Biblio

Platoon is one of cooperative driving applications where a set of vehicles can collaboratively sense each other for driving safety and traffic efficiency. However, platoon without security insurance makes the cooperative vehicles vulnerable to cyber-attacks, which may cause life-threatening accidents. In this paper, we introduce malicious attacks in platoon maneuvers. To defend against these attacks, we propose a Cyphertext-Policy Attribute-Based Encryption (CP-ABE) based Platoon Secure Sensing scheme, named CPSS. In the CPSS, platoon key is encapsulated in the access control structure in the key distribution process, so that interference messages sending by attackers without the platoon key could be ignored. Therefore, the sensing data which contains speed and position information can be protected. In this way, speed and distance fluctuations caused by attacks can be mitigated even eliminated thereby avoiding the collisions and ensuring the overall platoon stability. Time complexity analysis shows that the CPSS is more efficient than that of the polynomial time solutions. Finally, to evaluate capabilities of the CPSS, we integrate a LTE-V2X with platoon maneuvers based on Veins platform. The evaluation results show that the CPSS outperforms the baseline algorithm by 25% in terms of distance variations.

Density information has been used as a property of sound to restore objects in a quantitative manner in ultrasound tomography based on backscatter theory. In the traditional method, the authors only study the distorted Born iterative method (DBIM) to create density images using Tikhonov regularization. The downside is that the image quality is still low, the resolution is low, the convergence rate is not high. In this paper, we study the DBIM method to create density images using compressive sampling technique. With compressive sampling technique, the probes will be randomly distributed on the measurement system (unlike the traditional method, the probes are evenly distributed on the measurement system). This approach uses the l1 regularization to restore images. The proposed method will give superior results in image recovery quality, spatial resolution. The limitation of this method is that the imaging time is longer than the one in the traditional method, but the less number of iterations is used in this method.

Mobile Ad-hoc NETwork (MANET) is a collection of mobile devices which interchange information without the use of predefined infrastructures or central administration. It is employed in many domains such as military and commercial sectors, data and sensors networks, low level applications, etc. The important constraints in this network are the limitation of bandwidth, processing capabilities and battery life. The choice of an effective routing protocol is primordial. From many routing protocols developed for MANET, OLSR protocol is a widely-used proactive routing protocol which diffuses topological information periodically. Thus, every node has a global vision of the entire network. The protocol assumes, like the other protocols, that the nodes cooperate in a trusted environment. So, all control messages are transmitted (HELLO messages) to all 1-hop neighbor nodes or broadcasted (TC and MID messages) to the entire network in clear. However, a node, which listens to OLSR control messages, can exploit this property to lead an attack. In this paper, we investigate on MultiPoint Relay (MPR) attack considered like one of the efficient OLSR attacks by using a simulation in progressive size gridMANET.

As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.

Threat intelligence sharing is posited as an important aid to help counter cybersecurity attacks and a number of threat intelligence sharing communities exist. There is a general consensus that many challenges remain to be overcome to achieve fully effective sharing, including concerns about privacy, negative publicity, policy/legal issues and expense of sharing, amongst others. One recent trend undertaken to address this is the use of decentralized blockchain based sharing architectures. However while these platforms can help increase sharing effectiveness they do not fully address all of the above challenges. In particular, issues around trust are not satisfactorily solved by current approaches. In this paper, we describe a novel trust enhancement framework -TITAN- for decentralized sharing based on the use of P2P reputation systems to address open trust issues. Our design uses blockchain and Trusted Execution Environment technologies to ensure security, integrity and privacy in the operation of the threat intelligence sharing reputation system.

Single sign-on (SSO) is becoming more and more popular in the Internet. An SSO ticket issued by the identity provider (IdP) allows an entity to sign onto a relying party (RP) on behalf of the account enclosed in the ticket. To ensure its authenticity, an SSO ticket is digitally signed by the IdP and verified by the RP. However, recent security incidents indicate that a signing system (e.g., certification authority) might be compromised to sign fraudulent messages, even when it is well protected in accredited commercial systems. Compared with certification authorities, the online signing components of IdPs are even more exposed to adversaries and thus more vulnerable to such threats in practice. This paper proposes ticket transparency to provide accountable SSO services with privacy-preserving public logs against potentially fraudulent tickets issued by a compromised IdP. With this scheme, an IdP-signed ticket is accepted by the RP only if it is recorded in the public logs. It enables a user to check all his tickets in the public logs and detect any fraudulent ticket issued without his participation or authorization. We integrate blind signatures, identity-based encryption and Bloom filters in the design, to balance transparency, privacy and efficiency in these security-enhanced SSO services. To the best of our knowledge, this is the first attempt to solve the security problems caused by potentially intruded or compromised IdPs in the SSO services.

In a research community, the provenance sharing of scientific workflows can enhance distributed research cooperation, experiment reproducibility verification and experiment repeatedly doing. Considering that scientists in such a community are often in a loose relation and distributed geographically, traditional centralized provenance sharing architectures have shown their disadvantages in poor trustworthiness, reliabilities and efficiency. Additionally, they are also difficult to protect the rights and interests of data providers. All these have been largely hindering the willings of distributed scientists to share their workflow provenance. Considering the big advantages of blockchain in decentralization, trustworthiness and high reliability, an approach to sharing scientific workflow provenance based on blockchain in a research community is proposed. To make the approach more practical, provenance is handled on-chain and original data is delivered off-chain. A kind of block structure to support efficient provenance storing and retrieving is designed, and an algorithm for scientists to search workflow segments from provenance as well as an algorithm for experiments backtracking are provided to enhance the experiment result sharing, save computing resource and time cost by avoiding repeated experiments as far as possible. Analyses show that the approach is efficient and effective.

pubcrawl, Network on Chip Security, Scalability, resiliency, resilience, metrics, Tasks on NoC (Network-on-Chip) are less efficient because of long-distance data synchronization. An inefficient task schedule strategy can lead to a large number of remote data accessing that ruins the speedup of parallel execution of multiple tasks. Thus, we propose an energy efficient task schedule to reduce task traffic with a diffusional pattern. The task mapping algorithm can optimize traffic distribution by limit tasks into a small area to reduce NoC activities. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.

We present a demo of DryVR 2.0, a framework for verification and controller synthesis of cyber-physical systems composed of black-box simulators and white-box automata. For verification, DryVR 2.0 takes as input a black-box simulator, a white-box transition graph, a time bound and a safety specification. As output it generates over-approximations of the reachable states and returns "Safe" if the system meets the given bounded safety specification, or it returns "Unsafe" with a counter-example. For controller synthesis, DryVR 2.0 takes as input black-box simulator(s) and a reach-avoid specification, and uses RRTs to find a transition graph such that the combined system satisfies the given specification.

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.

The Time-Slotted Channel Hopping (TSCH) mode, defined by the IEEE 802.15.4e protocol, aims to reduce the effects of narrowband interference and multipath fading on some channels through the frequency hopping method. To work satisfactorily, this method must be based on the evaluation of the channel quality through which the packets will be transmitted to avoid packet losses. In addition to the estimation, it is necessary to manage channel blacklists, which prevents the sensors from hopping to bad quality channels. The blacklists can be applied locally or globally, and this paper evaluates the use of a local blacklist through simulation of a TSCH network in a simulated harsh industrial environment. This work evaluates two approaches, and both use a developed protocol based on TSCH, called Adaptive Blacklist TSCH (AB-TSCH), that considers beacon packets and includes a link quality estimation with blacklists. The first approach uses the protocol to compare a simple version of TSCH to configurations with different sizes of blacklists in star topology. In this approach, it is possible to analyze the channel adaption method that occurs when the blacklist has 15 channels. The second approach uses the protocol to evaluate blacklists in tree topology, and discusses the inherent problems of this topology. The results show that, when the estimation is performed continuously, a larger blacklist leads to an increase of performance in star topology. In tree topology, due to the simultaneous transmissions among some nodes, the use of smaller blacklist showed better performance.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

Along with the continuous progress of the information technology, Internet of Things is the inevitable way for realizing the fusion of communication and traditional network technology. Network coding, an important breakthrough in the field of communication, has many applied advantages in information network. This article analyses the eavesdropping problem of Internet of Things and presents an information secure network coding scheme against the eavesdropping adversaries. We show that, if the number of links the adversaries can eavesdrop on is less than the max-flow of a network, the proposed coding scheme not only `achieves the prefect information secure condition but also the max-flow of the network.

This paper proposed an advanced round modification fault analysis (RMFA) at the theoretical level on AEGIS-128, which is one of seven finalists in CAESAR competition. First, we clarify our assumptions and simplifications on the attack model, focusing on the encryption security. Then, we emphasize the difficulty of applying vanilla RMFA to AEGIS-128 in the practical case. Finally we demonstrate our advanced fault analysis on AEGIS-128 using machine-solver based algebraic techniques. Our enhancement can be used to conquer the practical scenario which is difficult for vanilla RMFA. Simulation results show that when the fault is injected to the initialization phase and the number of rounds is reduced to one, two samples of injections can extract the whole 128 key bits within less than two hours. This work can also be extended to other versions such as AEGIS-256.

CFRS (Collaborative Filtering Recommendation System) is one of the most widely used individualized recommendation systems. However, CFRS is susceptible to shilling attacks based on profile injection. The current research on shilling attack mainly focuses on the recognition of false user profiles, but these methods depend on the specific attack models and the computational cost is huge. From the view of item, some abnormal item detection methods are proposed which are independent of attack models and overcome the defects of user profiles model, but its detection rate, false alarm rate and time overhead need to be further improved. In order to solve these problems, it proposes an abnormal item detection method based on time window merging. This method first uses the small window to partition rating time series, and determine whether the window is suspicious in terms of the number of abnormal ratings within it. Then, the suspicious small windows are merged to form suspicious intervals. We use the rating distribution characteristics RAR (Ratio of Abnormal Rating), ATIAR (Average Time Interval of Abnormal Rating), DAR(Deviation of Abnormal Rating) and DTIAR (Deviation of Time Interval of Abnormal Rating) in the suspicious intervals to determine whether the item is subject to attacks. Experiment results on the MovieLens 100K data set show that the method has a high detection rate and a low false alarm rate.

At present, with the increase of automated attack tools and the development of the underground industrial chain brought by network attack, even well-managed network is vulnerable to complex multi-step network attack, which combines multiple network vulnerabilities and uses the causal relationship between them to achieve the attack target. The detection of such attack intention is very difficult. Therefore, in order to solve the problem that the real attack intention of the attackers in complex network is difficult to be recognized, this paper proposes to assume the possible targets in the network according to the important asset information in the network. By constructing the hierarchical attack path graph, the probability of each hypothetical attack intention target is calculated, and the real attack intention and the most likely attack path of the attacker are deduced. The hierarchical attack path graph we use can effectively overcome the cognitive difficulties caused by network complexity and large scale, and can quantitatively and qualitatively analyze the network status. It is of great importance to make the protection and strategy of network security.

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We develop novel scoring strategies to find the most important words to modify such that the deep classifier makes a wrong prediction. Simple character-level transformations are applied to the highest-ranked words in order to minimize the edit distance of the perturbation. We evaluated DeepWordBug on two real-world text datasets: Enron spam emails and IMDB movie reviews. Our experimental results indicate that DeepWordBug can reduce the classification accuracy from 99% to 40% on Enron and from 87% to 26% on IMDB. Our results strongly demonstrate that the generated adversarial sequences from a deep-learning model can similarly evade other deep models.

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN) processors, and make it hard to be scaled down, as well as feasibility degrading. This paper first analyses the impact of PV on the performance of ANN chips. Then proposes an in-situ transfer learning method at system level to reduce PV's influence with low-precision back-propagation. Simulation results show the proposed method could increase 50% tolerance of operating point drift and 70% $\sim$ 100% tolerance of mismatch with less than 1% accuracy loss of benchmarks. It also reduces 66.7% memories and has about 50× energy-efficiency improvement of multiplication in the learning stage, compared with the conventional full-precision (32bit float) training system.

Internet technology has changed how people work, live, communicate, learn and entertain. The internet adoption is rising rapidly, thus creating a new industrial revolution named "Industry 4.0". Industry 4.0 is the use of automation and data transfer in manufacturing technologies. It fosters several technological concepts, one of these is the Internet of Things (IoT). IoT technology is based on a big network of machines, objects, or people called "things" interacting together to achieve a common goal. These things are continuously generating vast amounts of data. Data understanding, processing, securing and storing are significant challenges in the IoT technology which restricts its development. This paper presents a new reference IoT model for future smart IoT solutions called Cloud Web of Things (CloudWoT). CloudWoT aims to overcome these limitations by combining IoT with edge computing, semantic web, and cloud computing. Additionally, this work is concerned with the security issues which threatens data in IoT application domains.

This paper presents DeDoS, a novel platform for mitigating asymmetric DoS attacks. These attacks are particularly challenging since even attackers with limited resources can exhaust the resources of well-provisioned servers. DeDoS offers a framework to deploy code in a highly modular fashion. If part of the application stack is experiencing a DoS attack, DeDoS can massively replicate only the affected component, potentially across many machines. This allows scaling of the impacted resource separately from the rest of the application stack, so that resources can be precisely added where needed to combat the attack. Our evaluation results show that DeDoS incurs reasonable overheads in normal operations, and that it significantly outperforms standard replication techniques when defending against a range of asymmetric attacks.

Malicious software or malware is one of the most significant dangers facing the Internet today. In the fight against malware, users depend on anti-malware and anti-virus products to proactively detect threats before damage is done. Those products rely on static signatures obtained through malware analysis. Unfortunately, malware authors are always one step ahead in avoiding detection. This research deals with dynamic malware analysis, which emphasizes on: how the malware will behave after execution, what changes to the operating system, registry and network communication take place. Dynamic analysis opens up the doors for automatic generation of anomaly and active signatures based on the new malware's behavior. The research includes a design of honeypot to capture new malware and a complete dynamic analysis laboratory setting. We propose a standard analysis methodology by preparing the analysis tools, then running the malicious samples in a controlled environment to investigate their behavior. We analyze 173 recent Phishing emails and 45 SPIM messages in search for potentially new malwares, we present two malware samples and their comprehensive dynamic analysis.

Global Platform (GP)1 specifications accepted as de facto industry standards are widely used for the development of embedded operating system running on secure chip devices. A promising approach to demonstrating the implementation of an OS meets its specification is formal verification. However, most previous work on operating system verification targets high-level source programs proving the correspondence between abstract specification and high-level implementation but ignoring the machine-code level implementation parts. Thus, this kind of correspondence proofs stay in a shallow level. In this paper, we present a novel methodology for formal specifying and certifying the implementation of an embedded operating system strictly follows the GP specification. We establish a multiple abstraction layers framework that has four layers, from up to down, which are Formal Global Platform Layer (FGPL), Formal Specification High Layer (FSHL), Formal Specification Low Layer (FSLL) and Formal Assembly Machine Layer (FAML). To demonstrate the effectiveness of our methodology, we take the communication module of our Trust-E operating system (running on an extended CompCert ARM assembly machine model) as a case study and have successfully constructed a multi-layered proof, fully formalized in the Coq proof assistant. Some parts of the module are written in C and some are written in assembly; we certify that all codes implementation follow Global Platform specification.

Since Gatys et al. proved that the convolution neural network (CNN) can be used to generate new images with artistic styles by separating and recombining the styles and contents of images. Neural Style Transfer has attracted wide attention of computer vision researchers. This paper aims to provide an overview of the style transfer application deep learning network development process, and introduces the classical style migration model, on the basis of the research on the migration of style of the deep learning network for collecting and organizing, and put forward related to gathered during the investigation of the problem solution, finally some classical model in the image style to display and compare the results of migration.

Ubiquitous Healthcare System (U-Healthcare) is a well-known application of wireless sensor networking (WSN). In this system, the sensors take less power for operating the function. As the data transfers between sensor and other stations is sensitive so there needs to provide a security scheme. Due to the low life of sensor nodes in Wireless Sensor Networks (WSN), asymmetric key based security (AKS) architecture is always considered as unsuitable for these types of networks. Several papers have been published in recent past years regarding how to incorporate AKS in WSN, Haque et al's Asymmetric key based Architecture (AKA) is one of them. But later it is found that this system has authentication problem and therefore prone to man-in-the-middle (MITM) attack, furthermore it is not a truly asymmetric based scheme. We address these issues in this paper and proposed a complete asymmetric approach using PEKS-PM (proposed by Pham in [8]) to remove impersonation attack. We also found some other vulnerabilities in the original AKA system and proposed solutions, therefore making it a better and enhanced asymmetric key based architecture.

The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy's expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.