The traditional approach of providing network security has been to borrow tools from cryptography and authentication. However, we argue that the conventional view of security based on cryptography alone is not sufficient for the unique characteristics and novel misbehaviors encountered in sensor networks. Fundamental to this is the observation that cryptography cannot prevent malicious or non-malicious insertion of data from internal adversaries or faulty nodes. We believe that in general tools from different domains such as economics, statistics and data analysis will have to be combined with cryptography for the development of trustworthy sensor networks. Following this approach, we propose a reputation-based framework for sensor networks where nodes maintain reputation for other nodes and use it to evaluate their trustworthiness. The framework is modularized; we will analyze each building block in detail in this paper. We will show that this framework provides a scalable, diverse and a generalized approach for countering all types of misbehavior resulting from malicious and faulty nodes.

Reputation-Based Frameworks for High Integrity Sensor Networks

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- or nano-sensors capable of processing and sending information to the base station (BS). Sensors embedded in the bodies of individuals can enable vital information exchange over wireless communication. Network forming of these sensors envisages long-term medical care without restricting patients’ normal daily activities as part of diagnosing or caring for a patient with a chronic illness or monitoring the patient after surgery to manage emergencies. This paper reviews WBAN, its security challenges, body sensor network architecture and functions, and communication technologies. The work reported in this paper investigates a significant security-level challenge existing in WBAN. Lastly, it highlights various mechanisms for increasing security and decreasing energy consumption.

/pdf/wireless-body-area-network-wban-a-survey-on-architecture-1yrmzjqf.pdf

Wireless Body Area Network (WBAN): A Survey on Architecture, Technologies, Energy Consumption, and Security Challenges

Cyber-Physical Systems (CPSs) are new types of intelligent systems that integrate computing, control, and communication technologies, bridging the cyberspace and physical world. These systems enhance the capabilities of our critical infrastructure and are widely used in a variety of safety-critical systems. CPSs are susceptible to cyber attacks due to their vulnerabilities such that their security has become a critical issue. Therefore, it is important to classify and comprehensively investigate this issue. Most of the existing surveys on it are conducted from a single perspective. In this paper, we present a comprehensive view of the security of CPSs from three perspectives: the physical domain, the cyber domain, and the cyber-physical domain. In the physical domain, we review some attacks that directly damage the physical components of CPSs such as sensors and discuss corresponding defenses. We also review the attacks that CPSs in the cyber domain may face and study methods to detect and defend against them. In addition, we survey the intelligent attacks faced by CPSs and the corresponding defensive means. In the cyber-physical domain, we provide an overview of attacks that come from the cyber domain and eventually damage the physical parts, and discuss the corresponding detection and defense methods. Finally, we present the challenges and future research directions. Through this in-depth review, we attempt to summarize the current security threats to CPSs and the state-of-the-art security means to provide researchers with a comprehensive overview. 

A Survey on Cyber-Physical Systems Security

A smart city is where existing facilities and services are enhanced by digital technology to benefit people and companies. The most critical infrastructures in this city are interconnected. Increased data exchange across municipal domains aims to manage the essential assets, leading to more automation in city governance and optimization of the dynamic offered services. However, no clear guideline or standard exists for modeling these data flows. As a result, operators, municipalities, policymakers, manufacturers, solution providers, and vendors are forced to accept systems with limited scalability and varying needs. Nonetheless, it is critical to raise awareness about smart-city cybersecurity and implement suitable measures to safeguard citizens’ privacy and security because cyber threats seem to be well-organized, diverse, and sophisticated. This study aims to present an overview of cyber threats, attacks, and countermeasures on the primary domains of smart cities (smart government, smart mobility, smart environment, smart living, smart healthcare, smart economy, and smart people). It aims to present information extracted from the state of the art so policymakers can perceive the critical situation and simultaneously be a valuable resource for the scientific community. It also seeks to offer a structural reference model that may guide the architectural design and implementation of infrastructure upgrades linked to smart city networks. 

/pdf/an-overview-of-cyber-threats-attacks-and-countermeasures-on-n4duzebl.pdf

An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities

Hybrid Grey PIPRECIA and Grey OCRA method-based dynamic multi-criteria decision-making model for mitigating non-cooperating node attacks in WSNs

Wireless sensor network (WSN) nodes owing to their openness, are susceptible to several threats, one of which is dishonest recommendation attacks providing false trust values that favor the attacker. In this letter, a malicious node detection strategy is proposed based on a fuzzy trust model and artificial bee colony algorithm (ABC) (FTM-ABC). The fuzzy trust model (FTM) is introduced to calculate the indirect trust, and the ABC algorithm is applied to optimize the trust model for detecting dishonest recommendation attacks. Besides, fitness function includes recommended deviation and interaction index deviation to enhance the effectiveness. Simulation results reveal the improved FTM-ABC maintains a high recognition rate and a low false-positive rate, even if the number of dishonest nodes reaches 50%.

A Malicious Node Detection Strategy Based on Fuzzy Trust Model and the ABC Algorithm in Wireless Sensor Network

The openness of Wireless Sensor Networks (WSNs) layout makes them vulnerable to attacks. One of the most threatening attacks is the wormhole attack. The wormhole attack is difficult to be detected since it causes false routing by private tunnels and damages to WSNs in data transmission. Therefore, in order to resist wormhole attacks and improve network performance, this paper proposes a detection algorithm integrated with the node trust optimization model (NTOM-DA) against wormholes in WSNs. First, add the node whose number of neighbors exceeds the threshold to the list of suspicious nodes, and then the exclusive neighbors of suspicious nodes communicate with each other. Mark the path whose hops exceed the wormhole threshold as the path to be tested. Establish a trust model to calculate the node trust and evaluate the path trust. The simulation results show that NTOM-DA is superior in detecting wormhole attacks. Even in the face of a network with high node density and high attack degree, it still has a high detection rate and a low false-positive rate, which effectively guarantees the safe and reliable operation of WSNs.

A Wormhole Attack Detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

Wireless sensor network (WSN) works in a complex environment where it is difficult for people to reach or work. The openness of nodes leads to security threats vulnerable to various attacks. The trust and reputation model can be applied in WSN to reduce damage caused by malicious nodes. However, there is a high false-positive rate in trust and reputation models because a node with less reputation due to the communication environment is judged as a malicious one directly. This paper presents a trust & reputation-based malicious node identification strategy with environmental parameters (TRS&EP) to interdict the malicious nodes, such as interrupt attack nodes and selective forwarding attack nodes. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then TRS&EP estimates benchmark trust according to the environmental parameters. The Gaussian radial basis function is simplified to calculate the similarity between the benchmark trust sequence and cycle reputation sequence. Furthermore, TRS&EP sets three reputation intervals and an adoptive threshold span to identify the malicious nodes by dynamically considering the work environment and states of nodes. The simulated results show that TRS&EP improves the recognition of malicious nodes above 1% compared to comparison algorithms and reduces the false-positive percentage by more than 1%.

/pdf/malicious-node-identification-strategy-with-environmental-1tpgs0vf6k.pdf

Malicious Node Identification Strategy With Environmental Parameters

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A Malicious Node Detection Strategy Based on Fuzzy Trust Model and the ABC Algorithm in Wireless Sensor Network

A Wormhole Attack Detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

Malicious Node Identification Strategy With Environmental Parameters