The Deep Neural Network (DNN) based detection model’s dependency on third-party crowdsourced sources poses a new security threat from backdoor attacks against malware detectors. Attackers attempt to inject hidden backdoors into the target model, allowing it to perform well on clean samples. Once the attacker-defined trigger activates the hidden backdoor, the model predictions for poisoned samples are maliciously altered. Different from existing backdoor attacks either utilize fixed triggers or generate sample-specific triggers, we explore a novel backdoor attack paradigm in malware domain and propose MalPDT, in which backdoor triggers achieve dynamic variability in trigger patterns and retain compatibility across malware samples. We train a generator capable of hiding information to produce dynamically variable encoded byte segments, which are then injected as triggers into the unused regions of PE malware in a functionality-preserving manner. In MalPDT, any combination of a malware sample and a trigger can form a poisoned sample capable of activating the backdoor, enabling plug-and-play capability. We conduct extensive experiments to validate the effectiveness of MalPDT in attacking models with or without defenses.

In IoT device applications, due to privacy protection requirements, it is often impossible to obtain large-scale labeled datasets for model training. A representation model that effectively extracts flow features with limited labeled samples is therefore critical. To meet this need, we propose a model combining temporal features (for trend changes/short-term fluctuations) and frequency-domain features (for periodicity/stability patterns) in network flows. In order to fully tap the potential of these two types of features, contrastive learning (CL) technology is used to model them respectively. For temporal-domain features, we employ a momentum encoder-based training strategy to learn robust representations adaptable to network-induced noise. For frequency domain features, we propose a new supervised CL loss function. It enhances inter-class separability while improving the representation ability (i.e., the model's capacity to extract discriminative features) of minority classes. The experimental results demonstrate the universality of this approach, which is not limited to specific data sets or attack types. The most representative results were obtained on the UNSW-NB15 dataset, with an accuracy of 0.9699 for the balanced training sets, surpassing the other best models' performance(0.6300). We have released our source code to facilitate future studies onhttps://github.com/Ann96125/MTRF.

Audio temporal forgery localization (ATFL) aims to find the precise forgery regions of the partial spoof audio that is purposefully modified. Existing ATFL methods rely on training efficient networks using fine-grained annotations, which are obtained costly and challenging in real-world scenarios. To meet this challenge, in this paper, we propose a progressive audio-language co-learning network (LOCO) that adopts co-learning and self-supervision manners to prompt localization performance under weak supervision scenarios. Specifically, an audio-language co-learning module is first designed to capture forgery consensus features by aligning semantics from temporal and global perspectives. In this module, forgery-aware prompts are constructed by using utterance-level annotations together with learnable prompts, which can incorporate semantic priors into temporal content features dynamically. In addition, a forgery localization module is applied to produce forgery proposals based on fused forgery-class activation sequences. Finally, a progressive refinement strategy is introduced to generate pseudo frame-level labels and leverage supervised semantic contrastive learning to amplify the semantic distinction between real and fake content, thereby continuously optimizing forgery-aware features. Extensive experiments show that the proposed LOCO achieves SOTA performance on three public benchmarks.

Smart and mobile devices are essential components of the Internet of Things (IoT) ecosystems, facilitating connectivity and automation across various domains. Due to its flexibility, the Android operating system is widely adopted in these devices. However, their increasing integration into IoT networks has introduced significant security risks, particularly from Android malware. To address these challenges, effective detection methods are needed to enhance IoT security. Given the success of contrastive learning in computer vision, researchers have increasingly explored its potential for Android malware detection. This article presents a static Android malware detection method that integrates deep learning with supervised contrastive learning. Based on the characteristic that contrastive learning enhances the model’s ability to effectively represent input samples, we design a novel contrastive loss based on structural similarity metrics and integrate it with contractive loss and binary cross-entropy loss to construct a hierarchical loss function for guiding model optimization. Furthermore, the method directly analyzes the classes.dex file from Android application package, eliminating the need for feature engineering or domain expertise, thus enhancing its applicability. Experimental results demonstrate that the proposed method achieves an 87.13% F1-score on the AndroZoo dataset, outperforming baseline models while maintaining computational efficiency and practical usability. Ablation studies validate the effectiveness of the hierarchical loss function in improving model performance and ensuring consistent malware representation within the same family.

The security of the FPGA cloud has become a major concern for both industry and academia due to its widespread use in many vital domains. In this article, we expose a hardware vulnerability in the FPGA cloud and demonstrate a covert and efficient denial-of-service (DoS) attack method that severely threatens the security of the FPGA cloud. First, we adopt a flip-flop-based ring oscillator (RO) to construct an adaptive ring oscillator network (RON). Second, we devise a power and temperature-based resource adjustment algorithm to decide the maximum number of ROs in the adaptive RON. By constraining the size of the adaptive RON, the power and the thermal footprint of the attack process can be reduced. Finally, we design an adaptive frequency sweeping algorithm to automatically search for an effective frequency and perform a successful attack on the FPGA. To validate our method, we conduct exemplary attacks on FPGAs. The results reveal that our method can successfully bypass the design rule checking (DRC) and security measures of the FPGA cloud to crash the FPGA. Besides high-end FPGAs, we demonstrate our method is suitable for some FPGAs with moderate performance. Furthermore, we discuss the impact of the number of ROs and the duty cycle on the proposed method.

Fault-induced vulnerabilities have been studied in various aspects. While traditional fault injection techniques easily detect system-level vulnerabilities like buffer overflows, fault executions can introduce subtle potential vulnerabilities that may not trigger any system-level observable behaviors. These are particularly dangerous in cryptography. Using advanced cryptanalysis methods, these vulnerabilities, such as producing faulty ciphertexts, have been successfully exploited and are regarded as great threats to the security of real-world cryptography. In this way, there is a pressing need to study this very area. Our paper generally explores the internals of the fault-induced data-level vulnerabilities, which are subtle vulnerabilities resulting from faults that may not cause system crashes or overt errors but can expose sensitive information or weaken cryptographic primitives under specific cryptanalytic techniques, in cryptographic libraries. We propose a novel framework which can systematically analyze the vulnerabilities in cryptographic libraries under different fault models. By employing this method, we identified numerous critical fault locations that could undermine the security of cryptographic systems across a broad spectrum of libraries, fault models, and platforms. Furthermore, we provide a comprehensive analysis of select case studies, and engage in detailed discussions about the strategies to alleviate such vulnerabilities.

The adversarial example presents new security threats to trustworthy detection systems. In the context of evading dynamic detection based on API call sequences, a practical approach involves inserting perturbing API calls to modify these sequences. The type of inserted API calls and their insertion locations are crucial for generating an effective adversarial API call sequence. Existing methods either optimize the inserted API calls while neglecting the insertion positions or treat these optimizations as separate processes. This can lead to inefficient attacks that insert a large number of unnecessary API calls. To address this issue, we propose a novel reinforcement learning (RL) framework, dubbed GAME-RL, which simultaneously optimizes both the perturbing APIs and their insertion positions. Specifically, we define malware modification through IAT (Import Address Table) hooking as a sequential decision-making process. We introduce an invalid action masking and an auto-regressive policy head within the RL framework, ensuring the feasibility of IAT hooking and capturing the inherent relationship between factors. GAME-RL learns more effective evasion strategies, taking into account functionality preservation and the black-box setting. We conduct comprehensive experiments on various target models, demonstrating that GAME-RL significantly improves the evasion rate while maintaining acceptable levels of adversarial overhead.

Persistent Fault Analysis(PFA) is a powerful analysis technique proposed in CHES 2018, which utilizes those faults that are injected before execution and persist throughout the encryption. However, when it is applied to the block cipher which has multiple S-boxes, the key cannot be recovered in just one attack. The adversary has to conduct the fault attack several times and inject faults into all the distinct S-boxes. In this paper, we proposeKey Schedule Guided Persistent Fault Attack(KGPFA), which utilizes the key schedule to guide the fault injection and fault analysis. By analyzing the key schedule, KGPFA exploits the relations between the key leakages caused by the same faulty S-box in various rounds. It can reduce the number of attacks and the number of faults required to recover the key. Our major contributions are twofold. Firstly, in the fault injection step, we provideKey Schedule Guided Persistent Fault Injection(KGPFI) strategies to reduce the number of attacks and the number of faults under the assumption of both ciphertext-only and known-plaintext attacks. Secondly, in the fault analysis step, as our target ciphers are Feistel-based, we propose theIneffective Algebraic Persistent Fault Analysis(IAPFA) to extend the usage ofAlgebraic Persistent Fault Analysis(APFA) in the ineffective persistent fault setting. To demonstrate the effectiveness of our technique, we apply KGPFA to four widely used block ciphers with multiple S-boxes, DES, 3DES, LBlock, and Camellia. In our experiment, in the ciphertext-only attack, the key of DES can be recovered with 300 ineffective ciphertexts (coresponding to 827 ciphertexts) and four faulty S-boxes within 12.18min. Under the assumption of known-plaintext, the key of DES is recovered within two faulty S-boxes in 2.34h. For LBlock, the key is recovered with two faulty S-boxes and 100 ineffective ciphertexts (coresponding to 6211 ciphertexts) in 1.16min.

In recent years, the construction of behavior-based analysis models is hindered by issues such as insufficient data, difficulty in labeling, and the complexity of behavior types. In reality, specific cyber threats often require manual analysis of raw network traffic, which is a complex and inefficient process. Flow spectrum can simplify the complex analysis process of raw network flow by mapping it from a high-dimensional space to a one-dimensional spectral space. However, the existing flow spectrum cannot adapt to the open-world scenarios and behavior-based detection for unknown cyber threats. To address these challenges, we propose a new flow spectrum construction scheme, named unFlowS, to effectively represent network flows and assist analysts to understand the behaviors of network traffic. unFlowS-Net, an unsupervised flow-based detection model we designed as the core of our scheme, can transform network flows into spectral lines. It makes unFlowS possible to detect unknown cyber threats. We further build spectral vectors for spectral lines generated by network flow sets, enabling the visualization of network behaviors within a period of time and automatic behavior-based detection. Experimental results demonstrated that unFlowS-Net can achieve better performance than state-of-the-art methods on unsupervised flow-based detection. Based on spectral vectors, not only can it intuitively display the network behavior characteristic of the target host, but also automatically detect suspicious network behaviors.

To ensure the privacy and security of Internet of Things data, encrypted transmission of data has become a common approach. However, this has also introduced limitations for the detection of malicious network flows, often requiring reliance on only a few selected features for categorizing malicious flows. In this paper, we proposed a novel Graph Integration Theory and applied it to construct graphs based solely on packet length sequences, aiming to enhance the detection capability of single-feature-based methods, such as packet length sequences. Our proposed approach not only demonstrated its applicability in binary and multi-class classification problems but also provided a detailed analysis of the underlying reasons for its effectiveness in detecting different types of attacks and in various classification networks. Additionally, we proposed the use of the Tree-Like structure to construct Traffic Interaction Graphs and verified that the Graph Integration Theory achieved excellent classification results in both the Tree-Like and Cross-Linked list structures. Specifically, the average detection accuracy achieved in the Tree-Like structure was 0.9842, while that in the Cross-Linked list structure was 0.9836. These results significantly outperformed those obtained using either original graph structure or packet length sequences alone for detection. In the ten-class classification problem, the proposed approach achieved a detection accuracy of 0.8557, which was much higher than the accuracy of 0.6252 obtained using only packet length sequences, as well as the accuracy of 0.6634 obtained using only the original graph structure.

As a common defense against side-channel attacks, random delay insertion introduces noise into the executive flow of encryption, which increases attack complexity. Accordingly, various techniques are exploited to mitigate the defense effect of such insertions. As an advanced mathematical technique, wavelet analysis is considered to be a more effective technology according to its detailed and comprehensive interpretation of signals. In this paper, we propose a unified and fully automated wavelet-based attack framework (denoted asUWAF), whose data processing is kept within one unified wavelet domain, with three enhanced components: denoising, alignment and key extraction. We put forward a new idea of combining machine learning with wavelet analysis to realize the full automation of the program for attack framework, rendering it possible to search exhaustively for the optimal combination of parameter settings in wavelet transform. Our proposal finds a new setting of wavelet parameters that have not been exploited ever before and achieves the performance enhancement for about 20 times fewer traces required for successful key recovery.UWAFis compared with several mainstream attack frameworks. Experimental results show that it outperforms those counterparts, and can be considered as an effective framework-level solution to defeat the countermeasure of random delay insertion.

Static analysis is a crucial protection layer that enables modern antivirus systems to address the rampant proliferation of malware. These systems are increasingly relying on deep neural networks (DNNs) to automatically extract reliable features and achieve outstanding detection accuracy. Since DNNs are known to be vulnerable to adversarial examples, several studies have proposed practical evasion attacks to generate adversarial perturbations that can evade malware detectors. These attacks, however, require specific designs for the given input sample, prohibiting them from large-scale deployment. Therefore, it is more practical to generate sample-agnostic perturbations that do not involve recalculations regardless of the input malware sample. To this end, we leverage an adversarial patch attack, which is a special type of adversarial attack that dose not know the sample being modified during the attack construction process. In particular, we propose a new adversarial attack against malware detection systems called MalPatch. It locates the nonfunctional part of malware for adversarial patch injection to protect its executability while generating adversarial examples based on different strategies. The generated patch can be injected into any malware sample, fooling the detector into classifying it as benign. Experimental results demonstrate that MalPatch is effective under different attack settings. In the white-box setting, MalPatch achieves 69%-78% success rates against DNN detectors based on raw byte features and 47%-96% success rates against four grayscale detectors based on image features. In the black-box setting, the success rates of MalPatch against the same models reach 54%-74% and 27%-42%, respectively. We conclude by discussing several of its potential countermeasures and the generality of our approach.

In the face of increasing network traffic, network security issues have gained significant attention. Existing network intrusion detection models often improve the ability to distinguish network behaviors by optimizing the model structure, while ignoring the expressiveness of network traffic at the data level. Visual analysis of network behavior through representation learning can provide a new perspective for network intrusion detection. Unfortunately, representation learning based on machine learning and deep learning often suffer from scalability and interpretability limitations. In this article, we establish an interpretable multi-layer mapping model to enhance the expressiveness of network traffic data. Moreover, the unsupervised method is used to extract the internal distribution characteristics of the data before the model to enhance the data. What’s more, we analyze the feasibility of the proposed flow spectrum theory on the UNSW-NB15 dataset. Experimental results demonstrate that the flow spectrum exhibits significant advantages in characterizing network behavior compared to the original network traffic features, underscoring its practical application value. Finally, we conduct an application analysis using multiple datasets (CICIDS2017 and CICIDS2018), revealing the model’s strong universality and adaptability across different datasets.

With the widespread application of machine learning techniques in malware detection, researchers have proposed various adversarial attack methods to generate adversarial examples (AEs) of malware, thereby evading detection. Previous studies have shown that the reinforcement learning (RL) framework can enable black-box attacks by performing a sequence of function-preserving operations, which produces functional evasive malware samples. However, it is difficult to obtain the useful guidance and feedbacks from the environment for agent training in the black-box scenario, which results in the RL framework being unable to learn the effective evasion policy. In this paper, we propose the Shapley prior and establish a prior-guidance-based RL framework, namely PSP-Mal, to generate AEs against Portable Executable (PE) malware detectors. Our framework improves on existing methods in three aspects: 1) We explore feature effects of the black-box model by computing Shapley values and further propose the Shapley prior to represent the expected impact of operations. 2) A novel prioritized experience utilization mechanism is established regarding the Shapley prior guidance in the RL framework. 3) The actions are expanded into item-content pairs and we use the Thompson sampling to choose effective content, which helps to reduce randomness and ensure repeatability. We compare the attack performance of our framework with other methods, and experimental results demonstrate that our algorithm is more effective. The evasion rates of PSP-Mal against the LightGBM models trained on EMBER and SOREL-20M reach 76.88% and 72.03%, respectively.

With the widespread use of FPGA clouds in high-end fields such as artificial intelligence, its security issues have become a major focus for both academia and industry. In this paper, we propose a covert Denial-of-Service (DoS) attack method that specifically targets commercial FPGA clouds, posing a serious threat to the availability of FPGA clouds. Firstly, we design a malicious ring oscillator network (RON) that can evade the design rule checking of FPGA clouds. Then, we propose a resource adjustment algorithm to determine the maximum number of ring oscillators (ROs) in the RON while satisfying the FPGA's power and on-chip temperature constraints to guarantee the covertness of the attack process. Finally, we manually search for the efficient attack frequency for the RON to attack FPGAs. To validate our method, we perform DoS attacks on XVU9P and 10AX115 FPGAs. The results demonstrate that our method can evade the security measures of FPGA clouds and successfully crash FPGAs.

For embedded devices, the uncertainty of target physical environments is always a great challenge. With constrained resources and common overloaded uses, they can be more exposed to hardware faults. Other than stability and ordinary security issues, there exist some subtle phenomenons that lead to potential cryptanalysis or secret leakage. In this paper, we present STALKER, a framework to analyze the fragility of libraries under hardware fault models. Compared with existing tools, our framework targets faulty execution outputs, and can flexibly work on different libraries, architectures and support different search schemes. We find dozens of security-sensitive bits that may cause critical issues and provide detailed analysis.

Attacker identification from network traffic is a common practice of cyberspace security management. However, network administrators cannot cover all security equipment due to the cyberspace management cost constraints, giving attackers the chance to escape from the surveillance of network security administrators by legitimate actions and to perform the attack in both physical domain and digital domain. Therefore, we proposed a hidden attack sequence detection method based on reinforcement learning to deal with the challenge through modeling the network administrators as an intelligent agent that learns their action policy from the interaction with the cyberspace environment. Following Deep Deterministic Policy Gradient (DDPG), the intelligent agent can not only discover the hidden attackers hiding in the legitimate action sequences but also reduce the cyberspace management cost. Furthermore, a dynamic reward DDPG method was proposed to improve defense performance, which set dynamic reward depending on the hidden attack sequences steps and agent’s check steps, compared to the fixed reward in common methods. Meanwhile, the method was verified in a simulated experimental cyberspace environment. Finally, the experimental results demonstrate that there are hidden attack sequences in cyberspace, and the proposed method can discover the hidden attack sequences. The dynamic reward DDPG shows superior performance in detecting hidden attackers, with a detection rate of 97.46%, which can improve the ability to discover hidden attackers and reduce the 6% cyberspace management cost compared to DDPG.

Hardware Trojan (HT) is a malicious modification in the chip circuitry, which may lead to undesired chip function changing or sensitive information leaking once activated. As recently studied, HT has become one of the main threats for Internet-of-Things (IoT) security, and therefore, protecting IoT against the HT attack attracts growing attention from IoT researchers. In this article, we propose an HT detection technique which makes use of chip temporal thermal information and self-organizing map (SOM) neural network to automatically isolate the Trojan-infected chips with the Trojan-free ones, and meanwhile, confirm the Trojan location at the infected chips. The experimental results reveal that our method is effective. Specifically, for the Trust-hub benchmarks, it can detect HTs which increase only 0.02% power consumption of the original design and localize the Trojan positions precisely without any error. In addition, we demonstrate the advantages of our method over two existing HT detection methods, namely, the thermal and power map (TPM) and ring oscillator net (RON), and make a thorough discussion on how the thermal image resolution, chip technology, and clustering algorithm affect the Trojan detection results.

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.

Nowadays, field programmable gate array (FPGA) has been widely used in Internet of Things (IoT) since it can provide flexible and scalable solutions to various IoT requirements. Meanwhile, hardware Trojan (HT), which may lead to undesired chip function or leak sensitive information, has become a great challenge for FPGA security. Therefore, distinguishing the Trojan-infected FPGAs is quite crucial for reinforcing the security of IoT. To achieve this goal, we propose a clock-tree-concerned technique to detect the HTs on FPGA. First, we present an experimental framework which helps us to collect the electromagnetic (EM) radiation emitted by FPGA clock tree. Then, we propose a Trojan identifying approach which extracts the mathematical feature of obtained EM traces, i.e., 2-D principal component analysis (2DPCA) in this paper, and automatically isolates the Trojan-infected FPGAs from the Trojan-free ones by using a BP neural network. Finally, we perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid in detecting HTs on FPGA. Specifically, for the trust-hub benchmarks, we can find out the FPGA with always on Trojans (100% detection rate) while identifying the triggered Trojans with high probability (by up to 92%). In addition, we give a thorough discussion on how the experimental setup, such as probe step size, scanning area, and chip ambient temperature, affects the Trojan detection rate.

Role-based access control (RBAC) is widely adopted in network security management, and role mining technology has been extensively used to automatically generate user roles from datasets in a bottom-up way. However, almost all role mining methods discover the user roles from existing user-permission assignments, which neglect the dependency relationships between user permissions. To extend the ability of role mining technology, this paper proposes a novel role mining framework based on multi-domain information. The framework estimates the similarity between different permissions based on the fundamental information in the physical, network, and digital domains and attaches interdependent permissions to the same role. Three simulated network scenarios with different multi-domain configurations are used to validate the effectiveness of our method. The experimental results show that the method can not only capture the interdependent relationships between permissions, but also detect user roles and permissions more reasonably.

The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.

Chip multiprocessor (CMP) suffers from growing threats on hardware security in recent years, such as side channel attack, hardware Trojan infection, chip clone, etc. In this paper, we propose a security-aware (SA) task mapping method to reduce the information leakage from CMP thermal side channel. First, we construct a mathematical function that can estimate the CMP security cost corresponding to a given mapping result. Then, we develop a greedy mapping algorithm that automatically allocates all threads of an application to a set of proper cores, such that the total security cost is optimized. Finally, we perform extensive experiments to evaluate our method. The experimental results show that our SA mapping effectively decreases the CMP side channel leakage. Compared to the two existing task mapping methods, Linux scheduler (LS; a standard Linux scheduler) and NoC-Sprinting (NS; a thermal-aware mapping technique), our method reduces side-channel vulnerability factor by up to 19% and 7%, respectively. Moreover, our method also gains higher computational efficiency, with improvement in million instructions per second achieving up to 100% against NS and up to 33% against LS.

An optimized lightweight Hardware Trojan (HT)based fault attack is proposed, especially for those resource-constrained environments such as IoT networks. Firstly, Algebraic fault analysis (AFA)is introduced to evaluate different fault models and search for the optimal one. Next, considering the limited resource, a lightweight HT is carefully designed which only flips one bit of the circuit in IoT device. Finally, AFA is applied again to exploit the fault and recover the secret key. An illustrative attack is demonstrated on DES implemented on an FPGA platform, SASEBO-GII. This paper shows that, for single bit fault injection at different rounds or different indexes in the same round, the reduced key search space of DES varies. The proposed technique can search for the optimal fault model, guide the lightweight Hardware Trojan design and automatically recover the secret key. Only one fault is required to recover the secret key of DES, which improves the stealthiness of the designed Hardware Trojan in IoT networks. The entire attack framework can also be applied to other block ciphers such as AES and PRESENT.

The block cipher LED is well suited for resource-constrained scenarios. However, it is vulnerable to the recent fault attacks and different results have been achieved even under the same fault model. In this paper, a comprehensive investigation is conducted on the fault analysis on LED. A novel differential fault analysis is proposed, which is based on the so-called constraint equations. The proposed attack can combine constraint equations at different levels, pushing the differential fault analysis on LED towards its limit in terms of the time complexity, the data complexity and the remained key search space. Under random nibble fault model, SINGLE fault injection can reduce the key search space of LED-64 to 27.90within 1.89s, compared to 217.65within 7 minutes in prior finest contributions. As to DFA on LED-128, TWO fault injections can reduce the key search space to 215.82within 247.88s, compared to 221.96within 16 minutes in previous work. To the best of our knowledge, the scheme that we proposed is the most efficient fault attack on LED cryptosystems.

The Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR) supported by the National Institute of Standards and Technology (NIST) is an ongoing project calling for submissions of authenticated encryption (AE) schemes. The competition itself aims at enhancing both the design of AE schemes and related analysis. The design goal is to pursue new AE schemes that are more secure than advanced encryption standard with Galois/counter mode (AES-GCM) and can simultaneously achieve three design aspects: security, applicability, and robustness. The competition has a total of three rounds and the last round is approaching the end in 2018. In this survey paper, we first introduce the requirements of the proposed design and the progress of candidate screening in the CAESAR competition. Second, the candidate AE schemes in the final round are classified according to their design structures and encryption modes. Third, comprehensive performance and security evaluations are conducted on these candidates. Finally, the research trends of design and analysis of AE for the future are discussed.

The study of software bugs has long been a key area in software security. Dynamic symbolic execution, in exploring the program's execution paths, finds bugs by analyzing all potential dangerous operations. Due to its high coverage and abilities to generate effective testcases, dynamic symbolic execution has attracted wide attention in the research community. However, the success of dynamic symbolic execution is limited due to complex program logic and its difficulty to handle large symbolic data. In our experiments we found that phase-related features of a program often prevents dynamic symbolic execution from exploring deep paths. On the basis of this discovery, we proposed a novel symbolic execution technology guided by program phase characteristics. Compared to KLEE, the most well-known symbolic execution approach, our method is capable of covering more code and discovering more bugs. We designed and implemented pbSE system, which was used to test several commonly used tools and libraries in Linux. Our results showed that pbSE on average covers code twice as much as what KLEE does, and we discovered 21 previously unknown vulnerabilities by using pbSE, out of which 7 are assigned CVE IDs.

Algebraic fault analysis (AFA), which combines algebraic cryptanalysis with fault attacks, has represented serious threats to the security of lightweight block ciphers. Inspired by an earlier framework for the analysis of side-channel attacks presented at EUROCRYPT 2009, a new generic framework is proposed to analyze and evaluate algebraic fault attacks on lightweight block ciphers. We interpret AFA at three levels: 1) the target; 2) the adversary; and 3) the evaluator. We describe the capability of an adversary in four parts: 1) the fault injector; 2) the fault model describer; 3) the cipher describer; and 4) the machine solver. A formal fault model is provided to cover most of current fault attacks. Different strategies of building optimal equation set are also provided to accelerate the solving process. At the evaluator level, we consider the approximate information metric and the actual security metric. These metrics can be used to guide adversaries, cipher designers, and industrial engineers. To verify the feasibility of the proposed framework, we make a comprehensive study of AFA on an ultra-lightweight block cipher called LBlock. Three scenarios are exploited, which include injecting a fault to encryption, to key scheduling, or modifying the round number or counter. Our best results show that a single fault injection is enough to recover the master key of LBlock within the affordable complexity in each scenario. To verify the generic feature of the proposed framework, we apply AFA to three other block ciphers, i.e., Data Encryption Standard, PRESENT, and Twofish. The results demonstrate that our framework can be used for different ciphers with different structures.

GOST is a well-known block cipher as the official encryption standard for the Russian Federation. A special feature of GOST is that its eight S-boxes can be secret. However, most of the researches on GOST assume that the design of these S-boxes is known. In this paper, the security of GOST against side-channel attacks is examined with algebraic fault analysis (AFA), which combines the algebraic cryptanalysis with the fault attack. Three AFAs on GOST, which have different attack goals in different scenarios, are investigated. The results show that 8 fault injections are required to recover the secret key when the full design of GOST is known, which is less than 64 fault injections required in previous work. 64 fault injections are required to recover the eight unknown S-boxes assuming the key is known. 270 fault injections are required to recover the key and the eight S-boxes when both are unknown. The results prove that AFA is very effective and keeping some components in a cipher secret cannot guarantee its security against fault attacks.

Algebraic side-channel attack (ASCA) is a typical technique that relies on a general solver to solve the equations of a cipher and its side-channel leaks. It falls under analytical side-channel attack and can recover the entire key at once. Many ASCAs are proposed against the AES, and they utilize the Gröbner basis-based, SAT-based, or optimizer-based solver. The advantage of the general solver approach is its generic feature, which can be easily applied to different cryptographic algorithms. The disadvantage is that it is difficult to take into account the specialized properties of the targeted cryptographic algorithms. The results vary depending on what type of solver is used, and the time complexity is quite high when considering the error-tolerant attack scenarios. Thus, we were motivated to find a new approach that would lessen the influence of the general solver and reduce the time complexity of ASCA. This paper proposes a new analytical side-channel attack on AES by exploiting the incomplete diffusion feature in one AES round. We named our technique incomplete diffusion analytical side-channel analysis (IDASCA). Different from previous ASCAs, IDASCA adopts a specialized approach to recover the secret key of AES instead of the general solver. Extensive attacks are performed against the software implementation of AES on an 8-bit microcontroller. Experimental results show that: 1) IDASCA can exploit the side-channel leaks in all AES rounds using a single power trace; 2) it has less time complexity and more robustness than previous ASCAs, especially when considering the error-tolerant attack scenarios; and 3) it can calculate the reduced key search space of AES for the given amount of side-channel leaks. IDASCA can also interpret the mechanism behind previous ASCAs on AES from a quantitative perspective, such as why ASCA can work under unknown plaintext/ciphertext scenarios and what are the extreme cases in ASCAs.

This paper proposes a fault analysis technique on LED by combining algebraic cryptanalysis and differential fault analysis (DFA). The technique is called algebraic differential fault analysis (ADFA). In ADFA on LED, we use DFA to deduce the possible fault differences of the correct and faulty S-Box input in the last round, and convert them into algebraic equations. We then combine the equation set of LED with the injected fault and use the CryptoMiniSat solver to recover the secret key. Our experiments show that, on a common PC, ADFA can succeed on LED under the nibble-based fault model within three minutes and with only one fault injection, which is more efficient than previous DFA work. To evaluate DFA on LED, we first propose an improved evaluation algorithm of DFA, then provide a modified ADFA approach to compute the solutions for the secret key. The results are more accurate than previous work. We also successfully extend ADFA on LED to other fault models using a single fault injection, where traditional DFAs are difficult to launch.

ABSTRACT Delay tolerant networks (DTNs) are new emerging technologies aiming to solve communication issues in challenged network environments. In such networks, any real‐time interactive key agreement protocol does not work due to the intermittent connectivity and long time delay in message round trips. In the context of specific applications, such as single hop authentication, manual public key exchange is the most direct method because single hop authentication can be achieved by holding a small part of node's public key. To evaluate how many public keys should be maintained by each node to achieve a high propagation speed while single hop authentication scheme is used, in this paper, we proposed a security message propagation model for DTNs formed by vehicles where the extended graph theory and rumor spreading terminology in complex networks were harnessed. We find that holding 8–10 public keys by each node is optimal. And decay rate threshold is 0.16 under which message can be disseminated throughout the whole network. Copyright © 2011 John Wiley & Sons, Ltd.