Biblio

More and more organizations are today using the cloud for their business as a quite convenient alternative to in-house solutions for storing, processing, and managing data. Cloud-based solutions are then permeating almost all aspects of business organizations, resulting appealing also for functions that, already in-house, may result sensitive or security critical, and whose enforcement in the cloud requires then particular care. In this paper, we provide an approach for securely relying on cloud-based services for the enforcement of Internal Controls and Audit (ICA) functions for corporate governance. Our approach is based on the use of selective encryption and of tags to provide a level of self-protection to data and for enabling only authorized parties to access data and perform operations on them, providing privacy and integrity guarantees, as well as accountability and non-repudiation.

Modern control flow attacks circumvent existing defense mechanisms to transfer the program control to attacker chosen malicious code in the program, leaving application vulnerable to attack. Advanced attacks such as Return-Oriented Programming (ROP) attack and its variants, transfer program execution to gadgets (code-snippet that ends with return instruction). The code space to generate gadgets is large and attacks using these gadgets are Turing-complete. One big challenge to harden the program against ROP attack is to confine gadget selection to a limited locations, thus leaving the attacker to search entire code space according to payload criteria. In this paper, we present a novel approach to label the nodes of the Control-Flow Graph (CFG) of a program such that labels of the nodes on a valid control flow edge satisfy a Hamming distance property. The newly encoded CFG enables detection of illegal control flow transitions during the runtime in the processor pipeline. Experimentally, we have demonstrated that the proposed Control Flow Integrity (CFI) implementation is effective against control-flow hijacking and the technique can reduce the search space of the ROP gadgets upto 99.28%. We have also validated our technique on seven applications from MiBench and the proposed labeling mechanism incurs no instruction count overhead while, on average, it increases instruction width to a maximum of 12.13%.

Aiming at the requirements of network access control, illegal outreach control, identity authentication, security monitoring and application system access control of information network, an integrated network access and behavior control model based on security policy is established. In this model, the network access and behavior management control process is implemented through abstract policy configuration, network device and application server, so that management has device-independent abstraction, and management simplification, flexibility and automation are improved. On this basis, a general framework of policy-based access and behavior management control is established. Finally, an example is given to illustrate the method of device connection, data drive and fusion based on policy-based network access and behavior management control.

The facial recognition time by time takes more importance, due to the extend kind of applications it has, but it is still challenging when faces big variations in the characteristics of the biometric data used in the process and especially referring to the transportation of information through the internet in the internet of things context. Based on the systematic review and rigorous study that supports the extraction of the most relevant information on this topic [1], a software architecture proposal which contains basic security requirements necessary for the treatment of the data involved in the application of facial recognition techniques, oriented to an IoT environment was generated. Concluding that the security and privacy considerations of the information registered in IoT devices represent a challenge and it is a priority to be able to guarantee that the data circulating on the network are only accessible to the user that was designed for this.

In recent days, cloud computing is one of the emerging fields. It is a platform to maintain the data and privacy of the users. To process and regulate the data with high security, the access control methods are used. The cloud environment always faces several challenges such as robustness, security issues and so on. Conventional methods like Cipher text-Policy Attribute-Based Encryption (CP-ABE) are reflected in providing huge security, but still, the problem exists like the non-existence of attribute revocation and minimum efficient. Hence, this research work particularly on the attribute-based mechanism to maximize efficiency. Initially, an objective coined out in this work is to define the attributes for a set of users. Secondly, the data is to be re-encrypted based on the access policies defined for the particular file. The re-encryption process renders information to the cloud server for verifying the authenticity of the user even though the owner is offline. The main advantage of this work evaluates multiple attributes and allows respective users who possess those attributes to access the data. The result proves that the proposed Data sharing scheme helps for Revocation under a fine-grained attribute structure.

The rapid evolution of the Information and Communications Technology (ICT) services transforms the conventional electrical grid into a new paradigm called Smart Grid (SG). Even though SG brings significant improvements, such as increased reliability and better energy management, it also introduces multiple security challenges. One of the main reasons for this is that SG combines a wide range of heterogeneous technologies, including Internet of Things (IoT) devices as well as Supervisory Control and Data Acquisition (SCADA) systems. The latter are responsible for monitoring and controlling the automatic procedures of energy transmission and distribution. Nevertheless, the presence of these systems introduces multiple vulnerabilities because their protocols do not implement essential security mechanisms such as authentication and access control. In this paper, we focus our attention on the security issues of the IEC 60870-5-104 (IEC-104) protocol, which is widely utilized in the European energy sector. In particular, we provide a SCADA threat model based on a Coloured Petri Net (CPN) and emulate four different types of cyber attacks against IEC-104. Last, we used AlienVault's risk assessment model to evaluate the risk level that each of these cyber attacks introduces to our system to confirm our intuition about their severity.

Proposed an approach for information security breaches detection in distributed control systems based on prediction of multidimensional time series formed of sensor and actuator data.

The authors carry out the analysis of the process of modeling threats to information security of automated process control systems. Basic principles of security threats model formation are considered. The approach to protection of automated process control systems based on the Shtakelberg game in a strategic form was modeled. An abstract mathematical model of information security threats to automated process control systems was developed. A formalized representation of a threat model is described, taking into account an intruder's potential. Presentation of the process of applying the described threat model in the form of a continuous Deming-Shewhart cycle is proposed.

Dynamometer cards can reflect different down-hole working conditions of sucker rod pumping wells. It has great significances to realize multiple fault diagnosis for actual oilfield production. In this paper, the extension theory is used to build a matter-element model to describe the fault diagnosis problem of the sucker rod pumping wells. The correlation function is used to calculate the correlation degree between the diagnostic fault and many standard fault types. The diagnosed sample and many possible fault types are divided into different combinations according to the correlation degree; the F-statistics of each combination is calculated and the “unbiased transformation” is used to find the mean of interval vectors. Larger F-statistics means greater differences within the faults classification; and the minimum F-statistics reflects the real multiple fault types. Case study shows the effectiveness of the proposed method.

Supervisory control and data acquisition (SCADA) systems are becoming increasingly susceptible to cyber-physical attacks on both physical and cyber layers of critical information infrastructure. Failure Mode and Effects Analysis (FMEA) have been widely used as a structured method to prioritize all possible vulnerable areas (failure modes) for design review of security of information systems. However, traditional RPN based FMEA has some inherent problems. Besides, there is a lacking of application of FMEA for security in SCADAs under vague and uncertain environment. Thus, the main purpose of this study was to propose a new evaluation model, which not only intends to recover above mentioned problems, but also intends to evaluate, prioritize and correct security risk of SCADA system's threat modes. A numerical case study was also conducted to demonstrate that the proposed new evaluation model is not only capable of addressing FMEA's inherent problems but also is best suited for a semi-quantitative high level analysis of a secure SCADA's failure modes in the early design phases.

As technology is developing exponentially and the world is moving towards automation, the resources have to be transferred through the internet which requires routers to connect networks and forward bundles (information). Due to the vulnerability of routers the data and resources have been hacked. The vulnerability of routers is due to minimum authentication to the network shared, some technical attacks on routers, leaking of passwords to others, single passwords. Based on the study, the solution is to maximize authentication of the router by embedding an application that monitors the user entry based on MAC address of the device, the password is frequently changed and that encrypted password is sent to a user and notifies the admin about the changes. Thus, these routers provide high-level security to the forward data through the internet.

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (\textbackslashtextgreater99.9%), nearly zero false positives and short (\textbackslashtextless; 360ms) latency.

At present, the network architecture is based on the TCP/IP protocol and node communications are achieved by the IP address and identifier of the node. The IP address in the network remains basically unchanged, so it is more likely to be attacked by network intruder. To this end, it is important to make periodic dynamic hopping in a specific address space possible, so that an intruder fails to obtain the internal network address and grid topological structure in real time and to continue to perform infiltration by the building of a new address space layout randomization system on the basis of SDN from the perspective of an attacker.

This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.

In enterprise environments, the amount of managed assets and vulnerabilities that can be exploited is staggering. Hackers' lateral movements between such assets generate a complex big data graph, that contains potential hacking paths. In this vision paper, we enumerate risk-reduction security requirements in large scale environments, then present the Agile Security methodology and technologies for detection, modeling, and constant prioritization of security requirements, agile style. Agile Security models different types of security requirements into the context of an attack graph, containing business process targets and critical assets identification, configuration items, and possible impacts of cyber-attacks. By simulating and analyzing virtual adversary attack paths toward cardinal assets, Agile Security examines the business impact on business processes and prioritizes surgical requirements. Thus, handling these requirements backlog that are constantly evaluated as an outcome of employing Agile Security, gradually increases system hardening, reduces business risks and informs the IT service desk or Security Operation Center what remediation action to perform next. Once remediated, Agile Security constantly recomputes residual risk, assessing risk increase by threat intelligence or infrastructure changes versus defender's remediation actions in order to drive overall attack surface reduction.

The paper discusses the architectural, algorithmic and computing aspects of creating and operating a class of expert system for managing technological safety of an enterprise, in conditions of a large flow of diagnostic variables. The algorithm for finding a faulty technological chain uses expert information, formed as a set of evidence on the influence of diagnostic variables on the correctness of the technological process. Using the Dempster-Schafer trust function allows determining the overall probability measure on subsets of faulty process chains. To combine different evidence, the orthogonal sums of the base probabilities determined for each evidence are calculated. The procedure described above is converted into the rules of the knowledge base production. The description of the developed prototype of the expert system, its architecture, algorithmic and software is given. The functionality of the expert system and configuration tools for a specific type of production are under discussion.

For aerospace FPGA software products, traditional simulation method faces severe challenges to verify product requirements under complicated scenarios. Given the increasing maturity of formal verification technology, this method can significantly improve verification work efficiency and product design quality, by expanding coverage on those "blind spots" in product design which were not easily identified previously. Taking UART communication as an example, this paper proposes several critical points to use formal verification for asynchronous communication protocol. Experiments and practices indicate that formal verification for asynchronous communication protocol can effectively reduce the time required, ensure a complete verification process and more importantly, achieve more accurate and intuitive results.

A parallel brute force attack on RC4 algorithm based on FPGA (Field Programmable Gate Array) with an efficient style has been presented. The main idea of this design is to use number of forecast keying methods to reduce the overall clock pulses required depended to key searching operation by utilizes on-chip BRAMs (block RAMs) of FPGA for maximizing the total number of key searching unit with taking into account the highest clock rate. Depending on scheme, 32 key searching units and main controller will be used in one Xilinx XC3S1600E-4 FPGA device, all these units working in parallel and each unit will be searching in a specific range of keys, by comparing the current result with the well-known cipher text if its match the found flag signal will change from 0 to 1 and the main controller will receive this signal and stop the searching operation. This scheme operating at 128-MHz clock frequency and gives us key searching speed of 7.7 × 106 keys/sec. Testing all possible keys (40-bits length), requires only around 39.5h.

Intellectual Property (IP) infringement including piracy and overproduction have emerged as significant threats in the semiconductor supply chain. Key-based obfuscation techniques (i.e., logic locking) are widely applied to secure legacy IP from such attacks. However, the fundamental question remains open whether an attacker is allowed an exponential amount of time to seek correct key or could it be useful to lock out the design in a non-destructive manner after several incorrect attempts. In this paper, we address this question with a robust design lockout technique. Specifically, we perform comparisons on obfuscation logic output that reflects the condition (correct or incorrect) of the applied key without changing the system behavior. The proposed approach, when combined with key obfuscation (logic locking) technique, increases the difficulty of reverse engineering key obfuscated RTL module. We provide security evaluation of DLockout against three common side-channel attacks followed by a quantitative assessment of the resilience. We conducted a set of experiments on four datapath intensive IPs and one crypto core for three different key lengths (32-, 64-, and 128-bit) under the typical design corner. On average, DLockout incurs negligible area, power, and delay overheads.

In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.

Edge computing requires a flexible choice of data-processing and rapidly computation performed at the edge of networks. We proposed an edge computing platform with container-based virtualization technology. In the platform, data-processing instances are modularized and deployed to edge nodes suitable for user requirements with keeping the data-processing flows within wide area network. This paper reports the platform controller and the process modules implemented to realize the secure and flexible edge computing platform.

The growing trend toward information technology increases the amount of data travelling over the network links. The problem of detecting anomalies in data streams has increased with the growth of internet connectivity. Software-Defined Networking (SDN) is a new concept of computer networking that can adapt and support these growing trends. However, the centralized nature of the SDN design is challenged by the need for an efficient method for traffic monitoring against traffic anomalies caused by misconfigured devices or ongoing attacks. In this paper, we propose a new model for traffic behavior monitoring that aims to ensure trusted communication links between the network devices. The main objective of this model is to confirm that the behavior of the traffic streams matches the instructions provided by the SDN controller, which can help to increase the trust between the SDN controller and its covered infrastructure components. According to our preliminary implementation, the behavior monitoring unit is able to read all traffic information and perform a validation process that reports any mismatching traffic to the controller.

Aiming at the shortcomings of the traditional network active security threat model that cannot continuously control the threat process, a network active security threat model based on offensive and defensive differential game is constructed. The attack and defense differential game theory is used to define the parameters of the network active security threat model, on this basis, the network security target is determined, the network active security threat is identified by the attack defense differential equation, and finally the network active security threat is quantitatively evaluated, thus construction of network active security threat model based on offensive and defensive differential game is completed. The experimental results show that compared with the traditional network active security threat model, the proposed model is more feasible in the attack and defense control of the network active security threat process, and can achieve the ideal application effect.

The Software Defined Networks (SDN) paradigm, often referred to as a radical new idea in networking, promises to dramatically simplify network management by enabling innovation through network programmability. However, notable security issues, such as app-to-control threats, remain a significant concern that impedes SDN from being widely adopted. To cope with those app-to-control threats, this paper proposes a solution to securely deploy valid network applications while protecting the SDN controller against the injection of the malicious application. This problem is mitigated by proposing a novel SDN architecture, dubbed SENAD, which splits the well-known SDN controller into: (1) a data plane controller (DPC), and (2) an application plane controller (APC), to secure this latter by design. The role of the DPC is dedicated for interpreting the network rules into OpenFlow entries and maintaining the communication with the data plane. The role of the APC, however, is to provide a secured runtime for deploying the network applications, including authentication, access control, resource isolation, control, and monitoring applications. We show that this approach can easily shield against any deny of service, caused for instance by the resource exhaustion attack or the malicious command injection, that is caused by the co-existence of a malicious application on the controller's runtime. The evaluation of our architecture shows that the packet\_in messages take less than 5 ms to be delivered from the data plane to the application plane on the long range.

The evolution of the microelectronics manufacturing industry is characterized by increased complexity, analysis, integration, distribution, data sharing and collaboration, all of which is enabled by the big data explosion. This evolution affords a number of opportunities in improved productivity and quality, and reduced cost, however it also brings with it a number of risks associated with maintaining security of data systems. The International Roadmap for Devices and System Factory Integration International Focus Team (IRDS FI IFT) determined that a security technology roadmap for the industry is needed to better understand the needs, challenges and potential solutions for security in the microelectronics industry and its supply chain. As a first step in providing this roadmap, the IFT conducted a security survey, soliciting input from users, suppliers and OEMs. Preliminary results indicate that data partitioning with IP protection is the number one topic of concern, with the need for industry-wide standards as the second most important topic. Further, the "fear" of security breach is considered to be a significant hindrance to Advanced Process Control efforts as well as use of cloud-based solutions. The IRDS FI IFT will endeavor to provide components of a security roadmap for the industry in the 2018 FI chapter, leveraging the output of the survey effort combined with follow-up discussions with users and consultations with experts.