Summary The Social Internet of Things (SIoT) allows for more efficient and intelligent communication between entities (humans and objects). Managing the digital identity of objects, protecting the owner's privacy, and resisting impersonation attacks are crucial challenges in SIoT. On the other hand, the classic centralized identity provider servers contain valuable information for hackers and malicious parties. Effective solutions can be presented using decentralized technologies such as blockchains. This paper proposes a decentralized identity management system for SIoT based on smart contract technology. This system drives object identity control; conversely, a zero‐knowledge‐based protocol and double‐spend prevention ideas are proposed for privacy preservation and Sybil attack resistance, respectively. The central idea is to implement self‐sovereign identity (SSI) so that even certifiers are not privy to object‐sensitive information. Additionally, it attempts to prevent the owner from creating Sybil objects using the web of trust method. The method's scalability and convergence can be proven by considering all acknowledgment nodes and different paths. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Abstract Sybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost‐benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Social bots are intelligent programs that control the behavior of fake accounts in an online social network(OSN). They pass themselves off as human accounts and manipulate the health of the ecosystem of OSNs. Thus, it is crucial to distinguish social bots from human accounts. Despite recent advances in social bot detection, state-of-the-art methods face challenges in scalability, generalization, and robustness. Considering these drawbacks, in this article, a new category of features, called friendship preference, is proposed. Friendship preference features are extracted from the profile attributes of the followers. The proposed feature extraction formula is designed to be scalable as the number of followers grows. Two classifiers are developed to evaluate the proposed category of features in terms of efficiency and usefulness. The classifiers are benchmarked on several real datasets. The experimental results indicate that the classifiers outperform Botometer in terms of classification performance. Scalability is assessed by inspecting the detection performance of classifiers when the attributes of a limited number of the accounts in the neighborhood of the users with large egonets are available. Furthermore, generalization is validated by crossover validation on various datasets. Finally, robustness and the ability for early detection of social bots are discussed.

Socialbots are intelligent software that controls all behaviour of fake accounts in an online social network. Since they are armed with detection evasion techniques, it is valuable to be able to determine the effectiveness of these techniques. In this study, an analytical model is developed to estimate a lower bound for the cost of automatic establishment of a socialbot network. Moreover, by considering fake accounts purchasing as an establishment strategy, an upper bound is suggested for acceptable costs. These two boundaries are compared to decide on the economic feasibility of a socialbot network design strategy. To demonstrate the practicality and effectiveness of the model, two case studies are investigated. They show that although designing a fully stealthy socialbot network is economically feasible, the infiltration time would be unacceptable. Thus, this ideal situation in which the establishment is fully stealthy, performs in a tolerable time, and satisfactory infiltration scale, is impractical. A possible solution could be achieved by reducing the time and cost in exchange for less stealthy behaviour while the infiltration scale kept unchanged. Since the model presents a trade‐off between stealthiness, time, and cost, it is a useful tool facilitating the design of a possible strategy.

Abstract The tremendous numbers of alerts provided by intrusion detection systems have made the alert correlation a vital issue. Despite of the considerable number of proposed methods, the online alert correlation is still an open issue. In this paper we proposed an online model for alert correlation. Our model consists of two modules: (1) the online fuzzy clustering module which clusters alerts into fuzzy events based on their similarity and historical relevance; (2) the fuzzy inter event pattern mining which provides the first module with the historical relevance of alerts by mining frequent fuzzy patterns among them. Using these two modules, our approach is as fast as similarity based approaches suitable for online alert correlation while it is able to extract complex attack scenarios like offline time consuming data mining based approaches. Furthermore, observing the frequent events makes our approach capable of detecting scenarios including wrapping tricks which tries to fake the source or destination IPs. The experimental results with the well‐known dataset DARPA2000 and the ISCX UNB intrusion detection evaluation dataset proved mentioned claims. Copyright © 2016 John Wiley & Sons, Ltd.

Nowadays malware writers usually employ several obfuscation techniques to evade detection. The number of variants detected each day has been increasing significantly. Unfortunately traditional detection approaches such as signature scanning are becoming inefficient to detect such malwares. Researches show that these obfuscations make some anomalies in Portable Executable files. In this paper, by focusing on important static heuristic features and fuzzy classification algorithms, we tried to detect malwares and packed files. In addition, we used preprocessing to evade anomaly exceptions in benign files that improved our detection results. The experimental results, using over 63000 file samples, indicate that the proposed detector achieves high detection results with low false positive and false negative rates. Furthermore, our experimental results on new malware samples that had been undetectable for many years by antivirus products and new custom packers, show that our system works well with new and unknown samples too. Copyright © 2015 John Wiley & Sons, Ltd.

Using the new wireless capabilities in vehicles, various applications have appeared in Vehicular Ad Hoc Networks (VANETs). Multimedia data sharing is one of such applications, which has recently been given so much attention. Most presented schemes in this field are focused on direct opportunistic meetings between vehicles or request/response broadcast in the whole network, which are inefficient and un-scalable. In this paper, we propose SEFF (a Scalable and EFficient distributed File sharing technique) that increases efficiency and scalability via taking advantages of BitTorrent protocol and consistent hashing technique. The results of simulations show that our approach increases the received file ratio, decreases total download time of files, and consequently can enhance efficiency and performance.

There are many approaches proposed to provide anonymous peer-to-peer communications. Data sent via peer-to-peer communications is vulnerable to traffic analysis. Traffic analysis is the process of intercepting and analysing messages in order to compromise information from patterns in communications. An intruder can get information about the content of the data, the requester's and provider's identities. Anonymous approaches are designed with the following three goals: to protect the identity of provider, to protect the identity of requester and to protect the contents of transferred data between them. This article presents a new peer-to-peer approach to achieve anonymity between a requester and a provider in a hybrid peer-to-peer information-sharing environment with trusted servers called supper node so that the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. This article shows that the proposed approaches improved reliability and has more security. This approach, based on onion routing and randomization, protects transferring data against traffic analysis attack. The ultimate goal of this anonymous communications approach is to allow a requester to communicate with a provider in such a manner that nobody can determine the requester's identity and the content of transferred data.

As the complexity of the integrated circuits increases, they become more susceptible to manufacturing faults, decreasing the total process yield. Thus, it would be desirable to develop techniques for reusing faulty dies, even with a degraded performance. In this paper, a new method for high level synthesis of degradable ASICs is presented. Our technique introduces the concept of Virtual Binding. In this approach, the operations are bound to virtual components that are linked with actual non-faulty components using a set of configuration multiplexers and flip-flops embedded in the data-path. Using virtual components simplifies the synthesis algorithm and decreases the size of generated control unit. Virtual-to-physical mapping of the components will be established by programming the configuration flip-flops after diagnosing the faulty components. The experimental results show that the area and delay overhead of the resulting circuits have acceptable values compared to the original, non-degradable circuits.

Since software is playing an increasingly important role in system-on-chip, retargetable compilation has been an active research area in the last few years. However, the retargetting of equally important downstream system tools, such as assemblers, linkers and debuggers, has either been ignored, or falls short of meeting the requirements of modern programming languages and operating systems. In this paper, we present techniques that can automatically retarget the GNU binutils tool kit, which contains a large array of production-quality downstream tools. Other than having all the advantages enjoyed by open-source software by aligning to a de facto standard, our techniques are systematic, as a result of using a formal model of instruction set architecture (ISA) and application binary interface (ABI); and simple, as a result of leveraging free software to the largest extent.