Malicious Server Research Articles

Secure Messaging Authentication Ceremonies Are Broken

People who use secure messaging apps are vulnerable to a hacked or malicious server unless they manually complete an authentication ceremony. In this article, we describe the usability challenges of the authentication ceremony and research to improve it. We conclude with recommendations for service providers and directions for research.

Secure and verifiable iris authentication system using fully homomorphic encryption

With the escalated usage of a biometric authentication system (BAS), template protection for biometrics attracted research interest in recent years. The assumption behind the existing homomorphic encryption-based BASs is that the server performs the computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources, which may result in false accept/reject. To tackle this challenge, we propose a secure and verifiable classification based iris authentication system (SvaS). SvaS aims to achieve both privacy-preserving (PP) training and PP classification of Nearest Neighbor and Multi-class Perceptron models. The Fan-vercauteren scheme provides confidentiality for the iris templates, and aggregate verification vector helps to verify the correctness of the computed classification result. Extensive experimental results on benchmark iris databases demonstrate that SvaS provides privacy to the iris templates with no loss in accuracy and eliminates the need to trust the server.

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.

Delegating a Product of Group Exponentiations with Application to Signature Schemes (Submission to Special NutMiC 2019 Issue of JMC)

AbstractMany public-key cryptosystems and, more generally, cryptographic protocols, use group exponentiations as important primitive operations. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that a computationally weaker client (i.e., capable of performing a relatively small number of modular multiplications) delegates such primitive operations to a computationally stronger server. Important requirements for such delegation protocols include privacy of the client’s input exponent and security of the client’s output, in the sense of detecting, except for very small probability, any malicious server’s attempt to convince the client of an incorrect exponentiation result. Only recently, efficient protocols for the delegation of a fixed-based exponentiation, over cyclic and RSA-type groups with certain properties, have been presented and proved to satisfy both requirements.In this paper we show that a product of many fixed-base exponentiations, over a cyclic groups with certain properties, can be privately and securely delegated by keeping the client’s online number of modular multiplications only slightly larger than in the delegation of a single exponentiation. We use this result to show the first delegations of entire cryptographic schemes: the well-known digital signature schemes by El-Gamal, Schnorr and Okamoto, over the q-order subgroup in ℤp, for p, q primes, as well as their variants based on elliptic curves. Previous efficient delegation results were limited to the delegation of single algorithms within cryptographic schemes.

DeMSF: a Method for Detecting Malicious Server Flocks for Same Campaign

Nowadays, cybercriminals tend to leverage dynamic malicious infrastructures with multiple servers to conduct attacks, such as malware distribution and control. Compared with a single server, employing multiple servers allows crimes to be more efficient and stealthy. As the necessary role infrastructures play, many approaches have been proposed to detect malicious servers. However, many existing methods typically target only on the individual server and therefore fail to reveal inter-server connections of an attack campaign.In this paper, we propose a complementary system, deMSF, to identify server flocks, which are formed by infrastructures involved in the same malicious campaign. Our solution first acquires server flocks by mining relations of servers from both spatial and temporal dimensions. Further we extract the semantic vectors of servers based on word2vec and build a textCNN-based flocks classifier to recognize malicious flocks. We evaluate deMSF with real-world traffic collected from an ISP network. The result shows that it has a high precision of 99% with 90% recall.

MD-MinerP: Interaction Profiling Bipartite Graph Mining for Malware-Control Domain Detection

Despite the efforts of information security experts, cybercrimes are still emerging at an alarming rate. Among the tools used by cybercriminals, malicious domains are indispensable and harm from the Internet has become a global problem. Malicious domains play an important role from SPAM and Cross-Site Scripting (XSS) threats to Botnet and Advanced Persistent Threat (APT) attacks at large scales. To ensure there is not a single point of failure or to prevent their detection and blocking, malware authors have employed domain generation algorithms (DGAs) and domain-flux techniques to generate a large number of domain names for malicious servers. As a result, malicious servers are difficult to detect and remove. Furthermore, the clues of cybercrime are stored in network traffic logs, but analyzing long-term big network traffic data is a challenge. To adapt the technology of cybercrimes and automatically detect unknown malicious threats, we previously proposed a system called MD-Miner. To improve its efficiency and accuracy, we propose the MD-MinerP here, which generates more features with identification capabilities in the feature extraction stage. Moreover, MD-MinerP adapts interaction profiling bipartite graphs instead of annotated bipartite graphs. The experimental results show that MD-MinerP has better area under curve (AUC) results and found new malicious domains that could not be recognized by other threat intelligence systems. The MD-MinerP exhibits both scalability and applicability, which has been experimentally validated on actual enterprise network traffic.

Analyzing User-Level Privacy Attack Against Federated Learning

Federated learning has emerged as an advanced privacy-preserving learning technique for mobile edge computing, where the model is trained in a decentralized manner by the clients, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. To analyze the privacy leakage of federated learning, this paper gives the first attempt to explore user-level privacy leakage by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, called multi-task GAN - Auxiliary Identification (mGAN-AI), which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works interfering the federated learning process, the proposed method works “invisibly” on the server side. Furthermore, considering the anonymization strategy for mitigating mGAN-AI, we propose a beforehand linkability attack which re-identifies the anonymized updates by associating the client representatives. A novel siamese network fusing the identification and verification models is developed for measuring the similarity of representatives. The experimental results demonstrate the effectiveness of the proposed approaches and the superior to the state-of-the-art.

Personalised rating

We introduce personalised rating, a network-based rating system where individuals, connected in a social network, decide whether or not to consume a service (e.g., a restaurant) based on the evaluations provided by their peers. We compare personalised rating with the more widely used objective rating where, instead, customers receive an aggregate evaluation of what everybody else has declared so far. We focus on the manipulability of such systems, allowing a malicious service provider (e.g., the restaurant owner) to transfer monetary incentive to the individuals in order to manipulate their rating and increase the overall profit. We study manipulation under various constraints, such as the proportion of individuals who evaluate the service and, in particular, how much the attacker knows of the underlying customers’ network, showing the conditions under which the system is bribery-proof, i.e., no manipulation strategy yields a strictly positive expected gain to the service provider. We also look at manipulation strategies that are feasible in theory but might, in general, be infeasible in practice, deriving a number of algorithmic properties of manipulation under personalised rating. In particular we show that establishing the existence of a rewarding manipulation strategy for the attacker—and, notably, an optimal one—is NP-complete, even with full knowledge of the underlying network structure.

Verifiable single-server private information retrieval from LWE with binary errors

Private Information Retrieval (PIR) allows a client to privately retrieve some data from a public database. There exist two types of PIR: (computational) Single-server PIR (SPIR) and (information-theoretic) multi-server PIR. In this paper, we focus on exploring SPIR. We first propose a simple and efficient additively-homomorphic encryption scheme of which privacy is based on the learning with binary errors assumption that is known as an interesting candidate for practical lattice-based cryptography. Then, according to our proposed homomorphic encryption scheme, we give a Verifiable (single/multi-bit) SPIR (VSPIR) scheme for the single-query case under the malicious server model. To the best of our knowledge, our proposal is the first practical non-interactive VSPIR scheme employing an efficient probabilistic proof that can discover the forged result with overwhelming probability. The corresponding communication complexity and computational complexity are comparable with those of some typical SPIR schemes. Moreover, we extend our single-query VSPIR scheme to construct a non-interactive multi-query solution. In particular, the corresponding communication complexity and computational complexity are the same as those of the single-query scheme. Finally, we provide detailed implementation results to confirm efficiency of our proposals.

Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes' theorem, and the indirect trust is derived from the Dempster-Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.

A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing

A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain names over time. Several methods have been suggested to detect fast-flux domains. However, these methods achieve low detection accuracy, especially for zero-day domains. They also entail a significantly long detection time and consume high memory storage. In this survey, we present an overview of the various techniques used to detect fast-flux domains according to solution scopes, namely, host-based, router-based, DNS-based, and cloud computing techniques. This survey provides an understanding of the problem, its current solution space, and the future research directions expected.

Comment on “Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things”

Internet-of-Things (IoT) devices have grown in popularity over the past few years. The RSA public-key cryptographic primitive is time consuming for resource-constrained IoT. Recently, Zhang et al. proposed a two-party outsourcing protocol between a client and a server for RSA decryption in IoT. It relies on the Chinese remainder theorem as proposed by Quisquater and Couvreur in 1982 and is very efficient. We show that their protocol does not achieve the claimed security guarantees: 1) the (secret) decryption exponent, the plaintext, and the factorization of the RSA modulus are revealed to a passive adversary and 2) a malicious server can make the client accept an (invalid) value of its choice as the result of the delegated computation.

Adaptively Secure Non-interactive CCA-Secure Threshold Cryptosystems: Generic Framework and Constructions

In threshold cryptography, private keys are divided into n shares, each one of which is given to a different server in order to avoid single points of failure. In the case of threshold public-key encryption, at least t out of n servers need to contribute to the decryption process. A threshold primitive is said robust if no coalition of t malicious servers can prevent remaining honest servers from successfully completing private key operations. Non-interactive schemes, considered the most practical ones, allow servers to contribute to decryption without interactions. So far, most non-interactive threshold cryptosystems were only proved secure against static corruptions. In the adaptive corruption scenario (where the adversary can corrupt servers at any time, based on its complete view), all existing robust threshold encryption schemes that also resist chosen-ciphertext attacks (CCA) till recently require interaction in the decryption phase. A very specific method (in composite order groups) for getting rid of interaction was recently suggested, leaving the question of more generic frameworks and constructions with better security and, in particular, better exibility (i.e., compatibility with distributed key generation). This paper advances the state of the art and describes a general construction of adaptively secure robust non-interactive threshold cryptosystems with chosen-ciphertext security. We define the novel notion of all-but-one perfectly sound threshold hash proof systems that can be seen as (threshold) hash proof systems with publicly verifiable and simulation-sound proofs. We show that this notion generically implies threshold cryptosystems combining the aforementioned properties. Then, we provide efficient instantiations under well-studied assumptions in bilinear groups (e.g., in such groups of prime order). These instantiations have a tighter security proof in the single-challenge setting and are indeed compatible with distributed key generation protocols.

A verifiable ranked ciphertext retrieval scheme based on bilinear mapping

SummaryIt is prevalent nowadays for data owners to outsource their data to the cloud. Most of the traditional searchable schemes are proposed under the honest and curious model, lacking verification of integrity and correctness of the retrieval results. Because the cloud server is not completely trustworthy and there may be improper execution of the users' retrieval requests, it is necessary to verify the retrieval results returned by the cloud server. In order to address these issues, we propose a verifiable ranked ciphertext retrieval scheme (VRCRS) based on bilinear mapping. For the purpose of ranking the keywords and improving the security of ciphertext retrieval scheme, we utilize the lucene search engine toolkit to score keywords, and improve the traditional inverted index structure for building a secure inverted index structure. By adding dummy words into the dictionary of keywords, the proposed scheme can resist the keywords statistics attack of malicious server. To ensure the correctness and integrity of returned ciphertext, we apply bilinear mapping to generate validation tags for keywords. The theoretical analysis and experimental results show that the proposed scheme is secure and efficient. VRCRS can actually identify the illegal behavior of cloud server such as tampering and forgery.

REST: A Thread Embedding Approach for Identifying and Classifying User-Specified Information in Security Forums

How can we extract useful information from a security forum? We focus on identifying threads of interest to a security professional: (a) alerts of worrisome events, such as attacks, (b) offering of malicious services and products, (c) hacking information to perform malicious acts, and (d) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Novel approaches are needed to address the challenges in this domain: (a) the difficulty in specifying the “topics” of interest efficiently, and (b) the unstructured and informal nature of the text. We propose, REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above. The key novelty of the work is a multi-step weighted embedding approach: we project words, threads and classes in appropriate embedding spaces and establish relevance and similarity there. We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads. First, REST robustness to initial keyword selection can extend the user-provided keyword set and thus, it can recover from missing keywords. Second, REST categorizes the threads into the classes of interest with superior accuracy compared to five other methods: REST exhibits an accuracy between 63.3-76.9%. We see our approach as a first step for harnessing the wealth of information of online forums in a user-friendly way, since the user can loosely specify her keywords of interest.

Privacy-Preserving Payment Splitting

AbstractWidely used payment splitting apps allow members of a group to keep track of debts between members by sending charges for expenses paid by one member on behalf of others. While offering a great deal of convenience, these apps gain access to sensitive data on users’ financial transactions. In this paper, we present a payment splitting app that hides all transaction data within a group from the service provider, provides privacy protections between users in a group, and provides integrity against malicious users or even a malicious server.The core protocol proceeds in a series of rounds in which users either submit real data or cover traffic, and the server blindly updates balances, informs users of charges, and computes integrity checks on user-submitted data. Our protocol requires no cryptographic operations on the server, and after a group’s initial setup, the only cryptographic tool users need is AES.We implement the payment splitting protocol as an Android app and the accompanying server. We find that, for realistic group sizes, it requires fewer than 50 milliseconds per round of computation on a user’s phone and the server requires fewer than 300microseconds per round for each group, meaning that our protocol enjoys excellent performance and scalability properties.

Multi-instance iris remote authentication using private multi-class perceptron on malicious cloud server

In recent years, biometric authentication system (BAS) has become the most promising and popular authentication system in identity management. Due to its capability to solve the limitations of unimodal systems, multi-biometric systems (MBS) have been extensively accepted in various fields. The main step in MBS is information fusion. On the other hand, directly storing the fused templates into a centralized server leads to privacy concerns. Recently, many BAS based on homomorphic encryption has been introduced to provide confidentiality for the fused templates. However, most of the existing solutions rely on an implication of the assumption that the server is “Honest-but-Curious”. As a result, the compromise of such server results into entire system vulnerability. To address this, we propose a novel P rivacy P reserving (PP) multi-instance iris remote authentication system to accord with attacks at the malicious server and over the transmission channel. Our scheme uses F ully H omomorphic E ncryption (FHE) to achieve the confidentiality of the fused iris templates and polynomial factorization algorithm to achieve the integrity of the matching result. We propose a PP iris authentication system using P rivate M ulti-C lass P erceptron (PMCP) by using the properties of FHE. Moreover, we propose C ontradistinguish S imilarity A nalysis (CSA), a feature level fusion technique that minimizes the between-class correlations and maximizes the pair-wise correlations. Our method has experimented on IITD and CASIA-V3-Interval iris databases to check the effectiveness and robustness. Experimental results show that our method provides improved accuracy, and eliminates the need to trust the cloud server when compared to the state-of-the-art approaches.

Efficient and Secure Delegation of Exponentiation in General Groups to a Single Malicious Server

Group exponentiation is an important and relatively expensive operation used in many public-key cryptosystems and, more generally, cryptographic protocols. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that this operation is delegated from a computationally weaker client to a computationally stronger server. In the case of a single, possibly malicious, server, this problem has remained open since the introduction of a formal model. In previous work we have proposed practical and secure solutions applicable to two classes of specific groups, related to well-known cryptosystems. In this paper, we investigate this problem in a general class of multiplicative groups, possibly going beyond groups currently subject to quantum cryptanalysis attacks. Our main results are efficient delegation protocols for exponentiation in these general groups. The main technique in our results is a reduction of the protocol’s security probability (i.e., the probability that a malicious server convinces a client of an incorrect exponentiation output) that is more efficient than by standard parallel repetition. The resulting protocols satisfy natural requirements such as correctness, security, privacy and efficiency, even if the adversary uses the full power of quantum computers. In particular, in our protocols the client performs a number of online group multiplications smaller by 1–2 orders of magnitude than in a non-delegated computation.

CROWDBC A Blockchain Base Decentralized Framework for Crowdsourcing

Due to lack of server reliability and user data privacy encryption of data is required before the cloud is outsourced. We have found a compromised method within the blockchain in order to perform a keyword search which is secured on data that is encrypted against malicious service providers and users in cloud. SSE: Privately offers the cloud storage used in symmetric search encryption (SSE) systems, which cannot be regarded as a true cloud. The cloud service is also known to be credible. Let us start by emphasizing the importance of data storage within a public chain. The client is allowed by system to upload them in form which is encrypted, data content is distributed to the nodes of cloud and make sure that the data is available through encryption techniques. Presentation of a blockchain based system for providing the keyword search service with secure storage of distributed data. TKSE performs verifiability on server side so that true cloud servers are protected from being posed by owners of malicious data in the data storage process. Furthermore, technology of blockchain and hash functions are used to allow payment which is fair without third parties involvement for research fees, although if cloud or user is harmful. Our review of security and evaluation of performance show that TKSE is efficient and safe and be suited for cloud computing.

PrivLBS: Preserving Privacy in Location-Based Services

Location-based services have been increasingly integrated into people’s daily activities. However, some of these services may not be trustworthy and lead to serious privacy breaches. While spatial transformation techniques such as location perturbation or generalization have been studied extensively, many of them only consider the locationat single timestamps without considering temporal correlations among the locations of a moving user, leaving the user’s location with no guarantees of privacy protection against attacks that would exploit this vulnerability. This work proposes a new technique for preserving data privacy, named PrivLBS, which ensures that the individual’s location will not be easily re-identified by malicious services. Extensive simulation experiments have been carried out to evaluate the efficiency of PrivLBS. Experimental results show that PrivLBS reaches higher protection compared to other related approaches over different kinds of attacks.