In this paper, we propose a framework for efficient and privacy-preserving outsourced calculation of

rational numbers, which we refer to as POCR. Using POCR, a user can securely outsource the storing

and processing of rational numbers to a cloud server without compromising the security of the

(original) data and the computed results. More specifically, we present a Paillier cryptosystem with

threshold decryption (PCTD), the core cryptographic primitive, to reduce the private key exposure risk

in POCR. We also present the toolkits required in the privacy preserving calculation of integers and

rational numbers to ensure that commonly used outsourced operations can be handled on-the-fly. We

then prove that the proposed POCR achieves the goal of secure integer and rational number calculation

without resulting in privacy leakage to unauthorized parties, as well as demonstrating the utility and

the efficiency of POCR using simulations.

ETPL

DSC -001 Efficient and Privacy-Preserving Outsourced Calculation of Rational

Numbers

Bandwidth measurement is important for many network applications and services, such as peer-to-peer

networks, video caching and anonymity services. To win a bandwidth-based competition for some

malicious purpose, adversarial Internet hosts may falsely announce a larger network bandwidth. Some

preliminary solutions have been proposed to this problem. They can either evade the bandwidth

inflation by a consensus view (i.e., opportunistic bandwidth measurements) or detect bandwidth frauds

via forgeable tricks (i.e., detection through bandwidth’s CDF symmetry). However, smart adversaries

can easily remove the forgeable tricks and report an equally larger bandwidth to avoid the consensus

analyses. To defend against the smart bandwidth inflation frauds, we design magic train, a new

measurement method which combines an unpredictable packet train with estimated round-trip time

(RTT) for detection. The inflation behaviors can be detected through highly contradictory bandwidth

results calculated using different magic trains or a train’s different segments, or large deviation between

the estimated RTT and the RTT reported by the train’s first packet. Being an uncooperative

measurement method, magic train can be easily deployed on the Internet. We have implemented the

magic train using RAW socket and LibPcap, and evaluated the implementation in a controlled testbed

and the Internet. The results have successfully confirmed magic train’s effectiveness in detecting and

preventing smart bandwidth inflation attacks.

ETPL

DSC - 002 Magic Train: Design of Measurement Methods against Bandwidth

Inflation Attacks

Delay tolerant networks (DTNs) are often encountered in military network environments where end-

to-end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a

provenance-based trust framework, namely PROVEST (PROVEnance-baSed Trust model) that aims

to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages

received by destination nodes while minimizing message delay and communication cost under

resource-constrained network environments. Provenance refers to the history of ownership of a valued

object or information. We leverage the interdependency between trustworthiness of information source

and information itself in PROVEST. PROVEST takes a data-driven approach to reduce resource

consumption in the presence of selfish or malicious nodes while estimating a node’s trust dynamically

in response to changes in the environmental and node conditions. This work adopts a model-based

method to evaluate the performance of PROVEST (i.e., trust accuracy and routing performance) using

Stochastic Petri Nets. We conduct a comparative performance analysis of PROVEST against existing

trust-based and non-trust-based DTN routing protocols to analyze the benefits of PROVEST. We

validate PROVEST using a real dataset of DTN mobility traces.

ETPL

DSC -003 PROVEST: Provenance-based Trust Model for Delay Tolerant Networks

Off-The-Shelf (OTS) software components are the cornerstone of modern systems, including safety-

critical ones. However, the dependability of OTS components is uncertain due to the lack of source

code, design artifacts and test cases, since only their binary code is supplied. Fault injection in

components’ binary code is a solution to understand the risks posed by buggy OTS components. In this

paper, we consider the problem of the accurate mutation of binary code for fault injection purposes.

Fault injection emulates bugs in high-level programming constructs (assignments, expressions,

function calls, ...) by mutating their translation in binary code. However, the semantic gap between the

source code and its binary translation often leads to inaccurate mutations. We propose Faultprog, a

systematic approach for testing the accuracy of binary mutation tools. Faultprog automatically

generates synthetic programs using a stochastic grammar, and mutates both their binary code with the

tool under test, and their source code as reference for comparisons. Moreover, we present a case study

on a commercial binary mutation tool, where Faultprog was adopted to identify code patterns and

compiler optimizations that affect its mutation accuracy.

ETPL

DSC - 004 Faultprog: Testing the Accuracy of Binary-Level Software Fault

Injection

More and more users are attracted by P2P networks characterized by decentralization, autonomy and

anonymity. However, users’ unconstrained behavior makes it necessary to use a trust model when

establishing trust relationships between peers. Most existing trust models are based on

recommendations, which, however, suffer from the shortcomings of slow convergence and high

complexity of trust computations, as well as huge overhead of network traffic. Inspired by the

establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is

proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the

service it is going to provide, and they are both required to pledge reputation mortgages for the service.

The request peer makes evaluations on all the candidates of service peer by referring their service

reputations and their guarantee peers’ reputations, and selects the one with highest evaluation to be its

service provider. In order to enhance GeTrust’s availability and prevent malicious behavior, we also

present incentive mechanism and anonymous reputation management strategy. Simulation results show

that GeTrust is effective and efficient in terms of improving successful transaction rate, resisting

complex attacks, reducing network overhead and lowering computational complexity.

ETPL

DSC -005 GeTrust: A guarantee-based trust model in Chord-based P2P networks

Information Centric Networking (ICN) is a new network architecture that aims to overcome the

weakness of existing IPbased networking architecture. Instead of establishing a connection between

the communicating hosts, ICN focuses on the content, i.e. data, transmitted in network. Content copies

in ICN can be cached at different locations. The content is out of its owner’s control once it is published.

Thus, enforcing access control policies on distributed content copies is crucial in ICN. Attribute-Based

Encryption (ABE) is a feasible approach to enforce such control mechanisms in this environment.

However, applying ABE in ICN faces two challenges: from management perspective, it is complicated

to manage attributes in distributed manners; from privacy protection perspective, unlike in traditional

networks, the enforced content access policies are public to all the ICN users. Thus, it is desirable that

unauthorized content viewers are not able to retrieve the access policy. To this end, a privacy-

preserving access control scheme for ICN and its corresponding attribute management solution are

presented in this paper. The proposed approach is compatible with existing flat name based ICN

architectures.

ETPL

DSC - 006 Attribute-based Access Control for ICN Naming Scheme

Although the dramatic increase in OSN usage, there are still a lot of security and privacy concerns. In

such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each

OSN user. In this paper, we propose a risk assessment based on the idea that the more a user behavior

diverges from what it can be considered as a ‘normal behavior’, the more it should be considered risky.

In doing this, we have takein into account that OSN population is really heterogeneous in observed

behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN

users’ behaviors. However, we expect that similar people tend to follow the similar rules with the

results of similar behavioral models. For this reason, we propose a risk assessment organized into two

phases: similar users are first grouped together, then, for each identified group, we build one or more

models for normal behavior. The carried out experiments on a real Facebook dataset show that the

proposed model outperforms a simplified behavioral-based risk assessment where behavioral models

are built over the whole OSN population, without a group identification phase.

ETPL

DSC -007 Risk Assessment in Social Networks based on User Anomalous Behaviour

In many existing incentive-based mobile sensing applications, the sensing job owner runs an auction

with the mobile phone users to maximize its purchased sensing resource. We notice that both the mobile

phone users and the job owner could behave dishonestly to pursue their own interests. This motivates

us to design secure and dependable auction mechanisms that generate the correct, promising output

even when both of them could cheat. In particular, in this paper, we consider a general auction in which

a buyer, who acts as the auctioneer, purchases the resource under a limited budget from a group of

sellers who act as the bidders. Considering bidders' privacy and their limited computing capacity, we

construct our mechanisms by integrating the innovative game theoretical techniques, logic deductions,

and efficient cryptographic operations. Our mechanisms are not only proved to be strategy-proof

against dishonest bidders in the sense that they are incentivized to bid their private types truthfully, but

also enable all the bidders to efficiently verify the correctness of the auction's outcome, that is

computed by the auctioneer, without revealing their private types to each other. Meanwhile, our

mechanisms are proved to have the theoretical guarantee that the auctioneer/buyer's expected revenue

(i.e. the amount of service it acquires after the auction) is no less than a certain portion of the optimal

revenue that the auctioneer can acquire when it knows all the bidders' types at no cost. Our extensive

evaluations show that our mechanisms achieve good performance in terms of the revenue maximization

and their efficiency.

ETPL

DSC - 008 Designing Secure and Dependable Mobile Sensing Mechanisms with

Revenue Guarantees

In this paper we present a framework and a set of algorithms for determining faults in networks when

large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact

that failures are geographically clustered in such cases. We address the challenge of determining faults

with incomplete symptom information due to a limited number of reporting nodes in the network.

netCSI consists of two parts: hypotheses generation algorithm, and ranking algorithm. When

constructing the hypotheses list of potential causes, we make novel use of the positive and negative

symptoms to improve the precision of the results. The ranking algorithm is based on conditional failure

probability models that account for the geographic correlation of the network objects in clustered

failures. We evaluate the performance of netCSI for networks with both random and realistic

topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-

COVERAGE, and achieve an average gain of 128% in accuracy for realistic topologies.

ETPL

DSC - 009 netCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in

Computer Networks

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services

with QoS and security guarantees. In vehicular ad hoc networks (VANETs), vehicles perform routing

functions, and at the same time act as end-systems thus routing control messages are transmitted

unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on

the routing process, and manipulation of the routing control messages. In this paper, we propose a

novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ

the ant colony optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple

QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented

evolving graph (VoEG) model to perform plausibility checks on the routing control messages

exchanged among vehicles. Simulation results show that the QoS can be guaranteed while applying

security mechanisms to ensure a reliable and robust routing service.

ETPL

DSC - 010 Secure and Robust Multi-Constrained QoS Aware Routing Algorithm for

VANETs

In recent years, wireless sensor networks have been widely used in healthcare applications, such as

hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to

eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of

work has been done to secure wireless medical sensor networks. The existing solutions can protect the

patient data during transmission, but cannot stop the inside attack where the administrator of the patient

database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent

the inside attack by using multiple data servers to store patient data. The main contribution of this paper

is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal

cryptosystems to perform statistic analysis on the patient data without compromising the patients'

privacy.

ETPL

DSC - 011 Privacy Protection for Wireless Medical Sensor Data

Credit and debit card data theft is one of the earliest forms of cybercrime. Still, it is one of the most

common nowadays. Attackers often aim at stealing such customer data by targeting the Point of Sale

(for short, PoS) system, i.e. the point at which a retailer first acquires customer data. Modern PoS

systems are powerful computers equipped with a card reader and running specialized software.

Increasingly often, user devices are leveraged as input to the PoS. In these scenarios, malware that can

steal card data as soon as they are read by the device has flourished. As such, in cases where customer

and vendor are persistently or intermittently disconnected from the network, no secure on-line payment

is possible. This paper describes FRoDO, a secure off-line micro-payment solution that is resilient to

PoS data breaches. Our solution improves over up to date approaches in terms of flexibility and

security. To the best of our knowledge, FRoDO is the first solution that can provide secure fully off-

line payments while being resilient to all currently known PoS breaches. In particular, we detail

FRoDO architecture, components, and protocols. Further, a thorough analysis of FRoDO functional

and security properties is provided, showing its effectiveness and viability.

ETPL

DSC - 012 FRoDO: Fraud Resilient Device for Off-Line Micro-Payments

A fundamental problem in the specification of regulatory privacy policies such as the Health Insurance

Portability and Accountability Act (HIPAA) in a computer system is to state the policies precisely,

consistent with their high-level intuition. In this paper, we propose UML sequence diagrams as a

practical means to graphically express privacy policies. A graphical representation allows decision-

makers such as application domain experts and security architects to easily verify and confirm the

expected behavior. Once intuitively confirmed, our work in this article introduces an algorithmic

approach to formalizing the semantics of sequence diagrams in terms of linear temporal logic (LTL)

templates. In all the templates, different semantic aspects are expressed as separate, yet simple LTL

formulas that can be composed to define the complex semantics of sequence diagrams. The

formalization enables us to leverage the analytical powers of automated decision procedures for LTL

formulas to determine if a collection of sequence diagrams is consistent, independent, etc. and also to

verify if a system design conforms to the privacy policies. We evaluate our approach by modeling and

analyzing a substantial subset of HIPAA rules using sequence diagrams.

ETPL

DSC - 013 Sequence Diagram Aided Privacy Policy Specification

A fundamental problem in the specification of regulatory privacy policies such as the Health Insurance

Portability and Accountability Act (HIPAA) in a computer system is to state the policies precisely,

consistent with their high-level intuition. In this paper, we propose UML sequence diagrams as a

practical means to graphically express privacy policies. A graphical representation allows decision-

makers such as application domain experts and security architects to easily verify and confirm the

expected behavior. Once intuitively confirmed, our work in this article introduces an algorithmic

approach to formalizing the semantics of sequence diagrams in terms of linear temporal logic (LTL)

templates. In all the templates, different semantic aspects are expressed as separate, yet simple LTL

formulas that can be composed to define the complex semantics of sequence diagrams. The

formalization enables us to leverage the analytical powers of automated decision procedures for LTL

formulas to determine if a collection of sequence diagrams is consistent, independent, etc. and also to

verify if a system design conforms to the privacy policies. We evaluate our approach by modeling and

analyzing a substantial subset of HIPAA rules using sequence diagrams.

ETPL

DSC - 014 Sequence Diagram Aided Privacy Policy Specification

Bandwidth measurement is important for many network applications and services, such as peer-to-peer

networks, video caching and anonymity services. To win a bandwidth-based competition for some

malicious purpose, adversarial Internet hosts may falsely announce a larger network bandwidth. Some

preliminary solutions have been proposed to this problem. They can either evade the bandwidth

inflation by a consensus view (i.e., opportunistic bandwidth measurements) or detect bandwidth frauds

via forgeable tricks (i.e., detection through bandwidth’s CDF symmetry). However, smart adversaries

can easily remove the forgeable tricks and report an equally larger bandwidth to avoid the consensus

analyses. To defend against the smart bandwidth inflation frauds, we design magic train, a new

measurement method which combines an unpredictable packet train with estimated round-trip time

(RTT) for detection. The inflation behaviors can be detected through highly contradictory bandwidth

results calculated using different magic trains or a train’s different segments, or large deviation between

the estimated RTT and the RTT reported by the train’s first packet. Being an uncooperative

measurement method, magic train can be easily deployed on the Internet. We have implemented the

magic train using RAW socket and LibPcap, and evaluated the implementation in a controlled testbed

and the Internet. The results have successfully confirmed magic train’s effectiveness in detecting and

preventing smart bandwidth inflation attacks.

ETPL

DSC - 015 Magic Train: Design of Measurement Methods against Bandwidth

Inflation Attacks

In vehicular ad-hoc network (VANET), the reliability of communication is associated with driving

safety. However, research shows that the safety-message transmission in VANET may be congested

under some urgent communication cases. More spectrum resource is an effective way to solve

transmission congestion. Hence, we introduce cognitive radio (CR) enabled VANET (CR-VANET),

where CR device can detect possible idle spectrum for VANET communications and assist to timely

broadcast safety-message. Given high-speed mobility of vehicles and dynamically-changing

availability of channels, a novel prediction algorithm is proposed to pick out the channel with the

greatest probability of availability, which can meet the quality of service (QoS) requirement of urgent

communications and effectively avoid conflict with licensed users. Specifically, the spatiotemporal

correlations among historical spectrum sensing data are exploited to form prior knowledge of channel

availability probability, and Bayesian inference is used to derive posterior probability of channel

availability. Comparing with other spectrum detection methods, the proposed algorithm has more than

8 percent detection performance improvement at false alarm probability 0.2, and thus can avoid access

conflict with licensed users dramatically. Furthermore, the proposed algorithm always has larger packet

reception probability (PRP) and lower transmission delay compared with conventional VANET

broadcasting. Hence, the proposed algorithm can improve reliability of safety-message transmission

and enhance driving safety significantly.

ETPL

DSC - 016 Historical Spectrum Sensing Data Mining for Cognitive Radio Enabled

Vehicular Ad-Hoc Networks

In vehicular networks, broadcast communications are critically important, as many safety-related

applications rely on single-hop beacon messages broadcast to neighbor vehicles. However, it becomes

a challenging problem to design a broadcast authentication scheme for secure vehicle-to-vehicle

communications. Especially when a large number of beacons arrive in a short time, vehicles are

vulnerable to computation-based Denial of Service (DoS) attacks that excessive signature verification

exhausts their computational resources. In this paper, we propose an efficient broadcast authentication

scheme called Prediction-Based Authentication (PBA) to not only defend against computation-based

DoS attacks, but also resist packet losses caused by high mobility of vehicles. In contrast to most

existing authentication schemes, our PBA is an efficient and lightweight scheme since it is primarily

built on symmetric cryptography. To further reduce the verification delay for some emergency

applications, PBA is designed to exploit the sender vehicle's ability to predict future beacons in

advance. In addition, to prevent memory-based DoS attacks, PBA only stores shortened re-keyed

Message Authentication Codes (MACs) of signatures without decreasing security. We analyze the

security of our scheme and simulate PBA under varying vehicular network scenarios. The results

demonstrate that PBA fast verifies almost 99 percent messages with low storage cost not only in high-

density traffic environments but also in lossy wireless environments.

ETPL

DSC - 017 PBA: Prediction-Based Authentication for Vehicle-to-Vehicle

Communications

Vehicular social network (VSN) is envisioned to serve as an essential data sensing, exchanging and

processing platform for the future Intelligent Transportation Systems. In this paper, we aim to address

the location privacy issue in VSNs. In traditional pseudonym-based solutions, the privacy-preserving

strength is mainly dependent on the number of vehicles meeting at the same occasion. We notice that

an individual vehicle actually has many chances to meet several other vehicles. In most meeting

occasions, there are only few vehicles appearing concurrently. Motivated by these observations, we

propose a new privacy-preserving scheme, called MixGroup, which is capable of efficiently exploiting

the sparse meeting opportunities for pseudonym changing. By integrating the group signature

mechanism, MixGroup constructs extended pseudonym-changing regions, in which vehicles are

allowed to successively exchange their pseudonyms. As a consequence, for the tracking adversary, the

uncertainty of pseudonym mixture is accumulatively enlarged, and therefore location privacy

preservation is considerably improved. We carry out simulations to verify the performance of

MixGroup. Results indicate that MixGroup significantly outperforms the existing schemes. In addition,

MixGroup is able to achieve favorable performance even in low traffic conditions.

ETPL

DSC - 018 MixGroup: Accumulative Pseudonym Exchanging for Location Privacy

Enhancement in Vehicular Social Networks

Cooperation between vehicles facilitates traffic management, road safety and infotainment

applications. Cooperation, however, requires trust in the validity of the received information. In this

paper, we tackle the challenge of securely exchanging parking spot availability information. Trust is

crucial in order to support the decision of whether the querying vehicle should rely on the received

information about free parking spots close to its destination and thus ignore other potentially free spots

on the way. Therefore, we propose Parking Communities, which provide a distributed and dynamic

means to establish trusted groups of vehicles helping each other to securely find parking in their

respective community area. Our approach is based on high-performance state-of-the-art encryption and

signature algorithms as well as a well-understood mathematical trust rating model. This approach

allows end-to-end encrypted request-response communications in combination with geocast and can

be used as an overlay to existing vehicular networking technologies. We provide a comprehensive

comparison with other security architectures and simulation results showing the feasibility of our

approach.

ETPL

DSC - 019 Trustworthy Parking Communities: Helping Your Neighbor to Find a

Space

More and more users are attracted by P2P networks characterized by decentralization, autonomy and

anonymity. However, users’ unconstrained behavior makes it necessary to use a trust model when

establishing trust relationships between peers. Most existing trust models are based on

recommendations, which, however, suffer from the shortcomings of slow convergence and high

complexity of trust computations, as well as huge overhead of network traffic. Inspired by the

establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is

proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the

service it is going to provide, and they are both required to pledge reputation mortgages for the service.

The request peer makes evaluations on all the candidates of service peer by referring their service

reputations and their guarantee peers’ reputations, and selects the one with highest evaluation to be its

service provider. In order to enhance GeTrust’s availability and prevent malicious behavior, we also

present incentive mechanism and anonymous reputation management strategy. Simulation results show

that GeTrust is effective and efficient in terms of improving successful transaction rate, resisting

complex attacks, reducing network overhead and lowering computational complexity.

ETPL

DSC - 020 GeTrust: A guarantee-based trust model in Chord-based P2P networks

In this paper, we study a group key agreement problem where a user is only aware of his neighbors

while the connectivity graph is arbitrary. In our problem, there is no centralized initialization for users.

A group key agreement with these features is very suitable for social networks. Under our setting, we

construct two efficient protocols with passive security. We obtain lower bounds on the round

complexity for this type of protocol, which demonstrates that our constructions are round efficient.

Finally, we construct an actively secure protocol from a passively secure one.

ETPL

DSC - 022 Group Key Agreement with Local Connectivity

Cloud services are prominent within the private, public and commercial domains. Many of these

services are expected to be always on and have a critical nature; therefore, security and resilience are

increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react

not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we

introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection

components of our cloud resilience architecture. More specifically, we exhibit the applicability of

novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor

level, through the utilisation of features gathered at the system and network levels of a cloud node. We

demonstrate that our scheme can reach a high detection accuracy of over $90$ percent whilst detecting

various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only

system-level data, but also network-level data depending on the attack type. Finally, the paper shows

that our approach to detection using dedicated monitoring components per VM is particularly

applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware

strains with no prior knowledge of their functionality or their underlying instructions.

ETPL

DSC - 021 Malware Detection in Cloud Computing Infrastructures

In this article, the authors analyze the security and dependability challenges for developing dependable

and secure Cloud applications. They also provide an overview of their research and development that

aims to ameliorate some of the obstacles.

ETPL

DSC - 023 Developing Dependable and Secure Cloud Applications

Although the dramatic increase in OSN usage, there are still a lot of security and privacy concerns. In

such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each

OSN user. In this paper, we propose a risk assessment based on the idea that the more a user behavior

diverges from what it can be considered as a ‘normal behavior’, the more it should be considered risky.

In doing this, we have takein into account that OSN population is really heterogeneous in observed

behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN

users’ behaviors. However, we expect that similar people tend to follow the similar rules with the

results of similar behavioral models. For this reason, we propose a risk assessment organized into two

phases: similar users are first grouped together, then, for each identified group, we build one or more

models for normal behavior. The carried out experiments on a real Facebook dataset show that the

proposed model outperforms a simplified behavioral-based risk assessment where behavioral models

are built over the whole OSN population, without a group identification phase.

ETPL

DSC - 024 Risk Assessment in Social Networks based on User Anomalous

Behaviour

With the advancement and deployment of leading-edge telecommunication technologies for sensing

and collecting traffic related information, the vehicular ad-hoc networks (VANETs) have emerged as

a new application scenario that is envisioned to revolutionize the human driving experiences and traffic

flow control systems. To avoid any possible malicious attack and resource abuse, employing

lightweight cryptosystems is widely recognized as one of the most effective approaches for the

VANETs to achieve confidentiality, integrity and authentication. As a typical substitution-permutation

network lightweight cryptosystem, LED supports 64-bit and 128-bit secret keys, which are flexible to

provide security for the RFID and other highly-constrained devices in the VANETs. Since its

introduction, some research of fault analysis has been devoted to attacking the last three rounds of

LED. It is an open problem to know whether provoking faults at a former round of LED allows

recovering the secret key. In this paper, we give an answer to this problem by showing a novel

impossible differential fault analysis on one round earlier of all LED keysize variants. Mathematical

analysis and simulating experiments show that the attack could recover the 64-bit and 128-bit secret

keys of LED by introducing 48 faults and 96 faults in average, respectively. The result in this study

describes that LED is vulnerable to a half byte impossible differential fault analysis. It will be beneficial

to the analysis of the same type of other iterated lightweight cryptosystems in the VANETs.

ETPL

DSC - 025 Impossible Differential Fault Analysis on the LED Lightweight

Cryptosystem in the Vehicular Ad-Hoc Networks