The ISC International Journal of Information Security

Abstract The Fuzzy Vault scheme is an encryption scheme, which can tolerate errors in the keys. This leads to the possibility of enhancing the security in environments where these errors can be common, such as biometrics storage systems. Although several researchers have provided implementations, we find that the scheme is vulnerable to attacks when not properly used. This paper describes an attack on the Fuzzy Vault scheme where the attacker is assumed to have access to multiple vaults locked by the same key and where a non-maximal vault size is used. The attack effectively reduces the vault size by identifying and removing cha_ points. As the vault size decreases, the rate at which cha_ points are identified increases exponentially. Several possible defenses against the attack are also discussed.

Abstract A Virtual Organization (VO) consists of some real organizations with common interests, which aims to provide inter organizational associations to reach some common goals by sharing their resources with each other. Providing security mechanisms, and especially a suitable access control mechanism, which enforces the defined security policy is a necessary requirement in VOs. Since VO is a complex environment with the huge number of users and resources, traditional access control models cannot satisfy VOs security requirements. Most of the current proposals are basically based on the attributes of users and resources. In this paper, we suggest using a combination of the semantic based access control (SBAC) model, and the attribute based access control (ABAC) model with the shared ontology of subjects' attributes in VOs. In this model, each participating organization makes its access control decisions according to an enhanced model of the ABAC model. However, access decision in the VO is made in more abstract level through an enhanced model of the SBAC model. Using the ontology of users and resources in this model facilitates access control in large scale VOs with numerous organizations. By the combination of SBAC and ABAC, we attain their benefits and eliminate their shortcomings. In order to show the applicability of the proposed model, an access control system, based on the proposed model, has been implemented in Java using available APIs, including Sun's XACML API, Jena, Pellet, and Protégé.

Abstract Communication security of wireless sensor networks is achieved using cryptographic keys assigned to the nodes. Due to resource constraints in such networks, random key pre-distribution schemes are of high interest. Although in most of these schemes no location information is considered, there are scenarios that location information can be obtained by nodes after their deployment. In this paper, we propose a novel probabilistic key pre-distribution scheme, for large-scale wireless sensor networks which utilizes location information in order to improve the performance of random key pre-distribution substantially. In order to apply the location information of the nodes in key distribution process, we partition the network into some regions and use graph coloring techniques to efficiently assign the random keys. The proposed scheme has a superior scalability by supporting larger number of nodes and also increasing the probability of existence of a shared exclusive key among the nearby nodes, i.e., the probability of having an isolated node is significantly reduced in comparison with the existing random key pre-distribution schemes. Our simulation results verify these terms.

Abstract Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applications make these features imperfect for such tasks. As a remedy, network traffic classification using machine learning techniques is now evolving. In this article, a new semi-supervised learning is proposed which utilizes clustering algorithms and label propagation techniques. The clustering part is based on graph theory and minimum spanning tree (MST) algorithm. In the next level, some pivot data instances are selected for the expert to vote for their classes, and the identified class labels will be used for similar data instances with no labels. In the last part, the decision tree algorithm is used to construct the classification model. The results show that the proposed method has a precise and accurate performance in classification of encrypted traffic for the network applications. It also provides desirable results for plain un-encrypted traffic classification, especially for unbalanced streams of data.

Abstract A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate that in comparison to several traditional and new techniques, the proposed hybrid approach achieves better classification accuracies. The compared classification approaches are C4.5, Naïve Bayes, k-NN, SVM, Ripper, PNrule and MOGF-IDS. Moreover the improvement on classification accuracy has been obtained for most of the classes of the intrusion detection classification problem. In addition, the results indicate that the proposed hybrid system's total classification accuracy is 94.33% and its classification cost is 0.1675. Therefore, the resultant fuzzy classification rules can be used to produce a reliable intrusion detection system.

Abstract There are some critical privacy concerns in the current online social networks (OSNs). Users' information is disclosed to different entities that they were not supposed to access. Furthermore, the notion of friendship is inadequate in OSNs since the degree of social relationships between users dynamically changes over the time. Additionally, users may define similar privacy settings for their friends in an OSN. In this paper, we present a centralized privacy-preserving framework for OSNs to address these issues. Using the proposed approach, the users enforce confidentiality and access control on the shared data while their connections/relationships with other users are kept anonymous in OSNs. In this way, the users themselves create and modify personalized privacy settings for their shared data while employing each other's privacy settings. Detailed evaluations of the proposed framework show the advantages of the proposed architecture compared to the most analogous recent approach.

Abstract Linear diffusion layer is an important part of lightweight block ciphers and hash functions. This paper presents an efficient class of lightweight 4x4 MDS matrices such that the implementation cost of them and their corresponding inverses are equal. The main target of the paper is hardware oriented cryptographic primitives and the implementation cost is measured in terms of the required number of XORs. Firstly, we mathematically characterize the MDS property of a class of matrices (derived from the product of binary matrices and companion matrices of $\sigma$-LFSRs aka recursive diffusion layers) whose implementation cost is $10m+4$ XORs for 4 <= m <= 8, where $m$ is the bit length of inputs. Then, based on the mathematical investigation, we further extend the search space and propose new families of 4x 4 MDS matrices with 8m+4 and 8m+3 XOR implementation cost. The lightest MDS matrices by our new approach have the same implementation cost as the lightest existent matrix.

Abstract Privacy issues during data publishing is an increasing concern of involved entities. The problem is addressed in the field of statistical disclosure control with the aim of producing protected datasets that are also useful for interested end users such as government agencies and research communities. The problem of producing useful protected datasets is addressed in multiple computational privacy models such as $k$-anonymity in which data is clustered into groups of at least $k$ members. Microaggregation is a mechanism to realize $k$-anonymity. The objective is to assign records of a dataset to clusters and replace the original values with their associated cluster centers which are the average of assigned values to minimize information loss in terms of the sum of within group squared errors ($SSE$). While the problem is shown to be NP-hard in general, there is an optimal polynomial-time algorithm for univariate datasets. This paper shows that the assignment of the univariate microaggregation algorithm cannot produce optimal partitions for integer observations where the computed centroids have to be integer values. In other words, the integrality constraint on published quantities has to be addressed within the algorithm steps and the optimal partition cannot be attained using only the results of the general solution. Then, an effective method that considers the constraint is proposed and analyzed which can handle very large numerical volumes. Experimental evaluations confirm that the developed algorithm not only produces more useful datasets but also is more efficient in comparison with the general optimal univariate algorithm.

Abstract Since smartphones are usually personal devices full of private information, they are a popular target for a vast variety of real-world attacks such as Code Reuse Attack (CRA). CRAs enable attackers to execute any arbitrary algorithm on a device without injecting an executable code. Since the standard platform for mobile devices is ARM architecture, we concentrate on available ARM-based CRAs. Currently, three types of CRAs are proposed on ARM architecture including Return2ZP, ROP, and BLX-attack in accordance to three sub-models available on X86. Ret2Libc, ROP, and JOP. In this paper, we have considered some unique aspects of ARM architecture to provide a general model for code reuse attacks called Patulous Code Reuse Attack (PCRA). Our attack applies all available machine instructions that change Program Counter (PC) as well as direct or indirect branches in order to deploy the principles of CRA convention. We have demonstrated the effectiveness of our approach by defining five different sub-models of PCRA, explaining the algorithm of finding PCRA gadgets, introducing a useful set of gadgets, and providing a sample proof of concept exploit on Android 4.4 platform.

Abstract Mu et al. have proposed an electronic voting protocol and claimed that it protects anonymity of voters, detects double voting and authenticates eligible voters. It has been shown that it does not protect voter's privacy and prevent double voting. After that, several schemes have been presented to fulfill these properties. However, many of them suffer from the same weaknesses. In this paper, getting Asadpour et al.'s scheme as one of the latest ones and showing its weaknesses, we propose a new voting scheme which is immune to the weaknesses of previous schemes without losing efficiency. The scheme, is based on a special structure, which directly uses the identity of the voter, hides it in that structure and reveals it after double voting. We also, show that the security of this scheme depends on hardness of RSA cryptosystem, Discrete Logarithm problem and Representation problem.

Abstract  Nowadays the security of the design is so important because of the different available attacks to the system. the main aim of this paper is to improve the security of the circuit design implemented on FPGA device. Two approaches are proposed for this purpose. The first is to fill out empty space using flip-flops and LUTs so that there is no available space for inserting a hardware Trojan. We name this filling structure as Gate-chain. The second approach increases the security of the implemented design by identifying the low observable/controllable points of the main design and wiring them to the unused ports or the pre-designed Gate-chains. The proposed solutions not only prevent Trojan insertion but also increase the Trojan detection capabilities. Simulation results on Xilinx devices implementing different benchmarks show that the proposed method incurs dynamic power overhead just in test mode with less than one percent of delay overhead for critical path in normal mode.

Abstract Distributed Denial-of-Service (DDoS) attacks are among the most critical security threats to distributed network infrastructures, including blockchain systems. These attacks degrade performance, cause congestion, and disrupt service delivery or transaction processing. Traditional mitigation techniques have undergone extensive development. However, they often fail to intelligently detect and manage traffic patterns and struggle to adapt to dynamic conditions in decentralized environments. This paper proposes a reinforcement learning-based congestion control (CC) method that dynamically adjusts congestion window (CWND) following traditional TCP principles based on signals such as delay and packet loss. What distinguishes our approach is that the RL-agent interprets persistent or abnormal congestion patterns as potential indicators of adversarial high-load conditions (e.g., DDoS-induced congestion) and adapts CWND adjustments more intelligently to reduce their adverse. Leveraging the Q-learning algorithm, the proposed approach adapts dynamically to fluctuating traffic and conditions. Its learning capability enables continuous monitoring of behavior and timely responsiveness to anomalies, including sustained congestion patterns often associated with adversarial traffic surges. Simulation results across various DDoS scenarios—evaluated against conventional CC algorithms—demonstrate considerable improvements in key performance indicators such as reduced latency, enhanced bandwidth utilization, improved stability, decreased packet loss, and increased throughput. The proposed Q-learning-based CC operates at the peer-to-peer layer, regulating flow among blockchain nodes. It is independent of consensus mechanisms while indirectly improving consensus efficiency by reducing message delays and packet loss. This method offers a scalable and intelligent solution for cc under adversarial conditions, thereby contributing to improved robustness and efficiency in both general distributed systems and blockchain networks.

Abstract Key agreement protocols are essential for secure communications in open and distributed environments. Recently, identity-based key agreement protocols have been increasingly researched because of the simplicity of public key management. The basic idea behind an identity-based cryptosystem is that a public key is the identity (an arbitrary string) of a user, and the corresponding private key is generated by a trusted Private Key Generator (PKG). However, it is unrealistic to assume that a single PKG will be responsible for issuing private keys to members of different organizations or a large-scale nation. Hence, it is needed to consider multiple PKG environments with different system parameters. In this paper, we propose an identity-based key agreement protocol among users of different networks with independent PKGs, which makes use of elliptic curves. We prove the security of the proposed protocol in the random oracle model and show that all security attributes are satisfied. We also demonstrate a comparison between our protocol and some related protocols in terms of the communication costs and the execution time. The results show that the execution time of our protocol is less than 10%, and its communication costs are about 50% of the competitor protocols.

Abstract The main goal of Simon’s Algorithm is to find the period of periodic functions. However, if the target function does not satisfy Simon's promise completely or if the number of superposition queries of the adversary is limited, Simon's algorithm cannot compute the actual period, unambiguously. These problems may lead to the failure of period-finding-based (PFB) quantum attacks. We focus in this paper on relaxing Simon's algorithm so that quantum adversaries can still carry out the mentioned attacks without any assumptions on the target function. To that end, we use two different methods, which are suitable for some of PFB quantum attacks. In the first method, as a complement to Kaplan's suggestion, we show that using Simon's algorithm one can find proper partial periods of Boolean vector functions, so that the probability of their establishment, independent of the target function, is directly related to the number of the attacker's quantum queries. Next, we examine how one can use partial period instead of the actual one. The advantage of this method is twofold: It enables the attackers to perform the quantum PFB distinguishers, with smaller number of quantum queries than those of the previous relaxation method. On the other hand, it generalizes the previous forgery attacks on modes of operation for message authentication codes. In the second method, we use Grover's algorithm, as a complement to Simon's algorithm in quantum key recovery attacks. This ensures that the time complexity of the mentioned attacks is less than that of a quantum brute-force attack.

Abstract Machine to machine (M2M) communication, which is also known as machine type communication (MTC), is one of the most fascinating parts of mobile communication technology and also an important practical application of the Internet of Things. The main objective of this type of communication, is handling massive heterogeneous devices with low network overheads and high security guarantees. Hence, various protocols and schemes were proposed to achieve security requirements in M2M communication and reduce computational and communication costs. In this paper, we propose the group-based secure lightweight handover authentication (GSLHA) protocol for M2M communication in LTE and future 5G networks. The proposed protocol mutually authenticates a group of MTC devices (MTCDs) and a new eNodeB (eNB) when these simultaneously enter the coverage of the eNB with considering all the cellular network requirements. The security analysis and formal verification by using the AVISPA tool show that the protocol has been able to achieve all the security goals and overcome various attacks. In addition, the comparative performance analysis of the handover authentication protocols shows that the proposed protocol has the best computational and communication overheads.

Abstract Despite significant advances in the access control domain, requirements of new computational environments like web services still raise new challenges. Lack of appropriate method for specification of access control policies (ACPs), composition, verification and analysis of them have all made the access control in the composition of web services a complicated problem. In this paper, a new independent formal model called Constrained Policy Graph (CPG) for specification of ACPs and their composition as well as verification of conflict or incompatibility among the ACPs is represented. It is shown how CPG can be used in modeling and verification of web service composition ACPs. Also the application of CPG for modeling policies in BPEL processes -as the most common composition method for web services- is illustrated.

Abstract The massive and automated access to Web resources through robots has made it essential for Web service providers to make some conclusion about whether the "user" is a human or a robot. A Human Interaction Proof (HIP) like Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) offers a way to make such a distinction. CAPTCHA is a reverse Turing test used by Web service providers to secure human interaction assumed services from Web bots. Several Web services that include and are not limited to free e-mail accounts, online polls, chat rooms, search engines, blogs, password systems, etc. use CAPTCHA as a defensive mechanism against automated Web bots. In this paper, we present a new clickable image-based CAPTCHA technique. The technique presents user with a CAPTCHA image composed of several sub-images. Properties of the proposed technique offer all of the benefits of image-based CAPTCHAs; grant improved security than that of usual OCR-based techniques, consume less Web page area than most of image-based techniques and at the same time improve the user-friendliness of the Web page.

Abstract Steganography is the ability to hide secret information in a cover-media such as sound, pictures and text. A new approach is proposed to hide a secret into Arabic text cover media using "Kashida", an Arabic extension character. The proposed approach is an attempt to maximize the use of "Kashida" to hide more information in Arabic text cover-media. To approach this, some algorithms have been designed and implemented in a system, called MSCUKAT (Maximizing Steganography Capacity Using "Kashida" in Arabic Text). The improvements of this attempt include increasing the capacity of cover media to hide more secret information, reducing the file size increase after hiding the secret and enhancing the security of the encoded cover media. This proposed work has been tested outperforming previous work showing promising results.

Abstract In information security, ignorance is not bliss. It is always stated that hiding the protocols (let the other be ignorant about it) does not increase the security of organizations. However, there are cases that ignorance creates protocols. In this paper, we propose distributed contingency logic, a proper extension of contingency (ignorance) logic. Intuitively, a formula is distributed contingent in a group of agent if and only if it does not follow from the knowledge of all individual agents put together. We formalize secret sharing scheme (a security property that is built upon ignorance of all agents), and a man in the middle attack to a weak protocol in our logic. We also illustrate a condition where disclose a secret may hide another one forever. Finally we prove the main theorems of every logic, soundness and completeness. We also prove that distributed contingency logic is more expressive than classical contingency logic and epistemic logic.

Abstract This study presents a new method based on the combination of cryptography and information hiding methods. Firstly, the image is encoded by the Double Random Phase Encoding (DRPE) technique. The real and imaginary parts of the encoded image are subsequently embedded into an enlarged normalized host image. DRPE demands two random phase mask keys to decode the decrypted image at the destination. The two random phase masks are regenerated by the chaos theory using a fractal image. To enhance its security, instead of sending the second phase mask directly, the initial conditions and the parameter of the chaotic map and the fractal image are transferred to the authorized user through a secure channel. Experimental results reveal that the proposed method not only enjoys high security but also resists the commonplace attacks.

Abstract GGH class of public-key cryptosystems relies on computational problems based on the closest vector problem (CVP) in lattices for their security. The subject of lattice based cryptography is very active and there have recently been new ideas that revolutionized the field. We present EEH, a GGH-Like public key cryptosystem based on the Eisenstein integers Z [ζ₃] where ζ3 is a primitive cube root of unity. EEH applies representations of polynomials to the GGH encryption scheme and we discuss its key size and parameters selection. We also provide theoretical and experimental data to compare the security and efficiency of EEH to GGH with comparable parameter sets and show that EEH is an improvement over GGH in terms of security and efficiency.

Abstract Wireless sensor networks (WSNs) have many applications in the areas of commercial, military and environmental requirements. Regarding the deployment of low cost sensor nodes with restricted energy resources, these networks face a lot of security challenges. A basic approach for preparing a secure wireless communication in WSNs, is to propose an efficient cryptographic key management protocol between sensor nodes to achieve maximum security with minimum cost. The main motivation of this paper is to apply the position of the sensor nodes as part of their identity for key management in heterogeneous sensor networks. In the proposed scheme, the position of sensor nodes is considered as a part of their identity and it is used for authentication and dedicating key to all network links. Comparing the proposed technique with other schemes shows that it has a higher level of scalability, security, and reliability with less memory complexity.

Abstract In this paper we propose a new method for applying hiding countermeasure against CPA attacks. This method is for software implementation, based on smoothing power consumption of the device. This method is evaluated on the SIMON scheme as a case study; however, it is not relying on any specific SIMON features. Our new method includes only AND equivalent and XOR equivalent operations since every cryptographic algorithm can be implemented with two basic operations, namely AND and XOR. Therefore, hamming weight and hamming distance take constant values at each moment of time. This can decrease data-dependency between processed values and consumed power. In order to practically evaluate the resulting implementation overheads and the resistance improvement against CPA, we implement the proposed coding scheme on SIMON, a lightweight block cipher, on a smart card with the ATmega163 microprocessor. We define resistance as the number of traces, which for less than that number; the correct key cannot be distinguished from all other hypothetical keys by its correlation coefficient in any moment of time. The results of this implementation show 350 times more immunity against correlation attacks.

Abstract In this paper, we propose a new method of differential fault analysis of SHA-3 which is based on the differential relations of the algorithm. Employing those differential relations in the fault analysis of SHA-3 gives new features to the proposed attacks, e.g., the high probability of fault detection and the possibility of re-checking initial faults and the possibility to recover internal state with 22-53 faults.

We also present two improvements for the above attack which are using differential relations in reverse direction to improve that attack results and using the algebraic relations of the algorithm to provide a second way to recover the internal state of SHA-3. Consequently, we show that with 5-8 faults on average, SHA-3's internal state can be fully recovered.
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Abstract Dynamic analysis is a prominent approach in analyzing the behavior of Android apps. To perform dynamic analysis, we need an event generator to provide proper environment for executing the app in an emulator. Monkey is the most popular event generator for Android apps in general, and is used in dynamic analysis of Android malware as well. Monkey provides high code coverage and yet high speed in generating events. However, in the case of malware analysis, Monkey suffers from several limitations. It only considers UI events but no system events, and because of random behavior in generating UI events, it may lose dropping the connectivity of the test environment during the analysis process. Moreover, it provides no defense against malware evasion techniques. In this paper, we try to enhance Monkey by reducing its limitations while preserving its advantages. The proposed approach has been implemented as an extended version of Monkey, named Curious-Monkey. Curious-Monkey provides facilities for handling system events, handling evasion techniques, and keeping the test environment's connectivity up during the analysis process. We conducted many experiments to evaluate the effectiveness of the proposed tool regarding two important criteria in dynamic malware analysis: the ability to trigger malicious payloads and the code coverage. In the evaluation process, we used the Evadroid benchmark and the AMD malware dataset. Moreover, we compared Curious-Monkey with Monkey and Ares tools. The results show that the Curious-Monkey provides better results in case of triggering malicious payloads, as well as better code coverage.