Biblio

Updating the structure of attack graph templates based on real-time alerts from Intrusion Detection Systems (IDS), in an Industrial Control System (ICS) network, is currently done manually by security experts. But, a highly-connected smart power systems, that can inadvertently expose numerous vulnerabilities to intruders for targeting grid resilience, needs automatic fast updates on learning attack graph structures, instead of manual intervention, to enable fast isolation of compromised network to secure the grid. Hence, in this work, we develop a technique to first construct a prior Bayesian Attack Graph (BAG) based on a predefined threat model and a synthetic communication network for a cyber-physical power system. Further, we evaluate a few score-based and constraint-based structural learning algorithms to update the BAG structure based on real-time alerts, based on scalability, data dependency, time complexity and accuracy criteria.

The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner for improving overall performance. However, adversarial servers in a distributed computing system deliberately send erroneous data in order to affect the computation for their benefit. Computing Boolean functions is the key component of many applications of interest, e.g., classification problem, verification functions in the blockchain and the design of cryptographic algorithm. In this paper, we consider the problem of computing a Boolean function in which the computation is carried out distributively across several workers with particular focus on security against Byzantine workers. We note that any Boolean function can be modeled as a multivariate polynomial which can have high degree in general. Hence, the recently proposed Lagrange Coded Computing (LCC) can be used to simultaneously provide resiliency, security, and privacy. However, the security threshold (i.e., the maximum number of adversarial workers that can be tolerated) provided by LCC can be extremely low if the degree of the polynomial is high. Our goal is to design an efficient coding scheme which achieves the optimal security threshold. We propose two novel schemes called coded Algebraic normal form (ANF) and coded Disjunctive normal form (DNF). Instead of modeling the Boolean function as a general polynomial, the key idea of the proposed schemes is to model it as the concatenation of some linear functions and threshold functions. The proposed coded ANF and coded DNF outperform LCC by providing the security threshold which is independent of the polynomial's degree.

The concept of the adversary model has been widely applied in the context of cryptography. When designing a cryptographic scheme or protocol, the adversary model plays a crucial role in the formalization of the capabilities and limitations of potential attackers. These models further enable the designer to verify the security of the scheme or protocol under investigation. Although being well established for conventional cryptanalysis attacks, adversary models associated with attackers enjoying the advantages of machine learning techniques have not yet been developed thoroughly. In particular, when it comes to composed hardware, often being security-critical, the lack of such models has become increasingly noticeable in the face of advanced, machine learning-enabled attacks. This paper aims at exploring the adversary models from the machine learning perspective. In this regard, we provide examples of machine learning-based attacks against hardware primitives, e.g., obfuscation schemes and hardware root-of-trust, claimed to be infeasible. We demonstrate that this assumption becomes however invalid as inaccurate adversary models have been considered in the literature.

In this paper, we formulate a combinatorial optimization problem that aims to maximize the accuracy of a lower bound estimate of the probability of security of a multi-robot system (MRS), while minimizing the computational complexity involved in its calculation. Security of an MRS is defined using the well-known control theoretic notion of left invertiblility, and the probability of security of an MRS can be calculated using binary decision diagrams (BDDs). The complexity of a BDD depends on the number of disjoint path sets considered during its construction. Taking into account all possible disjoint paths results in an exact probability of security, however, selecting an optimal subset of disjoint paths leads to a good estimate of the probability while significantly reducing computation. To deal with the dynamic nature of MRSs, we introduce two methods: (1) multi-point optimization, a technique that requires some a priori knowledge of the topology of the MRS over time, and (2) online optimization, a technique that does not require a priori knowledge, but must construct BDDs while the MRS is operating. Finally, our approach is validated on an MRS performing a rendezvous objective while exchanging information according to a noisy state agreement process.

This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators-intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.

The security of logic locking has been called into question by various attacks, especially a Boolean satisfiability (SAT) based attack, that exploits scan access in a working chip. Among other techniques, a robust design-for-security (DFS) architecture was presented to restrict any unauthorized scan access, thereby, thwarting the SAT attack (or any other attack that relies on scan access). Nevertheless, in this work, we successfully break this technique by recovering the secret key despite the lack of scan access. Our security analysis on a few benchmark circuits protected by the robust DFS architecture demonstrates the effectiveness of our attack; on average 95% of the key bits are correctly recovered, and almost 100% in most cases. To overcome this and other prevailing attacks, we propose a defense by making fundamental changes to the robust DFS technique; the new defense can withstand all logic locking attacks. We observe, on average, lower area overhead ( 1.65%) than the robust DFS design ( 5.15%), and similar test coverage ( 99.88%).

Currently, video surveillance systems play an important role in ensuring the safety of citizens, their property, etc., which greatly contributes to the reduction of crime. Due to the high intrinsic value and/or high efficiency of their use for the prevention and detection of crimes, they themselves often become the objects of illegal actions (theft, damage). The main purpose of video surveillance systems is to provide continuous visual monitoring of the situation at a particular facility or territory, as well as event registration. The breakdown of the camera is detected by the loss of signal in the control center. However, the absence of a signal for reasons other than these can also be caused by an accident on the power line, a communication channel break, software or hardware breakdown of the camera itself. In this regard, there is a problem of determining the exact cause of the lack of signal and, consequently, the need for a rapid response to it. The paper proposes an approach of video surveillance arrangement according to their main functional purpose, as well as their ability to monitor each other. Based on this approach, a mathematical model of the choice of locations and conditions of location of video surveillance equipment from a set of potentially acceptable as a problem of nonlinear Boolean programming is developed. This model maximizes the functionality of the video surveillance system, taking into account the importance of areas and objects of surveillance with restrictions on the number of video surveillance of each type, the nature of the terrain and existing buildings. An algorithm for solving this problem is proposed.

In recent years, with the rapid development of Internet of things, RFID tags have been widely used, in due to the chip used in radio frequency identification (RFID) tags is more demanding for resources, which also brings a great threat to the safety performance of cryptographic algorithms in differential power analysis (DPA). For this purpose, it is necessary to study the LED lightweight cryptography algorithm of RFID tags in the Internet of things, so as to explore a lightweight and secure cryptographic algorithm which can be applied to RFID Tags. In this paper, through the combination of Piccolo cryptographic algorithm and the new DPA protection technology threshold, we propose a LED lightweight cryptographic algorithm which can be applied to the RFID tag of the Internet of things. With the help of improve d exhaustive search and Boolean expression reconstruction, the two methods share the implementation of the S -box and the InvS-box, thereby effectively solves the burr threat problem of the S-box and the InvS-box in the sharing implementation process, the security performance of the algorithm is evaluated by the DPA attack of FPGA. The results show that the algorithm can achieve lightweight and security performance at the same time, can effectively meet the light and security requirements of RFID tag chip of Internet of things for cryptographic algorithms.

Logic locking, and Integrated Circuit (IC) Camouflaging, are techniques that try to hide the design of an IC from a malicious foundry or end-user by introducing ambiguity into the netlist of the circuit. While over the past decade an array of such techniques have been proposed, their security has been constantly challenged by algorithmic attacks. This may in part be due to a lack of formally defined notions of security in the first place, and hence a lack of security guarantees based on long-standing hardness assumptions. In this paper we take a formal approach. We define the problem of circuit locking (cL) as transforming an original circuit to a locked one which is ``unintelligable'' without a secret key (this can model camouflaging and split-manufacturing in addition to logic locking). We define several notions of security for cL under different adversary models. Using long standing results from computational learning theory we show the impossibility of exponentially approximation-resilient locking in the presence of an oracle for large classes of Boolean circuits. We then show how exact-recovery-resiliency and a more relaxed notion of security that we coin ``best-possible'' approximation-resiliency can be provably guaranteed with polynomial overhead. Our theoretical analysis directly results in stronger attacks and defenses which we demonstrate through experimental results on benchmark circuits.

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis to generate new parity features and rank them based on their highest Fourier coefficients. We demonstrate on real enterprise data sets that the engineered features enhance the performance of a wide range of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall, while sacrificing no more than 0.47% in accuracy. We also observe better isolation of malicious clusters, when performing clustering on engineered features. In general, a small number of engineered features achieve higher performance than raw data features according to our metrics of interest. Our feature engineering method also retains interpretability, an important consideration in cyber security applications.

A technique of finding a set of sequential circuit nodes in which Trojan Circuits (TC) may be implanted is suggested. The technique is based on applying the precise (not heuristic) random estimations of internal node observability and controllability. Getting the estimations we at the same time derive and compactly represent all sequential circuit full states (depending on input and state variables) in which of that TC may be switched on. It means we obtain precise description of TC switch on area for the corresponding internal node v. The estimations are computed with applying a State Transition Graph (STG) description, if we suppose that TC may be inserted out of the working area (out of the specification) of the sequential circuit. Reduced Ordered Binary Decision Diagrams (ROBDDs) for the combinational part and its fragments are applied for getting the estimations by means of operations on ROBDDs. Techniques of masking TCs are proposed. Masking sub-circuits overhead is appreciated.

Almost all commodity IT devices include firmware and software components from non-US suppliers, potentially introducing grave vulnerabilities to homeland security by enabling cyber-attacks via flaws injected into these devices through the supply chain. However, determining that a given device is free of any and all implementation flaws is computationally infeasible in the general case; hence a critical part of any vetting process is prioritizing what kinds of flaws are likely to enable potential adversary goals. We present Theseus, a four-year research project sponsored by the DARPA VET program. Theseus will provide technology to automatically map and explore the firmware/software (FW/SW) architecture of a commodity IT device and then generate attack scenarios for the device. From these device attack scenarios, Theseus then creates a prioritized checklist of FW/SW components to check for potential vulnerabilities. Theseus combines static program analysis, attack graph generation algorithms, and a Boolean satisfiability solver to automate the checklist generation workflow. We describe how Theseus exploits analogies between the commodity IT device problem and attack graph generation for networks. We also present a novel approach called Component Interaction Mapping to recover a formal model of a device's FW/SW architecture from which attack scenarios can be generated.

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the response and recovery engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. The RRE applies attack-response trees (ART) to analyze undesired system-level security events within host computers and their countermeasures using Boolean logic to combine lower level attack consequences. In addition, the RRE accounts for uncertainties in intrusion detection alert notifications. The RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. To support network-level multiobjective response selection and consider possibly conflicting network security properties, we employ fuzzy logic theory to calculate the network-level security metric values, i.e., security levels of the system's current and potentially future states in each stage of the game. In particular, inputs to the network-level game-theoretic response selection engine, are first fed into the fuzzy system that is in charge of a nonlinear inference and quantitative ranking of the possible actions using its previously defined fuzzy rule set. Consequently, the optimal network-level response actions are chosen through a game-theoretic optimization process. Experimental results show that the RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 500 nodes.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.