Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

/pdf/symmetrical-simulation-scheme-for-anomaly-detection-in-2c8n8stq.pdf

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

Intrusion detection based on machine learning in the internet of things, attacks and counter measures

: The Sony cyber incident in November 2014 has inspired questions as to the best method of providing cybersecurity in the United States. Specifically, who is best postured to provide cybersecurity and on what parts of the cyber domain should this security be focused? The United States government takes seriously the threat of cyberspace attacks; however, not all cyberspace incidents should result in the initiation of armed conflict. This paper argues the Sony cyber incident should not be considered a cyberspace attack as it relates to the law of war. It then reviews the current role of the Department of Homeland Security with respect to cybersecurity for critical infrastructure and key resources. Next, it will review the authorities for states National Guard units to provide cybersecurity for critical infrastructure and key resources. Finally, this paper discusses how the National Guard is best postured to safeguard the critical infrastructure and key resources within their borders.

/pdf/cybersecurity-for-critical-infrastructure-1790nt90ev.pdf

Cybersecurity for Critical Infrastructure

Nowadays, technological advancement has transformed traditional vehicles into Au-tonomous Vehicles (A.V.s). In addition, in our daily lives, A.V.s play an important role since they are considered an essential component of smart cities. A.V. is an intelligent vehicle capable of main-taining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, A.V.s collect information about the outside environment using sensors to ensure safe navigation. Furthermore, A.V.s reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, A.V.s could be threatened by cyberattacks, posing risks to human life. For example, re-searchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW AVs. Therefore, more research is needed to detect cyberattacks targeting the components of A.V.s to mitigate their negative consequences. This research will contribute to the security of A.V.s by detecting cyberattacks at the early stages. First, we inject False Data Injection (FDI) attacks into an A.V. simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify FDI attacks targeting the control system of the A.V. through a compromised sensor. We use long short-term memory (LSTM) deep networks to detect FDI attacks in the early stage to ensure the stability of the operation of A.V.s. Our method classifies the collected dataset into two classifications: normal and anomaly data. The ex-perimental result shows that our proposed model's accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area. 

The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods. 

/pdf/decentralized-blockchain-network-for-resisting-side-channel-1j2eav2g.pdf

Decentralized Blockchain Network for Resisting Side-Channel Attacks in Mobility-Based IoT

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

https://www.mdpi.com/2076-3417/11/16/7228/pdf

Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

With the increase of Internet of Things (IoT) applications, securing their communications is an important task. In multi-hop wireless networks, nodes must unconditionally trust their neighbours when performing routing activities. However, this is often their downfall as malicious nodes can infiltrate the network and cause disruptions during routing. We grant nodes the ability to evaluate the behaviour of their neighbours and, through consensus inspired from blockchain's miners, agree on the credibility of each node. The resulting metric is expressed as a node's reputation allowing, in the case of a malicious node, to isolate it from network operations. By illustrating this in an AODV-like multi-hop routing protocol, we can influence route selection no longer based solely upon the shortest number of hops, but also the highest overall reputation. Simulation results revealed that our approach can decrease packet drop rates by ≈ 48% in a static context when subjected to multiple black hole attacks compared to the original routing protocol.

AODV-Miner: Consensus-Based Routing Using Node Reputation

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Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

AODV-Miner: Consensus-Based Routing Using Node Reputation