wireless networks had a prominent threat upon data security. Without security measures many organizations contribute on these flaws of security to make it better. There are many vulnerabilities of security models which are discussed in this article such as hacking through Wi-Fi security by Aircrack-ng, previous security model vulnerabilities and also the performance of Aircrack-ng attack on Wi-Fi modem or routers. In order to crack WPA/WPA2, kali Linux operating system will be needed along with Aircrack-ng packages installed on any compatible PC. Some of the new standard WPA3 such like downgrade problem on which the system will let the device to downgrade from WPA3 to WPA2 in order to connect with incompatible devise. Further, it makes a way for hackers to obtain Wi-Fi passwords even from new model defined such as WPA3 by using old techniques. The new model introduced Wi-Fi security protocol WPA3 is also no longer a secure model it can be penetrated. Researchers have discovered some new vulnerability enables hackers to get out the Wi-Fi passwords.

Ad Hoc Network is wireless networks that get more attention from past to present. Mobile ad hoc network (MANET) is one of the types of ad hoc networks, it deployed rapidly because it infrastructure-less. A node in a mobile ad hoc network communicates through wireless links without wired channels. When source nodes want to communicate with the destination outside its transmission range it uses multi-hop mechanisms. The intermediate node forwards the data packet to the next node until the data packet reaches its destination. Due wireless links and lack of centralized administration device, mobile ad hoc network is more vulnerable for security attacks. The rushing attack is one of the most dangerous attacks in the on-demand routing protocol of mobile ad hoc networks. Rushing attack highly transmits route request with higher transmission power than the genuine nodes and become participate between source and destination nodes, after that, it delays or drop actual data pass through it. In this study, the researcher incorporates rushing attack in one of the most commonly used mobile ad hoc network routing protocols namely Ad hoc on-demand multipath distance vector and provides a rushing attack prevention method based on the time threshold value and random route selection. Based on the time RREQ arrives a node takes a decision, if the RREQ packet arrives before threshold value, the RREQ packet consider as came from an attacker and discarded else RREQ packet received then randomly select RREQ to forward. In this study performance metrics like packet delivery ratio, end-to-end delay and throughput have been evaluated using Network simulation (NS-2.35). As a result of simulation shows newly proposed prevention mechanism improves network performance in all cases than the network under attacker. For example, the average packet delivery ratio enhanced from 54.37% to 97.69%, throughput increased from 20.84bps to 33.06bpsand the average delay decreased from 1147.22ms to 908.04ms. It is concluded that the new proposed techniques show improvement in all evaluated performance metrics.

Vehicular Ad hoc Networks commonly abbreviated as VANETs, are an important component of MANET. VANET refers to the group of vehicles that are interlinked to one another through wireless network. Along with technology, comes the threats. Like other wireless networks, VANETs also are vulnerable to various security threats. Security in VANETs is a major issue that attracted many researchers and academicians. One small security breach can cause a big damage in case of VANETs as in this case human lives are involved. Intrusion Detection Systems (IDS) are employed in VANETs in order to detect and identify any malicious activity in the network. The IDS works by analysing the network and detecting any intrusions tried or made in the network so that proper steps could be taken timely to prevent damage from such activities. This paper reviews Intrusion Detection systems, classification of IDS based on various factors and then the architecture of IDS. We then reviewed some of the recent and important intrusion detection research works and then compared them with one another.

In the recent years, mobile ad hoc wireless networks (MANETs) have experienced a tremendous rise in popularity and usage due to their flexibility and ability to provide connectivity from anywhere at any time. In general, MANETs provide mobile communication to participating nodes in situation where nodes do not need access to an existing network infrastructure. MANETs have a network topology that changes over time due to lack of infrastructure and mobility of nodes. Detection of a malicious node in MANETs is hard to achieve due to the dynamic nature of the relationships between moving node and the nature of the wireless channel. Most traditional Intrusion Detection System (IDS) are designed to operate in a centralized manner; and do not operate properly in MANET because data in MANETs is distributed in different network devices. In this paper, we present an Hierarchical Cooperative Intrusion Detection Method (HCIDM) to secure packets routing in MANETs. HCIDM is a distributed intrusion detection mechanism that uses collaboration between nodes to detect active attacks against the routing table of a mobile ad hoc network. HCIDM reduces the effectiveness of the attack by informing other nodes about the existence of a malicious node to keep the performance of the network within an acceptable level. The novelty of the mechanism lies in the way the responsibility to protect the networks is distributed among nodes, the trust level is computed and the information about the presence of a malicious is communicated to potential victim. HCIDM is coded using the Network Simulator (NS-2) in an ad hoc on demand distance vector enable MANET during a black hole attack. It is found that the HCIDM works efficiently in comparison with an existing Collaborative Clustering Intrusion Detection Mechanism (CCIDM), in terms of delivery ratio, delay and throughput.

Security is a major thing to focus on during this modern era as it is very important to secure your surroundings for the well being of oneself and his family, But there are many drawbacks of using conventional security surveillance cameras as they have to be set in a particular angle for good visual and they do not cover a large area, conventional security cameras can only be used from a particular device and cannot alert the user during an unforeseen circumstance. Hence we require a much more efficient device for better security a web controlled surveillance robot is much more practical device to be used compared to conventional security surveillance, this system needs a single camera to perform its operation and the user can monitor a wide range of area, any device with a wireless connection to the internet can be used to operate this device. This robot can move to any location within the range of the network and can be accessed globally from anywhere and as it uses only one camera to secure a large area it is also cost-efficient. At the core of the system lies Raspberry-pi which is responsible for all the operation of the system and the size of the device can be engineered according to the area it is to be used.

Convergence of operation technology (OT) and information technology (IT) in industrial automation is currently being adopted as an accelerating trend. The Industrial Internet of Things (IIoT) consists of heterogeneous sensing, computing and actuation nodes that are meshed through a layer of communication protocols, and represents a key enabler for this convergence. Experimental test beds are required to validate complex system designs in terms of scalability, latency, real-time operation and security. We use the open source Coaty - distributed industrial systems framework to present a smart industry application integrating field devices and controllers over the OPCUA and MQTT protocols. The experimental evaluation, using both proprietary automation components and open software modules, serves as a reference tool for building robust systems and provides practical insights for interoperability.

The Internet of Things technology is one of the important directions of Smart Grid research, involving many wireless sensors and communication facilities, and has high requirements for security. The physical layer security technology can effectively solve the security problems under wireless communication. As the most common application scenario of wireless communication is multi-node wireless network communication, group secret key (GSK) based on physical layer security and information theory security is gradually attracting investigator’s interest. In this paper, a novel physical layer GSK generation scheme based on code-domain exchange of channel information in mesh network is proposed. Instead of traditional side-information exchange in symbol-domain, error-correcting code is applied to finish information exchange and reconciliation simultaneously in code-domain. Each node processes the known channel bit sequence and then encodes it to generate a check sequence. After broadcasting the check bit sequence to other nodes, each node decodes the received check bit sequences to obtained the unknown channel information. The simulation results show that the scheme can effectively reduce the times of information exchanges while keeping a good performance including low bit error rate and low block error rate.

The popularity of P2P (Peer-To-Peer) systems is increased tremendously due to massive increase in the Internet based applications. Initially, P2P systems were mainly designed for wired networks but today people are using more wireless networks and therefore these systems are gaining popularity. There are many wireless networks available today and WMNs (Wireless Mess Networks) are gaining popularity due to hybrid structure. People are using structured P2P systems-based applications within perimeter of a WMN. Structured P2P WMNs will assist the community to fetch the relevant information to accomplish their activities. There are inherent challenges in the structured P2P network and increased in wireless environment like WMNs. Structured P2P systems suffer from many challenges like lack of content availability, malicious content distribution, poor search scalability, free riding behaviour, white washing, lack of a robust trust model etc. Whereas, WMNs have limitations like mobility management, bandwidth constraint, limited battery power of user's devices, security, maintenance etc. in remote/ forward areas. We exploit the better possibility of content availability and search scalability in this paper. We propose replication schemes based on the popularity of content for structured P2P system applications in community based WMNs. The analysis of the performance shows that proposed scheme performs better than the existing replication scheme in different conditions.

Lattice-based Attribute-based encryption is a well-known cryptographic mechanism that can resist quantum attacks and has the ability of fine-grained access control, and it has a wide range of application scenarios in current Internet of Thing (IoT) era. However, lack of efficiency and existing the problem of large ciphertext expansion rate are the main disadvantages impede the applications of this mechanism. Thus, we propose an efficient and practical ciphertext policy attribute-based encryption (CP-ABE) scheme from lattices in the paper. In this scheme, to make the secret key reusable, we adjust access tree and propose a basic access tree structure, which can be converted from disjunctive normal form, and combine it with a light post-quantum scheme of Kyber. In addition, the compression method and plaintext expansion method are introduced to optimize the scheme. Our CP-ABE scheme is secure against chosen plaintext attack under the hardness of module learning with errors problem. We implement our scheme and compare it with three recent related schemes in terms of security, function and communication cost. Experiments and comparisons show that our CP-ABE scheme has advantages in high encryption efficiency, small matrix dimension, small key sizes, and low ciphertext expansion rate, which has some merit in practice.

Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.

Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.

Mobile Ad Hoc Network is a infrastructure less wireless network where the mobile nodes leaves and joins the mobile network very frequently. The routing of the packets from source node to destination node, the routing protocol is used. On Demand Distance Vector Routing protocol is very common and implemented with Mobile Ad Hoc Network nodes to handle the operations of packet routing from by any node as a source node to destination node. In this paper prevention of black hole attack by modifying the On Demand Distance Vector routing protocol. The sequence number of 32 bit is initiated with the Route Reply and route sequence packet broadcast to determine the request reply from black hole node under the Mobile Ad Hoc Network. The sequence number and On demand Distance Vector Routing protocol are integrated with a mechanism to find the Request Reply of message containing routing information from source to destination node in Mobile Ad Hoc Network.

The use of wireless connectivity has increased exponentially in recent years. Fifth generation (5G) communications will soon be deployed worldwide. Six-generation (6G) communications vision and planning have begun, and the use of 6G communications is expected to begin in the 2030s. The 6G system has higher capacity, higher data rates, lower latency, higher security and better quality of service (QoS) compared to the 5G system. This paper presents a brief overview on the vision and requirements of 6G wireless communications and networks. Finally, some of the challenges in launching the 6G are also explained.

In order to solve the defense problem of wireless network security threats in new energy stations, a new wireless network security risk assessment model which proposes a wireless access security evaluation method for power monitoring system based on fuzzy theory, was established based on the study of security risk assessment methods in this paper. The security evaluation method first divides the security evaluation factor set, then determines the security evaluation weight coefficient, then calculates the network security level membership matrix, and finally combines specific examples to analyze the resulting data. this paper provided new ideas and methods for the wireless access security evaluation of new energy stations.

Wireless sensors are often deployed in environments where it is difficult for them to discern friend from enemy. An example case is a military tactical scenario, where sensors are deployed to map the location of an item but where some of the nodes have been compromised or where there are other malicious nodes present. In this scenario, sharing data with other network nodes may present a critical security risk to the sensor nodes. Blockchain technology, with its ability to house a secure distributed ledger, offers a possible solution. However, blockchain applications for Wireless Sensor Networks suffer from poor latency in block propagation which in turn decreases throughput and network scalability. Several researchers have proposed solutions for improved network throughput. In this work, a comparison of these existing works is performed leading to a taxonomy of existing algorithms. Characteristics consistently found in algorithms reporting improved throughput are presented and, later, these characteristics are used in the development of a new algorithm for improving throughput. The proposed algorithm utilizes a proof-of- authority consensus algorithm with a node trust-based scheme. The proposed algorithm shows strong results over the base case algorithm and was evaluated with blockchain network simulations of up to 20000 nodes.

The solution of using existing WiFi devices for measurement and maintenance, and establishing a WiFi fingerprint database for precise localization has become a popular method for indoor localization. The traditional WiFi fingerprint privacy protection scheme increases the calculation amount of the client, but cannot completely protect the security of the client and the fingerprint database. In this paper, we make use of WiFi devices to present a Practical Privacy Protection Scheme In WiFi Fingerprint-based Localization PPWFL. In PPWFL, the localization server establishes a pre-partition in the fingerprint database through the E-M clustering algorithm, we divide the entire fingerprint database into several partitions. The server uses WiFi fingerprint entries with partitions as training data and trains a machine learning model. This model can accurately predict the client's partition based on fingerprint entries. The client uses the trained machine learning model to obtain its partition location accurately, picks up WiFi fingerprint entries in its partition, and calculates its geographic location with the localization server through secure multi-party computing. Compared with the traditional solution, our solution only uses the WiFi fingerprint entries in the client's partition rather than the entire fingerprint database. PPWFL can reduce not only unnecessary calculations but also avoid accidental errors (Unexpected errors in fingerprint similarity between non-adjacent locations due to multipath effects of electromagnetic waves during the propagation of complex indoor environments) in fingerprint distance calculation. In particular, due to the use of Secure Multi-Party Computation, most of the calculations are performed in the local offline phase, the client only exchanges data with the localization server during the distance calculation phase. No additional equipment is needed; our solution uses only existing WiFi devices in the building to achieve fast localization based on privacy protection. We prove that PPWFL is secure under the honest but curious attacker. Experiments show that PPWFL achieves efficiency and accuracy than the traditional WiFi fingerprint localization scheme.

The position information leakage of under-water sensor networks has been widely concerned. However, the underwater environment has unique characteristics compared with the terrestrial environment, for example, the asynchronous clock, stratification compensation. Therefore, the privacy preserving localization algorithm for terrestrial is not suitable. At present, the proposed privacy preserving localization algorithm is at the cost of reducing the localization accuracy and increasing the complexity of the algorithm. In this paper, a privacy preserving localization algorithm for underwater sensor networks with ray compensation is proposed. Besides, the localization algorithm we designed hides the position information of anchor nodes, and eliminates the influence of asynchronous clock. More importantly, the positioning accuracy is improved. Finally, the simulation results show that the location algorithm with privacy preserving and without privacy preserving have the same location accuracy. In addition, the algorithm proposed in this paper greatly improves the positioning accuracy compared with the existing work.

Wireless sensor networks (WSNs) have been widely used in various applications for continuous event monitoring and detection. Dual to lack of a protected physical boundary, WSNs are vulnerable to trace-back attacks. The existing secure routing protocols are designed to protect source location privacy by increasing uncertainty of routing direction against statistic analysis on traffic flow. Nevertheless, the security has not been quantitatively measured and shown the direction of secure routing design. In this paper, we propose a theoretical security measurement scheme to define and analyze the quantitative amount of the information leakage from each eavesdropped message. Through the theoretical analysis, we identify vulnerabilities of existing routing algorithms and quantitatively compute the direction information leakage based on various routing strategy. The theoretical analysis results also indicate the direction for maximization of source location privacy.

Source location privacy (SLP) protection ensures security of assets in monitoring wireless sensor networks (WSNs). Also, low end-to-end delay (EED) and high packet delivery ratio (PDR) guarantee high packet delivery reliability. Therefore, it is important to ensure high levels of SLP protection, low EED, and high PDR in mission-critical monitoring applications. Thus, this study proposes a new angle-based agent node routing protocol (APr) which is capable of achieving high levels of SLP protection, low EED, and high PDR. The proposed APr protocol employs multiple routing strategies to enable a dynamic agent node selection process and creation of obfuscating routing paths. Analysis results reveal that the APr protocol achieves high packet delivery reliability to outperform existing intermediate node-based protocols such as the AdrR and tree-based protocols such as the TbR. Furthermore, the APr protocol achieves significantly high levels of SLP protection to outperform the AdrR protocol.

Fake source packet routing protocols can ensure Source Location Privacy (SLP) protection. However, the protocols have demonstrated some performance limitations including high energy consumption, low packet delivery ratio (PDR), and long end-to-end delay (EED). In this study, a 2-level phantom routing protocol is proposed to address some limitations of an existing fake source packet routing protocol. The proposed protocol supplants the fake source packets with a random second level phantom node to alleviate the limitations. Analysis results confirm that the proposed protocol is capable of achieving strong SLP protection with minimized communication overhead. By removing the fake packet traffic in the network, the protocol incurs minimized energy consumption, maximized PDR, and minimized EED.

Future wearable devices are expected to increasingly exchange their positioning information with various Location-Based Services (LBSs). Wearable applications can include activity-based health and fitness recommendations, location-based social networking, location-based gamification, among many others. With the growing opportunities for LBSs, it is expected that location privacy concerns will also increase significantly. Particularly, in opportunistic wireless networks based on device-to-device (D2D) connectivity, a user can request a higher level of control over own location privacy, which may result in more flexible permissions granted to wearable devices. This translates into the ability to perform location obfuscation to the desired degree when interacting with other wearables or service providers across the network. In this paper, we argue that specific errors in the disclosed location information feature two components: a measurement error inherent to the localization algorithm used by a wearable device and an intentional (or obfuscation) error that may be based on a trade-off between a particular LBS and a desired location privacy level. This work aims to study the trade-off between positioning accuracy and location information privacy in densely crowded scenarios by introducing two privacy-centric metrics.

Wireless Sensor Networks promises the wireless network tools that does not require any stable infrastructure. Routing is the most important effect of network operation for the extended data rates within the network. Route discovery and route search sent the required packets from the target node source. However, good data transmission is also a threatening task in networks that provide efficient and energy-efficient routing. Various research activities focus on the topology control, source location privacy optimization and effective routing improvement in WSN. Wherein the existing security solutions both routing protocols and source location solutions disrupt the self-organizing nature of wireless sensor networks. Therefore, large overhead signatures are displayed and digitally verified by the requesting node. The cloud-based and routing based schemes have provided efficient security but there are a lot of obstacles for source data and travel path information security in the WSN network. This study is dedicated to calculate the desired number of deployments for sensor nodes in a given area once the selected metric achieves a certain level of coverage, while maintaining wireless connectivity in the network. A trusted node authentication scheme in wireless sensor network reduces the communication between nodes in a secure data transmission network, where shared cryptography is established all adjacent to the sensor node. Route discovery and retransmission increases the network overhead and increases the average end-to-end delay of the network in the conventional systems. This results in higher time complexity, communication overhead and less security of constrained sensor network resources.

One of the most important information in Wireless Sensor Networks (WSNs) is the location of each sensor node. This kind of information is very attractive to attackers for real position exposure of nodes making the whole network vulnerable to different kinds of attacks. According to WSNs privacy, there are two types of threats affect the network: Contextual and Content privacy. In this work, we study contextual privacy, where an eavesdropper tries to find the location of the source or sink node. We propose a Multi-Sinks Cluster-Based Location Privacy Protection (MSCLP) scheme in WSNs that divides the WSN into clusters, each cluster managed by one cluster head (CH). Each CH sends random fake packets in a loop then sends the real packet to the neighbor's CHs using a dynamic routing method to confuse the attacker from tracing back the real packet to reveal the actual location of the source node, we are taking in our consideration two important metrics: the energy consumption, and the delay.

The usage of K-anonymity to preserve location privacy for wireless sensor network (WSN) monitoring systems, where sensor nodes operate together to notify a server with anonymous shared positions. That k-anonymous position is a coated region with at least k people. However, we identify an attack model to show that overlapping aggregate locations remain privacy-risk because the enemy can infer certain overlapping areas with persons under k who violate the privacy requirement for anonymity. Within this paper we suggest a mutual WSN privacy protocol (REAL). Actual needs sensor nodes to arrange their sensing areas separately into a variety of non-overlapping, extremely precise anonymous aggregate positions. We also developed a state transfer framework, a locking mechanism and a time delay mechanism to address the three main REAL challenges, namely self-organisation, shared assets and high precision. We equate REAL's output with current protocols through virtual experiments. The findings demonstrate that REAL preserves the privacy of sites, offers more precise question answers and decreases connectivity and device expense.