Adversary Model. In our system model, the UAVs mainly use public network based communication. Consequently, there is a possibility of various attacks by a wide range of adversaries. In this paper, we consider the following security and privacy threats:
Adversary Model. The malicious nodes are assumed to have the following properties:
1. The total energy available to the adversary is the same as the energy available to any of the non faulty nodes.
2. The cost to violate the safety conditions must be large in terms of energy re- quirements.
3. The adversary can be modeled as a user who has to violate safety with access to Ef amount of energy from the system.
Adversary Model. In the proposed BioKA-ASVN, all the network entities communicate over the insecure wireless media. A user Ui can send a drone access request via GS, and the drone DRj can also send the sensing information to the associated GS via a public channel (e.g., Wi-Fi or wireless media). The majority of the information are private and confidential in the drone environment (for example, battle field, smart agriculture, and boarder surveillance). Since the information are exchanged over the insecure channel, there is a security concern. According to the Xxxxx-Xxx (DY) threat model [18], an unauthorized user (also called an adversary, ) not only can eavesdrop on the communication messages, but can also delete, modify, and inject malicious contents into the communication channel. We also adopt the de-facto and widely-recognized Xxxxxxx and Xxxxxxxx (CK)-threat model [19], where has more provision than the DY threat model. Under the CK-adversary threat model, is not restricted to intercepting, modifying, deleting, or inserting incorporated messages of various entities (for example, Ui, DRj, GS, and CS) engaged in the network as indicated in the DY model. Additionally, is also capable of capturing long-term secrets, short-term keys, and session states by hijacking a session if these credentials are loaded inside an insecure memory of communicating parties during the mutual authentication and key agreement phase. Blockchain Blockchain P2P Cloud Servers Ground Server (GS) (Authentication Server) User User Airspace for Drones
Fig. 1. Blockchain-based network model for air smart vehicular networks. Due to hostile environment, can physically capture a drone using any of the techniques: 1) “shoot it down with a gun”, 2) “use anti-drone drones”, 3) “use net-firing anti- drone guns”, 4) “jam the drone’s radio signal”, and 5) “use trained eagles to capture drones” [20]. xxx then attempt to launch further attacks, such as identity disclose, impersonation attacks and so on, using the retrieved secret information saved in the physically kidnapped DRj and side-channel attacks such as power analysis attacks [21]. In this paper, since GS is considered as a fully trusted authority and responsible for generating all public and private keys as well as certificates, all the generated public keys are authentic. Thus, it is not required to consider any other public key infrastructure (PKI) for public key authentication. In this work, we consider identity (ID), password (Pw), bio-metric templ...
Adversary Model. The adversary in our protocol is modelled as a (q, tp, κ)-algorithm A as defined above. A can control at most q parties each with a maximum speedup of κ, such that q < ⌊κn holds. In particular, the number of adversarial parties can be at most n ⌋+1 < 2 (this is the case for κ = 1). We consider an adaptive adversary which can corrupt a party at any point during the protocol execution. Once a party has been corrupted, it can arbitrarily deviate from the protocol execution. Furthermore, it can deliver a message over the multicast channel only to a subset of honest parties. In this way, it can send different messages to different subsets of honest parties over the multicast channel. However, the adversary can not drop the messages of honest parties from the channel or delay them for longer than ∆. Our adversary is xxxxxxx, which means it can observe all the messages that the honest parties send in any round of the protocol, and then choose its own messages for that round adaptively. We notice that we consider the standard notion of an adaptive, xxxxxxx adversary, as opposed to the stronger notion of a strongly xxxxxxx (or strongly adaptive) adversary (see for e.g., [ADD+19,ACD+19,CGZ21]) who can adaptively corrupt parties and then delete messages that they sent in the same round (prior to corruption).
Adversary Model. The adversary model determines the capabilities and possi- ble actions of the attacker. Similar to [11], [26] and [27], the adversary model is defined as follows.
1. The adversary reveals a long-term secret key of a participant in a conference and then impersonates others to this participant.
2. The adversary reveals some previous session keys and then learns the information about the session key of a fresh participant. Consequently, the adversary can impersonate the fresh participant with the session key to others.
3. The adversary reveals the long-term keys of one or more participants in the current run. Then, the adversary attempts to learn the previous session key.
Adversary Model. The Ȃ, being a probabilistic polynomial- time Turing machine for the protocol run, might eavesdrop, modify the messages, and fully control the public channel. This activity could be represented by the understated queries:
1. Complexity assumptions: Since, the proposed model’s
Adversary Model. To analyze the security of
Adversary Model. We assume a globally passive attacker that is capable of eavesdropping traffic of the entire wireless network. The attacker can eavesdrop, inject, modify, drop messages within the network at will. However, the attacker has only bounded computation capability, and is not capable of breaking the ID- based encryption system. That is, the ECDH problem and the BDH problem are assumed to be hard.
Adversary Model. We assume a passive adversary capable of eavesdropping the control input and sensor measurements transmitted between the plant and the networked controller, see Eve in Fig. 2. We also assume that the adversary is aware that the robot is a differential-drive robot but it might not have exact knowledge of all the robot’s parameters (e.g., T, r, D, W) and robot’s measurement function (e.g., h(·) and V). Therefore, we assume that the adversary has the following model: px(k + 1) = px(k)+ Xxxx px support, is often adopted in the industry (Xxxxxxx et al., 2 cos θa(k)(ωr(k)+ωl(k))+ζa (k) 2017). A schematic of a differential-drive robot is shown in Fig. 1a. The pose of a differential-drive robot is described by py(k + 1) = py(k)+ Xxxx sin θ (k)(ω (k)+ω (k))+ζpy (k)
Adversary Model. We consider an adaptive Byzantine adversary that can corrupt up to t < n/2 parties at any point of a protocol execution. We refer to the actual number of corruptions during an execution of the protocol as f t. A corrupt (or malicious) party Pi is under full control of the adversary and may deviate arbitrarily from the protocol. In particular, the adversary learns Xx’s signing key ski, which allows it to sign messages on Pi’s behalf. In addition, we allow the adversary to delete (or replace with its own) any undelivered messages of a newly corrupted party Pi that Pi sent while it was still honest. We denote the set of uncorrupted (or honest) parties as . We assume that the adversary is computationally bounded and cannot forge signatures of honest parties. In line with the literature in this area, we treat signatures as idealized primitives with perfect security. When instantiating the signature scheme with an existentially unforgeable one, we obtain protocols with non-neglible probability of failure. Common Coin. We assume an ideal coin-flip protocol CoinFlip that allows parties to agree with constant probability p < 1 on a random coin in 0, 1 . This protocol can be viewed as an ideal functionality [20] that upon receiving input r from t + 1 parties generates a random coin ci and sends (c(r)) to each party Pi ∈ P, where c(r) = c(r) with probability at least p. The value remains uniform from the adversary’s view until the first honest party has queried CoinFlip. Such a primitive can be achieved using verfiable random functions [21], threshold signatures [22], or verifiable secret sharing [14]. We begin by presenting definitions of well-known primitives, such as Byzan- tine agreement and graded consensus. Following this, we introduce new defini- tions for our proposed protocols: graded consensus with detection.
