Internet of Things connects lots of small constrained devices to the Internet. As in any other environment, communication security is important and cryptographic algorithms are one of many elements that we use in order to keep messages secure. It is necessary to use algorithms that do not require high computational power, lightweight ciphers are therefore an ideal candidate for this purpose. Since these devices work in various environments, it is necessary to test security of implementations of cryptographic algorithms. In this paper, we explore a possibility of attacking an ultralightweight cipher PRESENT by using a multiple fault attack. Utilizing the Differential Fault Analysis technique, we were able to recover the secret key with two faulty encryptions and an exhaustive search of 216 remaining key bits. Our attack aims at four nibbles in the penultimate round of the cipher, causing faulty output in all nibbles of the output. We also provide a practical attack scenario by exploiting Hardware Trojan (HT) technique for the proposed fault injection in a Xilinx Spartan-6 FPGA.
The most efficient attack so far was proposed by @cite_20 . They used a 2-byte random fault model, attacking the algorithm state after round 28. For PRESENT-80, they needed two 2-byte faults and for PRESENT-128 they needed three 2-byte faults and an exhaustive search of @math on average.
@cite_20
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PRESENT is a hardware-optimized 64-bit lightweight block cipher which supports 80-and 128-bit secret keys. In this paper, we propose a differential fault analysis DFA on PRESENT-80 128. The proposed attack is based on a 2-byte random fault model. In detail, by inducing several 2-byte random faults in input registers after 28 rounds, our attack recovers the secret key of the target algorithm. From simulation results, our attacks on PRESENT-80 128 can recover the secret key by inducing only two and three 2-byte random faults, respectively. These are superior to known DFA results on them. In 2010, Tao Xie and Dengguo Feng [XF10] constructed the rst single-block collision for MD5 consisting of two 64-byte messages that have the same MD5 hash. Details of their attack, developed using what they call an evolutionary approach, has not been disclosed security reasons". Instead they have posted a challenge to the cryptology community to nd a new dierent single-block collision attack for MD5. This paper answers that challenge by presenting a single-block collision attack based on other message dierences together with an example colliding message pair. The attack is based on a new collision nding algorithm that exploits the low number of bitconditions in the rst round. It uses a new way to choose message blocks that satisfy bitconditions up to step 22 and additionally uses three known tunnels to correct bitconditions up to step 25. The attack has an average runtime complexity equivalent to 2 49:8 calls to MD5’s compression function. In this paper, we present two differential fault analyses on PRESENT-80 which is a lightweight block cipher. The first attack is a basic attack which induces a fault on only one bit of intermediate states, and we can obtain the last subkey of the block cipher, given 48 faulty cipher texts on average. The second attack can retrieve the master key of the block cipher, given 18 faulty cipher texts on average. In the latter attack, we assume that we can induce faults on a single nibble of intermediate states. Given those faulty cipher texts, the computational complexity of attacks is negligible. We present a new class of low-bandwidth denial of service attacks that exploit algorithmic deficiencies in many common applications' data structures. Frequently used data structures have "average-case" expected running time that's far more efficient than the worst case. For example, both binary trees and hash tables can degenerate to linked lists with carefully chosen input. We show how an attacker can effectively compute such input, and we demonstrate attacks against the hash table implementations in two versions of Perl, the Squid web proxy, and the Bro intrusion detection system. Using bandwidth less than a typical dialup modem, we can bring a dedicated Bro server to its knees; after six minutes of carefully chosen packets, our Bro server was dropping as much as 71 of its traffic and consuming all of its CPU. We show how modern universal hashing techniques can yield performance comparable to commonplace hash functions while being provably secure against these attacks. This article proposes an enhanced differential fault analysis (DFA) method named as fault-propagation pattern-based DFA (FPP-DFA). The main idea of FPP-DFA is using the FPP of the ciphertext difference to predict the fault location and the fault-propagation path. It shows that FPP-DFA is very effective on SPN structure block ciphers using bitwise permutation, which is applied to two block ciphers. The first is PRESENT with the substitution-permutation sequence. With the fault model of injecting one nibble fault into the r-2nd round, on average 8 and 16 faults can reduce the key search space of PRESENT-80 128 to 214.7 and 221.1, respectively. The second is PRINTcipher with the permutation-substitution sequence. For the first time, it shows that although the permutation of PRINTcipher is secret key dependent, FPP-DFA still works well on it. With the fault model of injecting one nibble fault into the r-2nd round, 12 and 24 effective faults can reduce the key search space of PRINTcipher-48 96 to 213.7 and 222.8, respectively. In this paper, we propose an algorithm for constructing guess-and-determine attacks on keystream generators and apply it to the cryptanalysis of the alternating step generator (ASG) and two its modifications (MASG and MASG0). In a guess-and-determine attack, we first “guess” some part of an initial state and then apply some procedure to determine, if the guess was correct and we can use the guessed information to solve the problem, thus performing an exhaustive search over all possible assignments of bits forming a chosen part of an initial state. We propose to use in the “determine” part the algorithms for solving Boolean satisfiability problem (SAT). It allows us to consider sets of bits with nontrivial structure. For each such set it is possible to estimate the runtime of a corresponding guess-and-determine attack via the Monte-Carlo method, so we can search for a set of bits yielding the best attack via a black-box optimization algorithm augmented with several SAT-specific features. We constructed and implemented such attacks on ASG, MASG, and MASG0 to prove that the constructed runtime estimations are reliable. We show, that the constructed attacks are better than the trivial ones, which imply exhaustive search over all possible states of the control register, and present the results of experiments on cryptanalysis of ASG and MASG MASG0 with total registers length of 72 and 96, which have not been previously published in the literature. This paper presents a family of bitmap algorithms that address the problem of counting the number of distinct header patterns (flows) seen on a high-speed link. Such counting can be used to detect DoS attacks and port scans and to solve measurement problems. Counting is especially hard when processing must be done within a packet arrival time (8 ns at OC-768 speeds) and, hence, may perform only a small number of accesses to limited, fast memory. A naive solution that maintains a hash table requires several megabytes because the number of flows can be above a million. By contrast, our new probabilistic algorithms use little memory and are fast. The reduction in memory is particularly important for applications that run multiple concurrent counting instances. For example, we replaced the port-scan detection component of the popular intrusion detection system Snort with one of our new algorithms. This reduced memory usage on a ten minute trace from 50 to 5.6 MB while maintaining a 99.77 probability of alarming on a scan within 6 s of when the large-memory algorithm would. The best known prior algorithm (probabilistic counting) takes four times more memory on port scan detection and eight times more on a measurement application. This is possible because our algorithms can be customized to take advantage of special features such as a large number of instances that have very small counts or prior knowledge of the likely range of the count. Fooling deep neural networks with adversarial input have exposed a significant vulnerability in current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems. The main contribution of this research is the proposal of a generic framework which can be used to attack any ASR system, even if it's internal working is hidden. During the un-targeted attacks, the Word Error Rates (WER) of the ASR degrades from 0.5 to 5.4, indicating the potency of our approach. In targeted attacks, our solution reaches a WER of 2.14. In both attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97. Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. In both cases, optimization-based attack algorithms can achieve relatively low distortions and high attack success rates. However, they usually suffer from poor time and query complexities, thereby limiting their practical usefulness. In this work, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on the non-convex Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an @math convergence rate. The empirical results of attacking Inception V3 model and ResNet V2 model on the ImageNet dataset also verify the efficiency and effectiveness of the proposed algorithms. More specific, our proposed algorithms attain the highest attack success rate in both white-box and black-box attacks among all baselines, and are more time and query efficient than the state-of-the-art. An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and NASNet-A. However, all these models are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy by 1.8 (72.4 vs. 70.6 ) and 23 faster speed than MobileNet, the state-of-the-art efficient architecture. Meanwhile, PeleeNet is only 66 of the model size of MobileNet. We then propose a real-time object detection system by combining PeleeNet with Single Shot MultiBox Detector (SSD) method and optimizing the architecture for fast speed. Our proposed detection system, named Pelee, achieves 76.4 mAP (mean average precision) on PASCAL VOC2007 and 22.4 mAP on MS COCO dataset at the speed of 23.6 FPS on iPhone 8 and 74 FPS on NVIDIA TX2. The result on COCO outperforms YOLOv2 in consideration of a higher precision, 13.6 times lower computational cost and 11.3 times smaller model size. The code and models are open sourced. Modern Byzantine fault-tolerant state machine replication (BFT) protocols involve about 20,000 lines of challenging C++ code encompassing synchronization, networking and cryptography. They are notoriously difficult to develop, test and prove. We present a new abstraction to simplify these tasks. We treat a BFT protocol as a composition of instances of our abstraction. Each instance is developed and analyzed independently. To illustrate our approach, we first show how our abstraction can be used to obtain the benefits of a state-of-the-art BFT protocol with much less pain. Namely, we develop AZyzzyva, a new protocol that mimics the behavior of Zyzzyva in best-case situations (for which Zyzzyva was optimized) using less than 24 of the actual code of Zyzzyva. To cover worst-case situations, our abstraction enables to use in AZyzzyva any existing BFT protocol, typically, a classical one like PBFT which has been tested and proved correct. We then present Aliph, a new BFT protocol that outperforms previous BFT protocols both in terms of latency (by up to 30 ) and throughput (by up to 360 ). The development of Aliph required two new instances of our abstraction. Each instance contains less than 25 of the code needed to develop state-of-the-art BFT protocols. So far many software countermeasures against fault attacks have been proposed. However, most of them are tailored to a specific cryptographic algorithm or focus on securing the processed data only. In this work we present a generic and elegant approach by using a highly fault secure algebraic structure. This structure is compatible to finite fields and rings and preserves its error detection property throughout addition and multiplication. Additionally, we introduce a method to generate a fingerprint of the instruction sequence. Thus, it is possible to check the result for data corruption as well as for modifications in the program flow. This is even possible if the order of the instructions is randomized. Furthermore, the properties of the countermeasure allow the deployment of error detection as well as error diffusion. We point out that the overhead for the calculations and for the error checking within this structure is reasonable and that the transformations are efficient. In addition we discuss how our approach increases the security in various kinds of fault scenarios. We investigate the cost of Grover’s quantum search algorithm when used in the context of pre-image attacks on the SHA-2 and SHA-3 families of hash functions. Our cost model assumes that the attack is run on a surface code based fault-tolerant quantum computer. Our estimates rely on a time-area metric that costs the number of logical qubits times the depth of the circuit in units of surface code cycles. As a surface code cycle involves a significant classical processing stage, our cost estimates allow for crude, but direct, comparisons of classical and quantum algorithms. Since the first publication of a successful practical two-fault attack on protected CRT-RSA surprisingly little attention was given by the research community to an ensuing new challenge. The reason for it seems to be two-fold. One is that generic higher order fault attacks are very difficult to model and thus finding robust countermeasures is also difficult. Another reason may be that the published experiment was carried out on an outdated 8 bit microcontroller and thus was not perceived as a serious threat to create a sense of urgency in addressing this new menace. In this paper we describe two-fault attacks on protected CRT-RSA implementations running on an advanced 32 bit ARM Cortex M3 core. To our knowledge, this is the first practical result of two fault laser attacks on a protected cryptographic application. Considering that laser attacks are much more accurate in targeting a particular variable, the significance of our result cannot be overlooked. In August 2015, SHA-3 Standard was published as the next generation of Secure Hashing Algorithm. Several optimized implementations of SHA-3 on a 24-core processor with software and hardware co-design are proposed in this paper. For software designs, a 4-core mapping scheme with shared-memory reduces the delay time by exploring the internal parallelism of the algorithm; the scheme of 5 cores with packet router improves flexibility; and 24-core mapping with circuit router fully explores the parallelism with higher energy efficiency. Hardware approaches: specific instructions and novel latch-based accelerators, are proposed to improve performance and energy efficiency. The three software mappings provide a speedup of 3.1, 3.6 and 19.6, respectively. Almost 2 times better performance and energy efficiency are obtained with three specific instructions. The implementation of 4 latch-based accelerators achieves a throughput of 81.9 Gbps at 850MHz with 3.7 pJ bit energy consumption per bit. The best known cryptanalytic attack on McEliece's public-key cryptosystem based on algebraic coding theory is to repeatedly select k bits at random from an n-bit ciphertext vector, which is corrupted by at most t errors, in hope that none of the selected k bits are in error until the cryptanalyst recovers the correct message. The method of determining whether the recovered message is the correct one has not been throughly investigated. In this paper, we suggest a systematic method of checking, and describe a generalized version of the cryptanalytic attack which reduces the work factor sigdicantly (factor of 211 for the commonly used example of n=1024 Goppa code case). Some more improvements are also given. We also note that these cryptanalytic algorithms can be viewed as generalized probabilistic decoding algorithms for any linear error correcting codes. Abstract We present simple randomized algorithms for the fault detection problem : Given a specification in the form of a deterministic finite state machine A and an implementation machine B , determine whether B is equal to A . If A has n states and p inputs, then in randomized polynomial time we can construct with high probability a checking sequence of length O ( pn 4 log n ), i.e., a sequence that detects all faulty machines with at most n states. Better bounds can be obtained in certain cases. The techniques generalize to partially specified finite state machines. Deep autoregressive sequence-to-sequence models have demonstrated impressive performance across a wide variety of tasks in recent years. While several common architecture classes including recurrent, convolutional, and self-attention networks make different trade-offs between the amount of computation needed per layer and the length of the critical path at training time, inference for novel inputs still remains an inherently sequential process. We propose a novel blockwise parallel decoding scheme that takes advantage of the fact that some architectures can score sequences in sublinear time. By generating predictions for multiple time steps at once then backing off to the longest prefix validated by the scoring model, we can substantially improve the speed of greedy decoding without compromising performance. When tested on state-of-the-art self-attention models for machine translation and image super-resolution, our approach achieves iteration reductions of up to 2x over a baseline greedy decoder with no loss in quality. Relaxing the acceptance criterion and fine tuning model parameters allows for reductions of up to 7x in exchange for a slight decrease in performance. Our fastest models achieve a 4x speedup in wall-clock time. Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or performing gradient estimation. We introduce GenAttack, a gradient-free optimization technique that uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches. Against MNIST and CIFAR-10 models, GenAttack required roughly 2,126 and 2,568 times fewer queries respectively, than ZOO, the prior state-of-the-art black-box attack. In order to scale up the attack to large-scale high-dimensional ImageNet models, we perform a series of optimizations that further improve the query efficiency of our attack leading to 237 times fewer queries against the Inception-v3 model than ZOO. Furthermore, we show that GenAttack can successfully attack some state-of-the-art ImageNet defenses, including ensemble adversarial training and non-differentiable or randomized input transformations. Our results suggest that evolutionary algorithms open up a promising area of research into effective black-box attacks. We consider a fault tolerance broadcast network of n processors each holding one bit of information. The goal is to compute a given Boolean function on the n bits. In each step, a processor may broadcast one bit of information. Each listening processor receives the bit that was broadcasted with error probability bounded by a fixed constant spl epsi . The errors in different steps, as well as for different receiving processors in the same step, are mutually independent. The protocols that are considered in this model are oblivious protocols: At each step, the processors that broadcast are fixed in advanced and independent of the input and the outcome of previous steps. The primal complexity measure in this model is the total number of broadcasts that is performed by the protocol. We present here the first linear complexity protocols for several classes of Boolean functions, including the OR function, functions that have O(l)-minterm (maxterm) size, functions that have linear size AC sub 0 formulae and some other functions. This answer an open question of Yao (1997), considering this fault tolerance model of El-Gamal (1984) and Gallager (1988).