Biblio

Cyber incidents are occurring every day using various attack strategies. Deploying security solutions with strong configurations will reduce the attack surface and improve the forensic readiness, but will increase the security overhead and cost. In contrast, using moderate or low security configurations will reduce that overhead, but will inevitably decrease the investigation readiness. To avoid the use of cost-prohibitive approaches in developing forensic-ready systems, we present in this paper a game theoretic approach for deploying an investigation-ready infrastructure. The proposed game is a non-cooperative two-player game between an adaptive cyber defender that uses a cognitive security solution to increase the investigation readiness and reduce the attackers' untraceability, and a cyber attacker that wants to execute non-provable attacks with a low cost. The cognitive security solution takes its strategic decision, mainly based on its ability to make forensic experts able to differentiate between provable identifiable, provable non-identifiable, and non-provable attack scenarios, starting from the expected evidences to be generated. We study the behavior of the two strategic players, looking for a mixed Nash equilibrium during competition and computing the probabilities of attacking and defending. A simulation is conducted to prove the efficiency of the proposed model in terms of the mean percentage of gained security cost, the number of stepping stones that an attacker creates and the rate of defender false decisions compared to two different approaches.

The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.

As the computer architecture technology evolves, communication protocols have been demanded not only having reliable security but also flexible functionality. Advanced cryptography has been expected as a new generation cryptography which suffices such the requirements. A pairing is one of the key technologies of the cryptography and the pairing has been known as having a substantial amount of construction parameters. Recently, the elliptic curve with embedding degree 14 is evaluated as one of the efficient curves for pairing. In the paper, we implement an optimal ate pairing on the elliptic curve by applying several variants of multiplication algorithms of extension field of degree 7 on multiple devices. The best multiplication algorithm among the candidates is derived. Besides, for efficient calculations, we propose a pseudo 7-sparse algorithm and a fast calculation method of final exponentiation. As a result, we discover the proper multiplication algorithm bases on the rate of addition and multiplications on several different computer platforms. Our proposed pseudo 7-sparse algorithm is approximately 1.54% faster than a regular algorithm on almost all tested platforms. Eventually, for the total execution time of pairing we record 9.33ms on Corei5-9500.

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

Compared with traditional centralized control strategies, the distributed control systems significantly improve the flexibility and expandability of an modular multilevel converter (MMC). However, the stability issue in the MMC distributed control system with the presence of control loop coupling interactions is rarely discussed in existing research works. This article is to improve the stability of an MMC distributed control system by inhibiting the control conflict due to the coupling interactions among control loops with incomplete control information. By modeling the MMC distributed control system, the control loop coupling interactions are analyzed and the essential cause of control conflict is revealed. Accordingly, a control parameter design principle is proposed to effectively suppress the disturbances from the targeted control conflict and improve the MMC system stability. The rationality of the theoretical analysis and the effectiveness of the control parameter design principle are confirmed by simulation and experimental results.

In this paper, a generic model for a differential stripline is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential stripline is used to take into consideration the frequency-dependent dielectric loss. In order to demonstrate the expandability of this approach, the methodology was applied to two differential pairs of striplines in the frequency range of 10 MHz to 20 GHz.

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.

Optimization plays an important role in many problems that expect the accurate output. Security of the data stored in remote servers purely based on secret key which is used for encryption and decryption purpose. Many secret key generation algorithms such as RSA, AES are available to generate the key. The key generated by such algorithms are need to be optimized to provide more security to your data from unauthorized users as well as from the third party auditors(TPA) who is going to verify our data for integrity purpose. In this paper a method to optimize the secret key by using cuckoo search algorithm (CSA) is proposed.

Provable Data Possession (PDP) is one of the data security techniques to make sure that the data stored in the cloud storage exists. In PDP, the integrity of the data stored in the cloud storage is probabilistically verified by the user or a third-party auditor. In the conventional PDP, the user creates the metadata used for audition. From the viewpoint of user convenience, it is desirable to be able to audit without operations other than uploading. In other words, the challenge is to provide a transparent PDP that verifies the integrity of files according to the general cloud storage system model so as not to add operations to users. We propose a scheme in which the cloud generates the metadata used during verification, and the user only uploads files. It is shown that the proposed scheme is resistant to the forgery of cloud proof and the acquisition of data by a third-party auditor.

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.

Cloud computing is an evolving technology that provides data storage and highly fast computing services at a very low cost. All data stored in the cloud is handled by their cloud service providers or the caretaker of the cloud. The data owner is concerned about the authenticity and reliability of the data stored in the cloud as the data owners. Data can be misappropriated or altered by any unauthorized user or person. This paper desire to suggest a secure public auditing scheme applying third party auditors to authenticate the privacy, reliability, and integrity of data stored in the cloud. This proposed auditing scheme composes the use of the AES-256 algorithm for encryption, SHA-512 for integrity check and RSA-15360 for public-key encryption. And perform data dynamics operation which deals with mostly insertion, deletion, and, modification.

With the rapid progress of informatization construction in power business, data resource has become the basic strategic resource of the power industry and innovative element in power production. The security protection of data in power business is particularly important in the informatization construction of power business. In order to implement data security protection, transparent encryption is one of the fifteen key technical standards in the Construction Guideline of the Standard Network Data Security System. However, data storage in the encrypted state is bound to affect the security audit of data to a certain extent. Based on this problem, this paper proposes a scheme to audit the sensitivity of the power business data under the protection of encryption to achieve an efficient sensitivity audit of ciphertext data with the premise of not revealing the decryption key or data information. Through a security demonstration, this paper fully proves that this solution is secure under the known plaintext attacks.

Due to the significant increase in the size of the internet and the number of users on this platform there has been a tremendous increase in load on various websites and web-based applications. This load is from the user end which causes unforeseen conditions which leads to unacceptable consequences such as crash or a data loss scenario at the webserver end. Therefore, there is a need to reduce the load on the server as well as the chances of network attacks that increase with the increased user base. The undue consequences such as data loss and server crash are caused due to two main reasons: the first one being an overload of users and the second due to an increased number of automatic programs or robots. A technique can be utilized to overcome this scenario by introducing a delay in the operation speed on the user end through the use of a CAPTCHA mechanism. Most of the classical approaches use a single method for the generation of the CAPTCHA, to overcome this proposed model uses the versatile image CAPTCHA generation mechanism. We have introduced a system that utilizes manualbased, face detection-based, colour based and random object insertion technique to generate 4 different random types of CAPTCHA. The proposed methodology implements a region of interest and convolutional neural networks to achieve the generation of the CAPTCHA effectively.

The resistance to attacks aimed to break CAPTCHA challenges and the effectiveness, efficiency and satisfaction of human users in solving them called usability are the two major concerns while designing CAPTCHA schemes. User-friendliness, universality, and accessibility are related dimensions of usability, which must also be addressed adequately. With recent advances in segmentation and optical character recognition techniques, complex distortions, degradations and transformations are added to text-based CAPTCHA challenges resulting in their reduced usability. The extent of these deformations can be decreased if some additional security mechanism is incorporated in such challenges. This paper proposes an additional security mechanism that can add an extra layer of protection to any text-based CAPTCHA challenge, making it more challenging for bots and scripts that might be used to attack websites and web applications. It proposes the use of hidden text-boxes for user entry of CAPTCHA string which serves as honeypots for bots and automated scripts. The honeypot technique is used to trick bots and automated scripts into filling up input fields which legitimate human users cannot fill in. The paper reports implementation of honeypot technique and results of tests carried out over three months during which form submissions were logged for analysis. The results demonstrated great effectiveness of honeypots technique to improve security control and usability of text-based CAPTCHA challenges.

Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.

Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.

Multiple techniques for modeling cybersecurity attacks and defense have been developed. The use of tree- structures as well as techniques proposed by several firms (such as Lockheed Martin's Cyber Kill Chain, Microsoft's STRIDE and the MITRE ATT&CK frameworks) have all been demonstrated. These approaches model actions that can be taken to attack or stopped to secure infrastructure and other resources, at different levels of detail.This paper builds on prior work on using the Blackboard Architecture for cyberwarfare and proposes a generalized solution for modeling framework/paradigm-based attacks that go beyond the deployment of a single exploit against a single identified target. The Blackboard Architecture Cyber Command Entity attack Route (BACCER) identification system combines rules and facts that implement attack type determination and attack decision making logic with actions that implement reconnaissance techniques and attack and defense actions. BACCER's efficacy to model examples of tree-structures and other models is demonstrated herein.

With the increasing interdependence of critical infrastructures, the probability of a specific infrastructure to experience a complex cyber-physical attack is increasing. Thus it is important to analyze the risk of an attack and the dynamics of its propagation in order to design and deploy appropriate countermeasures. The attack trees, commonly adopted to this aim, have inherent shortcomings in representing interdependent, concurrent and sequential attacks. To overcome this, the work presented here proposes a stochastic methodology using Petri Nets and Continuous Time Markov Chain (CTMC) to analyze the attacks, considering the individual attack occurrence probabilities and their stochastic propagation times. A procedure to convert a basic attack tree into an equivalent CTMC is presented. The proposed method is applied in a case study to calculate the different attack propagation characteristics. The characteristics are namely, the probability of reaching the root node & sub attack nodes, the mean time to reach the root node and the mean time spent in the sub attack nodes before reaching the root node. Additionally, the method quantifies the effectiveness of specific defenses in reducing the attack risk considering the efficiency of individual defenses.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

Although OpenFlow-based SDN networks make it easier to design and test new protocols, when you think of clean slate architectures, their use is quite limited because the parameterization of its flows resides primarily in TCP/IP protocols. Besides, despite the many benefits that SDN offers, some aspects have not yet been adequately addressed, such as management plane activities, network startup, and options for connecting the data plane to the control plane. Based on these issues and limitations, this work presents a bootstrap protocol for SDN-based networks, which allows, beyond the network topology discovery, automatic configuration of an inband control plane. The protocol is designed to act only on layer two, in an autonomous, distributed and deterministic way, with low overhead and has the intent to be the basement for the implementation of other management plane related activities. A formal specification of the protocol is provided. In addition, an analytical model was created to preview the number of required messages to establish the control plane. According to this model, the proposed protocol presents less overhead than similar de-facto protocols used to topology discovery in SDN networks.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1 instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.

Modern JavaScript applications extensively depend on third-party libraries. Especially for the Node.js platform, vulnerabilities can have severe consequences to the security of applications, resulting in, e.g., cross-site scripting and command injection attacks. Existing static analysis tools that have been developed to automatically detect such issues are either too coarse-grained, looking only at package dependency structure while ignoring dataflow, or rely on manually written taint specifications for the most popular libraries to ensure analysis scalability. In this work, we propose a technique for automatically extracting taint specifications for JavaScript libraries, based on a dynamic analysis that leverages the existing test suites of the libraries and their available clients in the npm repository. Due to the dynamic nature of JavaScript, mapping observations from dynamic analysis to taint specifications that fit into a static analysis is non-trivial. Our main insight is that this challenge can be addressed by a combination of an access path mechanism that identifies entry and exit points, and the use of membranes around the libraries of interest. We show that our approach is effective at inferring useful taint specifications at scale. Our prototype tool automatically extracts 146 additional taint sinks and 7 840 propagation summaries spanning 1 393 npm modules. By integrating the extracted specifications into a commercial, state-of-the-art static analysis, 136 new alerts are produced, many of which correspond to likely security vulnerabilities. Moreover, many important specifications that were originally manually written are among the ones that our tool can now extract automatically.

A time-delay switch (TDS) cyber attack is a deliberate attempt by malicious adversaries aiming at destabilizing a power system by impeding the communication signals to/from the centralized controller from/to the network sensors and generating stations that participate in the load frequency control (LFC). A TDS cyber attack can be targeting the sensing loops (transmitting network measurements to the centralized controller) or the control signals dispatched from the centralized controller to the governor valves of the generating stations. A resilient TDS control strategy is proposed and developed in this work that thwarts network instabilities that are caused by delays in the sensing loops, and control commands, and guarantees normal operation of the LFC mechanism. This will be achieved by augmenting the telemetered control commands with locally generated feedback control laws (i.e., “decentralized” control commands) taking measurements that are available and accessible at the power generating stations (locally) independent from all the telemetered signals to/from the centralized controller. Our objective is to devise a controller that is capable of circumventing all types of TDS and DoS (Denial of Service) cyber attacks as well as a broad class of False Data Injection (FDI) cyber attacks.

The impact of microarchitectural attacks in Personal Computers (PCs) can be further adapted to and observed in internetworked All Programmable System-on-Chip (AP SoC) platforms. This effort involves the access control or execution of Intellectual Property cores in the FPGA of an AP SoC Victim internetworked with an AP SoC Attacker via Internet Protocol (IP). Three conceptions of attacks were implemented: buffer overflow attack at the stack, return-oriented programming attack, and command-injection-based attack for dynamic reconfiguration in the FPGA. Indeed, a specific preventive countermeasure for each attack is proposed. The functionality of the countermeasures mainly comprises adapted words addition (stack protection) for the first and second attacks and multiple encryption for the third attack. In conclusion, the recommended countermeasures are realizable to counteract the implemented attacks.