Timing Attacks Research Articles

Creation of an encryption algorithm resistant to attacks through side channels of leakage

The paper is devoted to.the development of an encryption algorithm resistant to leakage through side channels The objectives of the work were the development and computational-experimental justification of the algorithm. Materials and methods. A new algorithm of encryption resistant to attacks through side channels is proposed.The algorithm resistance to attacks on the basis of numerical experiments is analyzed.As a result of analyzing statistical data before and after modifications of the algorithm it was found that the developed algorithm is resistant to time and energy attacks. Results of the study.The main result of the work is the development of an encryption algorithm resistant to attacks through side leakage channels. The resilience of the algorithm to attacks was confirmed by analyzing the statistical data before and after modifications to the algorithm. The comparisons concluded that the developed algorithm is resistant to timing attack and power consumption attack. Discussion and Conclusion. The resulting algorithm can be applied to counter groups of attacks on cryptographic devices, in which the attacker tries to obtain any information useful to him, through the physical features of the implemented cryptosystem.

Enabling secure modern web browsers against cache-based timing attacks

Enabling secure modern web browsers against cache-based timing attacks

Machine Recognition of DDoS Attacks Using Statistical Parameters

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

High-performance montgomery modular multiplier with NTT and negative wrapped convolution

Modular multiplication plays a crucial role in modern cryptography. Montgomery modular multiplication(MMM), one of the most classic and practical modular multiplication algorithms, has been widely used in cryptographic algorithms such as RSA, Diffie–Hellman algorithm, and Elliptic Curve Cryptography. In this paper, we incorporate negative wrapped convolution (NWC) into the FFT-based Montgomery modular multiplication to avoid the issue of zero-padding and use carry-save arithmetics for parallel computation. By utilizing coefficient pairs (pos_part and neg_part), we reconstruct the final result and eliminate the restrictions imposed by nega-cyclic parts. Moreover, Karatsuba-like algorithm is introduced for building fine-grained large integer multipliers. We have modified the parameter specifications for our design to meet requirements from diverse application scenarios. We implement the design on Xilinx Virtex-7 FPGA under different conditions and compare the results with the state-of-the-art MMM designs. The comparisons confirm that our design has the following characteristics: low latency for process, competitive area-latency-product(ALP), efficient DSP usage, and constant delay, which enhances security against timing attacks.

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

With the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems (STS/IDS) are one of the effective defense tools against these potential attacks that are constantly increasing and diversifying. The ultimate goal is to train these systems with various artificial intelligence methods, to detect subsequent attacks in real time and to take the necessary precautions. In this study, classical feature selection methods and Frequent Item Set Mining were used in feature selection in a hybrid model, and it was aimed to classify network traffic data for normal and attack by using many machine learning methods, including Logistic Regression, with the final features obtained. The method uses a data set originally containing 85 features to make a decision while making this classification. These attributes are extracted using CICFlowMeter from a PCAP file where network traffic is recorded. The results show that the proposed method in the study classifies more than 225000 records in the data set with a success rate of 97.68%.

Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response

Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%.

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.

Online Distributed Schedule Randomization to Mitigate Timing Attacks in Industrial Control Systems

Industrial control systems (ICSs) consist of a large number of control applications that are associated with periodic real-time flows with hard deadlines. To facilitate large-scale integration, remote control, and co-ordination, wireless sensor and actuator networks form the main communication framework in most ICSs. Among the existing wireless sensor and actuator network protocols, WirelessHART is the most suitable protocol for real-time applications in ICSs. The communications in a WirelessHART network are time-division multiple access based. To satisfy the hard deadlines of the real-time flows, the schedule in a WirelessHART network is pre-computed. The same schedule is repeated over every hyperperiod (i.e., lowest common multiple of the periods of the flows). However, a malicious attacker can exploit the repetitive behavior of the flow schedules to launch timing attacks (e.g., selective jamming attacks). To mitigate timing attacks, we propose an online distributed schedule randomization strategy that randomizes the time-slots in the schedules at each network device without violating the flow deadlines, while ensuring the closed-loop control stability. To increase the extent of randomization in the schedules further, and to reduce the energy consumption of the system, we incorporate a period adaptation strategy that adjusts the transmission periods of the flows depending on the stability of the control loops at runtime. We use Kullback-Leibler divergence and prediction probability of slots as two metrics to evaluate the performance of our proposed strategy. We compare our strategy with an offline centralized schedule randomization strategy. Experimental results show that the schedules generated by our strategy are 10% to 15% more diverse and 5% to 10% less predictable on average compared to the offline strategy when the number of base schedules and keys vary between 4 and 6 and 12 and 32, respectively, under all slot utilization (number of occupied slots in a hyperperiod). On incorporating period adaptation, the divergence in the schedules reduceat each period increase with 46% less power consumption on average.

Machine Learning Algorithms for Cloud Computing Security: A Review

: Cloud computing has revolutionized the way businesses and individuals access and manage IT resources. However, cloud computing also introduces new security challenges. Cloud-based systems are often more complex and distributed than traditional on-premises systems, making them more vulnerable to attack.Machine learning (ML) is a powerful tool that can be used to analyze cloud computing attacks and improve cloud security. ML algorithms can be trained on large datasets of attack data to learn the patterns and characteristics of different types of attacks. Once trained, ML models can be used to detect new attacks in real time and recommend mitigation strategies. This paper reviews the state of the art in using ML to analyze cloud computing attacks. It discusses the different ways that ML can be used to improve cloud security, including developing intrusion detection systems (IDS), anomaly detection systems, and attack forecasting systems. The paper also discusses the challenges of using ML for cloud security, such as the need for large and high-quality training data and the need to adapt ML models to new and emerging threats.The paper concludes by discussing the future of using ML for cloud security. It argues that ML is a key technology for improving cloud security in the face of increasingly sophisticated and targeted attacks.

Routine Extubation in the Operating Room After Isolated Coronary Artery Bypass

BackgroundThe benefits of fast-track extubation in the intensive care unit (ICU) after cardiac surgery are well established. Although extubation in the operating room (OR) is safe in carefully selected patients, widespread use of this strategy in cardiac surgery remains unproven. This study was designed to evaluate perioperative outcomes with OR vs ICU extubation in patients undergoing nonemergency, isolated coronary artery bypass grafting (CABG). MethodsThe Society of Thoracic Surgeons (STS) data for all single-center patients who underwent nonemergency isolated CABG over a 6-year interval were analyzed. Perioperative morbidity and mortality with ICU vs OR extubation were compared. ResultsBetween January 1, 2017 and December 31, 2022, 1397 patients underwent nonemergency, isolated CABG; 891 (63.8%) of these patients were extubated in the ICU, and 506 (36.2%) were extubated in the OR. Propensity matching resulted in 414 pairs. In the propensity-matched cohort, there were no differences between the 2 groups in incidence of reintubation, reoperation for bleeding, total operative time, stroke or transient ischemic attack, renal failure, or 30-day mortality. OR-extubated patients had shorter ICU hours (14 hours vs 20 hours; P < .0001), shorter postoperative hospital length of stay (3 days vs 5 days; P < .0001), a greater likelihood of being discharged directly to home (97.3% vs 89.9%; P < .0001), and a lower 30-day readmission rate (1.7% vs 4.1%; P = .04). ConclusionsRoutine extubation in the OR is a feasible and safe strategy for a broad spectrum of patients after nonemergency CABG, with no increase in perioperative morbidity or mortality. Wider adoption of routine OR extubation for nonemergency CABG is indicated.

Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree

Billions of gadgets are already online, making the IoT an essential aspect of daily life. However, the interconnected nature of IoT devices also leaves them open to cyber threats. The quantity and sophistication of cyber assaults aimed against Internet of Things (IoT) systems have skyrocketed in recent years. This paper proposes a next-generation cyber attack prediction framework for IoT systems. The framework uses the multi-class support vector machine (SVM) and the improved CHAID decision tree machine learning methods. IoT traffic is classified using a multi-class support vector machine to identify various types of attacks. The SVM model is then optimized with the help of the CHAID decision tree, which prioritizes the attributes most relevant to the categorization of attacks. The proposed framework was evaluated on a real-world dataset of IoT traffic. The findings demonstrate the framework's ability to categorize attacks accurately. The framework may determine which attributes are most crucial for attack categorization to enhance the SVM model's precision. The proposed technique focuses on network traffic characteristics that can be signs of cybersecurity threats on IoT networks and affected Network nodes. Selected feature vectors were also created utilizing the elements acquired on every IoT console. The evaluation results on the Multistep Cyber-Attack Dataset (MSCAD) show that the proposed CHAID decision tree can significantly predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction is essential in managing cyber attacks in real-time communication. Because of its efficiency and scalability, the model may be used to forecast cyber attacks in real time, even in massive IoT installations. Because of its computing efficiency, it can make accurate predictions rapidly, allowing for prompt detection and action. By locating possible entry points for attacks and mitigating them, the framework helps strengthen the safety of IoT systems.

Look-up the Rainbow: Table-based Implementation of Rainbow Signature on 64-bit ARMv8 Processors

The Rainbow Signature Scheme is one of the finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) standardization competition, but failed to win because it has lack of stability in the parameter selection. It is the only signature candidate based on a multivariate quadratic equation. Rainbow signatures have smaller signature sizes compared with other post-quantum cryptography candidates. However, they require expensive tower-field based polynomial multiplications. In this article, we propose an efficient implementation of Rainbow signatures using a look-up table–based multiplication method. The polynomial multiplications in Rainbow signatures are performed on the 𝔽 16 field, which is divided into sub-fields 𝔽 4 and 𝔽 2 under the tower-field method. To accelerate the multiplication process on target processors, we propose a look-up table–based tower-field multiplication technique. In 𝔽 16 , all values are expressed in 4-bit data format and can be implemented using a 256-byte look-up table access. The implementation uses the TBL and TBX instructions of the 64-bit ARMv8 target processor. For Rainbow III and Rainbow V, they are computed on the 𝔽 256 field using an additional 16-byte table instead of creating a new look-up table. The proposed technique uses the vector registers of 64-bit ARMv8 processors and can calculate 16 result values with a single instruction. We also proposed implementations that are resistant to timing attacks. There are two types of implementations. The first one is the cache side-attack resistant implementation, which utilizes the 128-byte cache lines of the M1 processor. In this implementation, cache misses do not occur, and cache hits always occur. The second type is the constant-time implementation. This method takes a step-by-step approach to finding the required look-up table value and ensures that the same number of accesses is made regardless of which look-up table value is called. This implementation is designed to be constant-time, meaning it does not leak timing information. Our experiments on modern Apple M1 processors showed up to 428.73× and 114.16× better performance for finite field multiplications and Rainbow signatures schemes, respectively, compared with previous reference implementations. To the best of our knowledge, this proposed Rainbow implementation is the first optimized Rainbow implementation for 64-bit ARMv8 processors.

ADTCD: An Adaptive Anomaly Detection Approach Toward Concept Drift in IoT

The data collected by sensors is streaming data in the Internet of Things(IoT). Although existing deep learningbased anomaly detection methods generally perform well on static data, they struggle to respond timely to streaming data after distribution changes. However, streaming data suffers from conceptual drift due to the highly dynamic nature of IoT. In network security, concept drift-oriented anomaly detection is a crucial task, because it can adjust the model to adapt to the latest data, and detect attacks in time. Existing streaming anomaly detection methods are confronted with some challenges, including of the latency of model updates, the uneven importance of new data, and the self-poisoning due to model self-updates. To tackle the above challenges, we propose a knowledge distillation based adaptive anomaly detection model towards concept-drift, ADTCD. ADTCD transfers the knowledge of the teacher model to the student model and only updates the student model to reduce the delay. We construct an algorithm of dynamically adjusting model parameters, which dynamically adjusts model weights through local inference on new samples, in order to improving the models responsiveness to new distribution data, meanwhile solving the problem of uneven importance of new data. In additions, we adopt a one-class support vector-based outlier removal method to tackle the self-poisoning problem. In comprehensive experiments on seven high-dimensional datasets, ADTCD achieves an AUC improvement of 12.46% compared to the state-of-the-art streaming anomaly detection methods. Our future direction will focus on exploring the concept drift problem using methods beyond autoencoders.

Towards a metrics suite for evaluating cache side-channel vulnerability: Case studies on an open-source RISC-V processor

Caches are key microarchitectural components of modern processors to improve memory access speed. However, attackers can readily implement timing side-channel attacks by exploiting the inherent time difference between cache hits and misses, which brings serious security threats to computer systems. In recent years, cache attacks have achieved fruitful consequences regarding attack techniques, targets, and platforms. At the same time, in order to measure the cache vulnerability, researchers have been looking at verifying the security policies and have proposed a variety of evaluation metrics. However, there are certain limitations in the current metrics, such as harsh assumptions, the distinguishing ability cannot be maintained all the time, the false positive and false negative noises can not be explicitly quantified, other tools are needed, and the verification environment is difficult to reproduce. To face these issues, we proposed a set of evaluation metrics, which includes the highest score (HS), minimum rounds to disclosure (MRD), and key score scissors differential (KSSD); we give the formal expressions in practice and theoretical, respectively. In order to facilitate the accurate reproduction of the experimental environment and results, we build a dual-core RISC-V bare-metal system based on the open-source framework Chipyard. On this basis, we conduct comprehensive evaluations to exploit vulnerabilities within the RISC-V cache architecture to verify the validity of the metrics suite. Furthermore, we compared the metrics with the commonly used success rate (SR) and guessing entropy (GE). The comparison results show that our proposed metrics suite can accurately depict the effects of attacks. Moreover, KSSD can maintain discrimination and converge quickly; MRD has more practical guidance; HS can specifically describe false negative noises. Finally, we perform a theoretical evaluation of AES algorithms with different T-table implementations using the proposed metrics, analyze the system's false positive and false negative noises, and suggest how the number of cache evictions can be determined under the random replacement policy.

FTODefender: An efficient flow table overflow attacks defending system in SDN

Software-Defined Networking (SDN) is a promising architecture that disentangles the control plane from the data plane. A mainstream southbound protocol for controller-to-switch communication in SDN is OpenFlow. In an OpenFlow-enabled network, SDN switches are connected to the logically centralized control plane, which issues control messages to direct packet forwarding and processing in the data plane. Given that the finite capacity of physical flow tables of switches, typically implemented with ternary content addressable memory (TCAM), attackers have the ability to carry out Denial-of-Service (DoS) attacks aimed at depleting the TCAM’s resources and causing an overflow of the flow table. In this paper, we propose FTODefender, a method combining eviction and cut-off attack sources to detect Low-rate Flow Table Overflow (LFTO) attacks and mitigate this kind of attacks in time. FTODefender consists of two modules: Detector and Mitigator. Detector periodically examines the flow table and extract four specific detection features. It then employs the trained CRITIC weights to calculate the detection score based on these extracted features to verify if the LFTO attack has happened. Mitigator computes the features for each flow and utilizes the LightGBM-LR classification model to identify attack flows among all flows. Then, the module evicts the malicious rules to mitigate attacks. Finally, Mitigator counts the occurrence of each source IP address in the eviction list, determines the attacker IP based on a predefined threshold, and issues a flow rule to drop all packets originating from the attacker’s IPs to sever the attack sources. Simulations show the effectiveness of FTODefender in mitigating the LFTO attacks, which proves that FTODefender is a practical solution to flow table overflow attacks.

Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.

AVX-TSCHA: Leaking information through AVX extensions in commercial processors

Modern x86 processors support an AVX instruction set to boost performance. However, this extension set may also cause security issues. We discovered that there are vulnerable properties in the implementation of the masked load/store instructions. First, these instructions can suppress exceptions caused by invalid or inaccessible memory access. Second, the execution time of these instructions leaks the current state of the page mappings, permissions, and TLB states.Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the significance of our side-channel attack by showing User and Kernel ASLR breaks on the recent Intel and AMD processors in various environments, including cloud computing systems (Amazon AWS, Google GCP, and Microsoft Azure), an SGX enclave (a fine-grained ASLR break), and major OSes (Linux, Windows, and macOS). Our attack can identify the Linux kernel's base address in 0.29 ms as well as those of loaded kernel modules in 2.24 ms, with a near-zero error rate. We further demonstrate that our attack can be used to infer user behavior, such as mouse movements and data transmissions over the network. Our evaluation results on multiple mobile, desktop, and server processors (a total of 26 Intel and AMD CPUs) show that 1) the AVX timing side-channel works on the vast majority of Intel processors (from the Sandy Bridge microarchitecture) as well as AMD processors (from the Zen microarchitecture onward) and 2) our KASLR breaks are very fast and reliable. To the best of our knowledge, our attack is the first to demonstrate a KASLR break on both the recent Intel Alder Lake and AMD Zen 3 CPUs. We highlight that more robust isolation or fine-grained randomization should be adopted to mitigate our presented attacks successfully.

Intrusion detection in cloud computing based on time series anomalies utilizing machine learning

The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.

The role of artificial intelligence in information security: a review of its application in the cyber industry

At present, the implementation of information technologies in organizations is increasingly common, since it benefits the development and automation of the processes they carry out; however, it is important to take into account that the incorporation of these technological solutions may affect the security of your systems and the information contained in them. This is where artificial intelligence can help organizations improve their ability to protect their sensitive information against possible cybersecurity threats, while reducing the workload on the teams in charge of it; however, executing this strategy comes with its own drawbacks, which organizations implementing it will need to address. The objective of this article is to determine and analyze the benefits and limitations offered by artificial intelligence applied to information security, for which a systematic review of the articles and papers published in the Scopus, Scielo and Alicia databases was carried out. during the last 5 years (2019 - 2023), with the intention of giving priority to current publications on the subject. The results obtained show us that Artificial Intelligence (AI) provides benefits in terms of detecting attacks in real time and preventing threats, having as limitations its implementation complexity, the need for broad access to data, the distrust generated by its independent interpretation and the lack of privacy towards user data.

Revisiting the Software-Efficient Stream Ciphers RCR-64 and RCR-32

Abstract The synchronous stream ciphers RCR-64 and RCR-32 designed by Sekar, Paul and Preneel are strengthened variants of the ciphers TPy and TPypy (designed by Biham and Seberry), respectively. The RCR ciphers have remained unbroken since they were published in 2007. In this paper, we present arguments that not only support the designers’ security claims but suggest, in general, that the ciphers are secure against several classes of cryptanalytic attacks. We find that the ciphers are best used with 256-bit keys and 384-bit IVs. We also suggest ways to protect software implementations of the RCR ciphers against (cache-)timing and processor flag attacks. Our performance evaluation suggests that the protected implementation of the RCR-64 encrypts long messages at speeds comparable to some of the fastest stream ciphers available today. Consequently, we find that the RCR ciphers may be well suited for PC-based applications in general and streaming audio / video applications in particular. This is the first paper to present a detailed study on the security and performance of the RCR ciphers.