The ISC International Journal of Information Security

Abstract In recent years, determining the common information privately and efficiently between two mutually mistrusting parties have become an important issue in social networks. Many Private Set Intersection (PSI) protocols have been introduced to address this issue. By applying these protocols, two parties can compute the intersection between their sets without disclosing any information about components that are not in the intersection. Due to the broad range of computational resources that the cloud can provide for its users, determining the set intersection by cloud may decrease the computational cost of the users. The proposed protocols by Abadi et al. are two protocols in this context. In this paper, we show that their protocols are vulnerable to eavesdropping attack. Also, a solution is proposed to secure the protocol against mentioned attack. Moreover, we analyze the performance of both O-PSI and modified O-PSI protocols and show that our scheme is comparable with the O-PSI protocol. Actually, one trivial solution for the Abadi et al.’s proposed schemes is to use a secure channel like TLS. However, in the performance evaluation, we compare our applied modification with this trivial solution, and show that our proposed modification is more efficient as some extra encryptions imposed by TLS are no longer required.

Abstract Perturbed Quantization (PQ) steganography scheme is almost undetectable with the current steganalysis methods. We present a new steganalysis method for detection of this data hiding algorithm. We show that the PQ method distorts the dependencies of DCT coefficient values; especially changes much lower than significant bit planes. For steganalysis of PQ, we propose features extraction from the empirical matrix. The proposed features can be exploited within an empirical matrix of DCT coefficients which some most significant bit planes were deleted. We obtain four empirical matrices and fuse resulted features from these matrices which have been employed for steganalysis. This technique can detect PQ embedding on stego images with 77 percent detection accuracy on mixed embedding rates between 0.05 _ 0.4 bits per non-zero DCT AC coefficients (BPNZC). Comparing the results, we also show that the detection rates are effectively comparable with respect to current steganalysis techniques for PQ steganography.

Abstract Database outsourcing is an idea to eliminate the burden of database management from organizations. Since data is a critical asset of organizations, preserving its privacy from outside adversary and untrusted server should be warranted. In this paper, we present a distributed scheme based on storing shares of data on different servers and separating indexes from data on a distinct server. Shamir's secret sharing scheme is used for distributing data to data share servers. A B+-tree index on the order preserved encrypted values for each searchable attribute is stored in the index server. To process a query, the client receives responses including record numbers from the index server and asks these records from data share servers. The final result is computed by the client using data shares. While the proposed approach is secure against different database attacks, it supports exact match, range, aggregation, and pattern matching queries efficiently. Simulation results show the prominence of our approach in comparison with the bucketing scheme as it imposes lower computation and communication costs on the client.

Abstract Elliptic Curve Cryptosystems (ECC) have recently received significant attention by researchers due to their high performance such as low computational cost and small key size. In this paper a novel untraceable blind signature scheme is presented. Since the security of proposed method is based on difficulty of solving discrete logarithm over an elliptic curve, performance of the proposed scheme is quite commendable in comparison with the previous work in terms of security and time complexity.

Abstract Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. The proposed method is based on a causal approach due to the strength of causal methods in practice. To provide a picture of the current intrusive activity on the network, we need a real-time alert correlation. Most causal methods can be deployed offline but not in real-time due to time and memory limitations. In the proposed method, the knowledge base of the attack patterns is represented in a graph model called the Causal Relations Graph. In the offline mode, we construct Queue trees related to alerts' probable correlations. In the real-time mode, for each received alert, we can find its correlations with previously received alerts by performing a search only in the corresponding tree. Therefore, the processing time of each alert decreases significantly. In addition, the proposed method is immune to deliberately slowed attacks. To verify the proposed method, it was implemented and tested using DARPA2000 dataset. Experimental results show the correctness of the proposed alert correlation and its efficiency with respect to the running time.

Abstract ΑΕS _ CMCCv₁, ΑVΑLΑNCHEv₁, CLΟCv₁, and SILCv₁ are four candidates of the first round of CAESAR. CLΟCv₁ is presented in FSE 2014 and SILCv₁ is designed upon it with the aim of optimizing the hardware implementation cost. In this paper, structural weaknesses of these candidates are studied. We present distinguishing attacks against ΑES  _ CMCCv₁ with the complexity of two queries and the success probability of almost 1, and distinguishing attacks on CLΟCv₁ and SILCv₁ with the complexity of Ο (2^n/2) queries and the success probability of 0.63, in which n is bit length of message blocks. In addition, a forgery attack is presented against ΑVΑLΑNCHEv₁ which requires only one query and has the success probability of 1. The attacks reveal weaknesses in the structure of these first round candidates and inaccuracy of their security claims.

Abstract This paper investigates the multiplicative spread spectrum watermarking method for the image. The information bit is spreaded into middle-frequency Discrete Cosine Transform (DCT) coefficients of each block of an image using a generated pseudo-random sequence. Unlike the conventional signal modeling, we suppose that both signal and noise are distributed with Laplacian distribution, because the sample loss of digital media can be better modeled with this distribution than the Gaussian one. We derive the optimum decoder for the proposed embedding method thanks to the maximum likelihood decoding scheme. We also analyze our watermarking system in the presence of noise and provide analytical evaluations and several simulations. The results show that it has the suitable performance and transparency required for watermarking applications.

Abstract Recently permutation multimedia ciphers were broken in a chosen-plaintext scenario. That attack models a very resourceful adversary which may not always be the case. To show insecurity of these ciphers, we present a cipher-text only attack on speech permutation ciphers. We show inherent redundancies of speech can pave the path for a successful cipher-text only attack. To that end, regularities of speech are extracted in time and frequency using short time Fourier transform. We show that spectrograms of cipher-texts are in fact scrambled puzzles. Then, different techniques including estimation, image processing, and graph theory are fused together in order to create and solve these puzzles. Conducted tests show that the proposed method achieves accuracy of 87.8% and intelligibility of 92.9%. These scores are 50.9% and 34.6%, respectively, higher than scores of previous method. Finally a novel method, based on moving spectrogram distance, is proposed that can give accurate estimation of segment length of the scrambler system.

Abstract Recent advances in Machine Learning and Deep Learning have significantly expanded their applications in various domains. The resource-intensive process of training deep neural networks, in terms of substantial labeled data acquisition and computational power, makes these models valuable intellectual property for organizations, hence rising an increasingly crucial need for securing them. A major security threat to deep neural networks is the adversarial examples problem, specifically the black-box type. In these attacks, adversaries generate inputs with often imperceptible crafted perturbations to deceive the model into incorrect classifications, all with no access to the model internals and solely by interacting with it via queries and responses. Among the two primary methods for creating black-box adversarial examples i.e. model extraction-based and query-based approaches, this research focuses on the query-based type, and it explores a novel defense mechanism to mitigate their success. Our proposed method called Divergent Twins Fencing (DTF), employs two subtly different models trained with two different loss functions to incline the execution burden of these attacks. The evaluation criteria for this defense method include measuring the success rate and the average number of queries required to generate adversarial examples using two of the most potent attack methods
from recent studies and comparing its defense performance against a leading defense strategy in the literature, i.e., Random Noise Defense (RND) Method, demonstrating our method’s efficacy in enhancing model security against black-box adversarial attacks.

Abstract GOST block cipher designed in the 1970s and published in 1989 as the Soviet and Russian standard GOST 28147-89. In order to enhance the security of GOST block cipher after proposing various attacks on it, designers published a modified version of GOST, namely GOST2, in 2015 which has a new key schedule and explicit choice for S-boxes. In this paper, by using three exactly identical portions of GOST2 and fixed point idea, more enhanced fixed point attacks for filtration of wrong keys are presented. More precisely, the focus of the new attacks is on reducing memory complexity while keeping other complexities unchanged as well. The results show a significant reduction in the memory complexity of the attacks, while the time complexity slightly increased in comparison to the previous fixed point attacks. To the best of our knowledge, the lowest memory complexity for an attack on full-round GOST2 block cipher is provided here.

Abstract Nowadays, from one hand multimedia authentication techniques are widely used to achieve trustworthiness, on the other hand, due to the rapid growth of image processing software technologies, having a secure method to protect the copyright of these data seems fairly essential. Multipurpose watermarking emerged in order to simultaneously accomplish multimedia authentication and copyright protection. In this paper, we propose a multipurpose watermarking method which achieves perfect security, the ability to detect tampered areas of the watermarked image as well as a lower BER rate, at the cost of reducing capacity by half. This watermarking scheme is blind in the sense that on the receiver side, neither the original host image nor the embedded watermark is needed for ownership watermark extraction or tamper detection. Experimental results show that our method is able to reconstruct extracted tampered watermarks even after various attacks such as JPEG compression, average filtering, gamma correction, median filtering, speckle noise, JPEG compression, sharpening, Wiener filter, and median filtering. Comparisons are provided with other multipurpose watermarking methods which primarily aim at simultaneous goals of copyright protection and authentication. We also show the superiority of our proposed method to three watermarking methods attaining these objectives on a one-goal-at-a-time basis.

Abstract Nowadays, information plays an important role in organizations. Sensitive information is often stored in databases. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. Therefore, the existence of Intrusion Detection Systems in databases is necessary. In this paper, we propose an intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level). For this purpose, we propose a detection method at transaction level, which is based on describing the expected transactions within the database applications. Then at inter-transaction level, we propose a detection method that is based on anomaly detection and uses data mining to find dependency and sequence rules. The main advantage of this system, in comparison with the previous database intrusion detection systems, is that it can detect malicious behaviors in both transaction and inter-transaction levels. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative alarms. In order to evaluate the accuracy of the proposed system, some experiments have been done. The experiment results demonstrate that the true positive rate (recall metric) is higher than 80%, and the false positive rate is lower than 10% per different data sets and choosing appropriate ranges for support and confidence thresholds. The experimental evaluation results show high accuracy and effectiveness of the proposed system.

Abstract A steganography system must embed the message in an unseen and unrecognizable manner in the cover signal. Embedding information in transform coefficients, especially Discrete Wavelet Transform (DWT), is one of the most successful approaches in this field. The proposed method in this paper has two main steps. In the first step, the XOR logical function was used to embed two bits of data in the adjacent DWT coefficient pair. No change in the coefficients will occur if the XOR result of the two bits of low-value data of the two adjacent coefficients is identical to the two bits of secret data. Otherwise, one or both of the coefficient(s) will need a one-unit increase or decrease. In the second step, the genetic algorithm was used to select, between the two possible solutions, a new value for the adjacent coefficient pair that needs to be changed. Using the genetic algorithm, the selections were made such that the generated stego image experienced the least change relative to the cover image. The results of comparing this method with the existing methods in low- and high-level embedding showed that the proposed method was successful in producing stego images with high-quality criteria. In addition, the SPAM steganalysis method did not show high accuracy in its detection. One of the benefits of the proposed method is the need for a short key to embed and extract the secret message. This issue increases the security and feasibility of the proposed method.

Abstract In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have binary relationships, there is no direct rating available. Therefore, a new method is required to infer trust values in these networks. To bridge this gap, this paper aims to propose a new method which takes advantage of user similarity to predict trust values without any need for direct ratings. In this approach, which is based on socio-psychological studies, user similarity is calculated from the profile information and the texts shared by the users via text-mining techniques. Applying Ziegler ratios to our approach revealed that users are more than 50% more similar to their trusted agents than to arbitrary peers, which proves the validity of the original idea of the study about inferring trust from language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that the predicted trust values are sufficiently acceptable (with a precision of 61%). We have also studied the benefits of using context in inferring trust. In this regard, the analysis revealed that the precision of the predictions can be improved up to 72%. Besides the application of this approach in web-based social networks, the proposed technique can also be of much help in any direct rating mechanism to evaluate the correctness of trust values assigned by users, and increase the robustness of trust and reputation mechanisms against possible security threats.

Abstract Midori64 is a lightweight SPN block cipher introduced by Banik et al. at ASIACRYPT 2015 which operates on 64-bit states through 16 rounds using a 128-bit key. In the last decade, Midori64 has been exposed to several attacks intensely. In this paper, we provide the first boomerang attack on Midori64 in the literature, to the best of our knowledge. For this purpose, firstly we present a practical single key 7-round boomerang attack on Midori64 improving the mixture idea of Biryukov by a new technique which we call ``mixture pool", and then extend our attack up to 9 rounds with time complexity $2^{122.3}$, and memory and data complexity $2^{36}$. (The authors of Midori stated that they expect much smaller rounds than 8 rounds of Midori64 are secure against boomerang-type attacks.) We also emphasize that the mixture pool idea provides a kind of data-memory tradeoff and hence presents more usefulness for boomerang-type attacks.

Abstract Fragile watermarking is the task of embedding a watermark in a media (an image in this paper) such that even small changes, called tamper, can be detected or even recovered to prevent unauthorized alteration. A well-known category of spatial fragile watermarking methods is based on embedding the watermark in the least significant bits of the image to preserve the quality. In addition, Hamming code is a coding algorithm in communication that transmits the data-bits by augmenting some check-bits in order to exactly detect and recover single-bit modifications. This property is previously used to detect and perfectly recover the images modified by small tampers less than a quarter of the image in diameter. To achieve this goal, the Hamming code is applied on a distributed pixel, bits of which are gathered from sufficient far pixels in the image. It guarantees that such tampers can toggle at most one bit of each distributed Hamming code that is recoverable. It was the only guaranteed perfect reconstruction method of small tampers, based on our knowledge. In this paper, the method has been extended to support distortion in two bits of a Hamming code by use of common structures of distributed codes. It leads to guarantee recovery of tampers less than half of the image in width and height. According to the experimental results, the proposed method achieved better performance, in terms of recovering the tampered areas, in comparison to state-of-the-art.

Abstract Wireless networks, Internet of Things (IoT), Internet of Everything (IoE), and smart homes have become extremely important terms in our present-day life. Most of the buildings, companies, institutions, and even homes depend on
these technologies for interaction, communication, automation, and everything surrounding humans. To understand the advanced topics in wireless networks and IoT devices, it is necessary to use one of the practical learning tools, called
Packet Tracer. This wireless network simulator is freely available by Cisco Networking Academy. In this paper, we will use Packet Tracer to design a smart home based on wireless and IoT devices and illustrate how to create different networking scenarios to make our homes more comfortable and convenient.

Abstract Botnets are recognized as one of the most dangerous threats to the Internet infrastructure. They are used for malicious activities such as launching distributed denial of service attacks, sending spam, and leaking personal information. Existing botnet detection methods produce a number of good ideas, but they are far from complete yet, since most of them cannot detect botnets in an early stage of their lifecycle; moreover, they depend on a particular command and control (C&C) protocol. In this paper, we address these issues and propose an online unsupervised method, called BotOnus, for botnet detection that does not require a priori knowledge of botnets. It extracts a set of flow feature vectors from the network traffic at the end of each time period, and then groups them to some flow clusters by a novel online fixed-width clustering algorithm. Flow clusters that have at least two members, and their intra-cluster similarity is above a similarity threshold, are identified as suspicious botnet clusters, and all hosts in such clusters are identified as bot infected. We demonstrate the effectiveness of BotOnus to detect various botnets including HTTP-, IRC-, and P2P-based botnets using a testbed network. The results of experiments show that it can successfully detect various botnets with an average detection rate of 94.33% and an average false alarm rate of 3.74%.

Abstract Code reuse attacks such as return oriented programming and jump oriented programming are the most popular exploitation methods among attackers. A large number of practical and non-practical defenses are proposed that differ in their overhead, the source code requirement, detection rate and implementation dependencies. However, a usual aspect among these methods is consideration of the common behaviour of code reuse attacks, which is the construction of a gadget chain. Therefore, the implication of a gadget and the minimum size of an attack chain are a matter of controversy. Conservative or relaxed thresholds may cause false positive and false negative alarms, respectively. The main contribution of this paper is to provide a tricky aspect of code reuse techniques, called tiny code reuse attacks (Tiny-CRA) that demonstrates the ineffectiveness of the threshold based detection methods. We show that with bare minimum assumptions, Tiny-CRA can reduce the size of a gadget chain in shuch a way that no distinction can be detected between normal behavior of a program and a code-reuse execution. To do so, we exhibit our Tiny-CRA primitives and introduce a useful gadget set available in libc. We demonstrate the effectiveness of our approach by implementing nine different shell-codes and exploiting real-world buffer overflow vulnerability in HT Editor 2.0.20.

Abstract Many security protocols have the aim of authenticating one agent acting as initiator to another agent acting as responder and vice versa. Sometimes, the authentication fails because of executing several parallel sessions of a protocol, and because an agent may play both the initiator and responder role in parallel sessions. We take advantage of the notion of transition systems to specify authentication for parallel multiple session's execution. To model the authentication, two main notions called 1. agent's scope and 2. agent's recognizability are introduced, which consider the difference of ability of agents due to their different roles in the protocol and different access to keys and secrets. To formalize above notions, a process algebra provided by some primitives for manipulating cryptographic messages is used. We formalize some security protocols and examine our definition of authentication for them. We just discuss the symmetric key case.

Abstract Nowadays, botnets are considered as essential tools for planning serious cyber attacks. Botnets are used to perform various malicious activities such as DDoS attacks and sending spam emails. Different approaches are presented to detect botnets; however most of them may be ineffective when there are only a few infected hosts in monitored network, as they rely on similarity in bots activities to detect the botnet. In this paper, we present a host-based method that can detect individual bot-infected hosts. This approach is based on botnet life-cycle, which includes common symptoms of almost all types of botnet despite their differences. We analyze network activities of each process running on the host and propose some heuristics to distinguish behavioral patterns of bot process from legitimate ones based on statistical features of packet sequences and evaluating an overall security risk for it. To show the effectiveness of the approach, a tool named BotRevealer has been implemented and evaluated using real botnets and several popular applications. The results show that in spite of diversity of botnets, BotRevealer can effectively detect the bot process among other active processes.

Abstract In this study, we propose a secure communication scheme based on the synchronization of two identical fractional-order chaotic systems. The fractional-order derivative is in Caputo sense, and for synchronization, we use a robust sliding-mode control scheme. The designed sliding surface is taken simply due to using special technic for fractional-order systems. Also, unlike most manuscripts, the fractional-order derivatives of state variables can be chosen differently. The stability of the error system is proved using the Lyapunov stability of fractional-order systems. Numerical simulations illustrate the ability and effectiveness of the proposed method. Moreover, synchronization results are applied to secure communication using the masking method. The security analysis demonstrates that the introduced algorithm has a large keyspace, high sensitivity to encryption keys, higher security, and the acceptable performance speed.

Abstract In the biclique attack, a shorter biclique usually results in less data complexity, but at the expense of more computational complexity. The early abort technique can be used in partial matching part of the biclique attack in order to slightly reduce the computations. In this paper, we make use of this technique, but instead of slight improvement in the computational complexity, we keep the amount of this complexity the same and reduce the data complexity enormously by a shorter biclique.
With this approach, we analysed full-round of LBlock, and also LBlock with modified key schedule (which was designed to resist biclique attack) both with data complexity 2^12, while the data complexity of the best biclique attack on the former was 2^52 and for the latter there is no attack on the full-round cipher, so far. Then we proposed a new key schedule that is more resistant against biclique cryptanalysis, though the low diffusion of the cipher makes it vulnerable to this attack regardless of the strength of the key schedule. Also using this method, we analyzed TWINE-80 with 2^12 data complexity. The lowest data complexity for the prior attack on the TWINE-80 was 2^60. In all the attacks presented in this paper, the computational complexities are slightly improved in comparison to the existing attacks.

Abstract An important issue in P2P networks is the existence of malicious nodes that decreases the performance of such networks. Reputation system in which nodes are ranked based on their behavior, is one of the proposed solutions to detect and isolate malicious (low ranked) nodes. Gossip Trust is an interesting previously proposed algorithm for reputation aggregation in P2P networks based on the concept of gossip. Despite its important contribution, this algorithm has deficiencies especially with high number of nodes that leads to high execution time and low accuracy in the results. In this paper, a grouped Gossip based Reputation Aggregation (GGRA) algorithm is proposed. In GGRA, Gossip Trust is executed in each group between group members and between groups instead of executing in the whole network. Due to the reduction in the number of nodes and using strongly connected graph instead of a weakly one, gossip algorithm in GGRA is executed quickly. With grouping, not only reputation aggregation is expected to be more scalable, but also because of the decrement in the number of errors of the gossiped communication, the results get more accurate. The evaluation of the proposed algorithm and its comparison with Gossip Trust confirms the expected results.

Abstract Wireless Sensor Networks (WSNs) offer inherent packet redundancy since each point within the network area is covered by more than one sensor node. This phenomenon, which is known as sensors co-coverage, is used in this paper to detect unauthenticated events. Unauthenticated event broadcasting in a WSN imposes network congestion, worsens the packet loss rate, and increases the network energy congestion. In the proposed method, the more the safe, the less the unsafe (MSLU) method, each secure occurred event must be confirmed by various sensor nodes; otherwise the event is dropped. Indeed, the proposed method tends to forward event occurrence reports that are detected by various sensor nodes. The proposed method is evaluated by means of simulation as well as analytical modeling. A wide range of simulations, which are carried out using NS-2, show that the proposed method detects more than 85% of unauthenticated events. This comes at the cost of the network end-to-end delay of 20% because the proposed method does not impose delay on incoming packets. In addition, the proposed method is evaluated by means of an analytical model based on queuing networks. The model accurately estimates the network performance utilizing the proposed unauthenticated event detection method.