Biblio

Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly complicate the adoption of common hardening techniques. Combined with the irregular and involved nature of embedded patch management, this results in prolonged vulnerability exposure windows and vulnerabilities that are relatively easy to exploit. Considering the sensitive and critical nature of many embedded systems, this situation merits significant improvement. In this work, we present the first quantitative study of exploit mitigation adoption in 42 embedded operating systems, showing the embedded world to significantly lag behind the general-purpose world. To improve the security of deeply embedded systems, we subsequently present μArmor, an approach to address some of the key gaps identified in our quantitative analysis. μArmor raises the bar for exploitation of embedded memory corruption vulnerabilities, while being adoptable on the short term without incurring prohibitive extra performance or storage costs.

While there has been considerable research on making power grid Supervisory Control and Data Acquisition (SCADA) systems resilient to attacks, the problem of transitioning these technologies into deployed SCADA systems remains largely unaddressed. We describe our experience and lessons learned in deploying an intrusion-tolerant SCADA system in two realistic environments: a red team experiment in 2017 and a power plant test deployment in 2018. These experiences resulted in technical lessons related to developing an intrusion-tolerant system with a real deployable application, preparing a system for deployment in a hostile environment, and supporting protocol assumptions in that hostile environment. We also discuss some meta-lessons regarding the cultural aspects of transitioning academic research into practice in the power industry.

Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.

The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

Distributed applications cannot assume that their security policies will be enforced on untrusted hosts. Trusted execution environments (TEEs) combined with cryptographic mechanisms enable execution of known code on an untrusted host and the exchange of confidential and authenticated messages with it. TEEs do not, however, establish the trustworthiness of code executing in a TEE. Thus, developing secure applications using TEEs requires specialized expertise and careful auditing. This paper presents DFLATE, a core security calculus for distributed applications with TEEs. DFLATE offers high-level abstractions that reflect both the guarantees and limitations of the underlying security mechanisms they are based on. The accuracy of these abstractions is exhibited by asymmetry between confidentiality and integrity in our formal results: DFLATE enforces a strong form of noninterference for confidentiality, but only a weak form for integrity. This reflects the asymmetry of the security guarantees of a TEE: a malicious host cannot access secrets in the TEE or modify its contents, but they can suppress or manipulate the sequence of its inputs and outputs. Therefore DFLATE cannot protect against the suppression of high-integrity messages, but when these messages are delivered, their contents cannot have been influenced by an attacker.

Good programming languages provide helpful abstractions for writing secure code, but the security properties of the source language are generally not preserved when compiling a program and linking it with adversarial code in a low-level target language (e.g., a library or a legacy application). Linked target code that is compromised or malicious may, for instance, read and write the compiled program's data and code, jump to arbitrary memory locations, or smash the stack, blatantly violating any source-level abstraction. By contrast, a fully abstract compilation chain protects source-level abstractions all the way down, ensuring that linked adversarial target code cannot observe more about the compiled program than what some linked source code could about the source program. However, while research in this area has so far focused on preserving observational equivalence, as needed for achieving full abstraction, there is a much larger space of security properties one can choose to preserve against linked adversarial code. And the precise class of security properties one chooses crucially impacts not only the supported security goals and the strength of the attacker model, but also the kind of protections a secure compilation chain has to introduce. We are the first to thoroughly explore a large space of formal secure compilation criteria based on robust property preservation, i.e., the preservation of properties satisfied against arbitrary adversarial contexts. We study robustly preserving various classes of trace properties such as safety, of hyperproperties such as noninterference, and of relational hyperproperties such as trace equivalence. This leads to many new secure compilation criteria, some of which are easier to practically achieve and prove than full abstraction, and some of which provide strictly stronger security guarantees. For each of the studied criteria we propose an equivalent “property-free” characterization that clarifies which proof techniques apply. For relational properties and hyperproperties, which relate the behaviors of multiple programs, our formal definitions of the property classes themselves are novel. We order our criteria by their relative strength and show several collapses and separation results. Finally, we adapt existing proof techniques to show that even the strongest of our secure compilation criteria, the robust preservation of all relational hyperproperties, is achievable for a simple translation from a statically typed to a dynamically typed language.

A permissioned blockchain platform comes with numerous assurances such as transaction confidentiality and system scalability to several organizations. Most permissioned blockchains rely on a Public-Key Infrastructure (PKI)as cryptographic tools to provide security services such as identity authentication and data confidentiality. Using PKI to validate transactions includes validating digital certificates of endorsement peers which creates an overhead in the system. Because public-key operations are computationally intensive, they limit the scalability of blockchain applications. Due to a large modulus size and expensive modular exponentiation operations, public-key operations such as RSA become slower than polynomial-based schemes, which involve a smaller modulus size and a less smaller number of modular multiplications. For instance, the 2048-bit RSA is approximately 15,728 times slower than a polynomial with a degree of 50 and 128-bit modulus size. In this paper, we propose a lightweight polynomial-based key management scheme in the context of a permissioned blockchain. Our scheme involves computationally less intensive polynomial evaluation operations such as additions and multiplications that result in a faster processing compared with public-key schemes. In addition, our proposed solution reduces the overhead of processing transactions and improves the system scalability. Security and performance analysis are provided in the paper.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

Denial of Service (DoS) attacks have been a serious security concern, as no service is, in principle, protected against them. Although a Dolev-Yao intruder with unlimited resources can trivially render any service unavailable, DoS attacks do not necessarily have to be carried out by such (extremely) powerful intruders. It is useful in practice and more challenging for formal protocol verification to determine whether a service is vulnerable even to resource-bounded intruders that cannot generate or intercept arbitrary large volumes of traffic. This paper proposes a novel, more refined intruder model where the intruder can only consume at most some specified amount of resources in any given time window. Additionally, we propose protocol theories that may contain timeouts and specify service resource usage during protocol execution. In contrast to the existing resource-conscious protocol verification models, our model allows finer and more subtle analysis of DoS problems. We illustrate the power of our approach by representing a number of classes of DoS attacks, such as, Slow, Asymmetric and Amplification DoS attacks, exhausting different types of resources of the target, such as, number of workers, processing power, memory, and network bandwidth. We show that the proposed DoS problem is undecidable in general and is PSPACE-complete for the class of resource-bounded, balanced systems. Finally, we implemented our formal verification model in the rewriting logic tool Maude and analyzed a number of DoS attacks in Maude using Rewriting Modulo SMT in an automated fashion.

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.

Peer-assisted content distribution networks (CDNs)have emerged to improve performance and reduce deployment costs of traditional, infrastructure-based content delivery networks. This is done by employing peer-to-peer data transfers to supplement the resources of the network infrastructure. However, these hybrid systems are vulnerable to accounting attacks in which the peers, or caches, collude with clients in order to report that content was transferred when it was not. This is a particular issue in systems that incentivize cache participation, because malicious caches may collect rewards from the content publishers operating the CDN without doing any useful work. In this paper, we introduce CAPnet, the first technique that lets untrusted caches join a peer-assisted CDN while providing a bound on the effectiveness of accounting attacks. At its heart is a lightweight cache accountability puzzle that clients must solve before caches are given credit. This puzzle requires colocating the data a client has requested, so its solution confirms that the content has actually been retrieved. We analyze the security and overhead of our scheme in realistic scenarios. The results show that a modest client machine using a single core can solve puzzles at a rate sufficient to simultaneously watch dozens of 1080p videos. The technique is designed to be even more scalable on the server side. In our experiments, one core of a single low-end machine is able to generate puzzles for 4.26 Tbps of bandwidth - enabling 870,000 clients to concurrently view the same 1080p video. This demonstrates that our scheme can ensure cache accountability without degrading system productivity.

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash as well as symmetric/asymmetric encryption and decryption algorithms ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

Designing a machine learning based network intrusion detection system (IDS) with high-dimensional features can lead to prolonged classification processes. This is while low-dimensional features can reduce these processes. Moreover, classification of network traffic with imbalanced class distributions has posed a significant drawback on the performance attainable by most well-known classifiers. With the presence of imbalanced data, the known metrics may fail to provide adequate information about the performance of the classifier. This study first uses Principal Component Analysis (PCA) as a feature dimensionality reduction approach. The resulting low-dimensional features are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. Furthermore, in this paper, we apply a Multi-Class Combined performance metric Combi ned Mc with respect to class distribution through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset. We were able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10 using PCA, while maintaining a high accuracy of 99.6% in multi-class and binary classification.

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations.

Mobile Ad hoc Network (MANET) is the collection of mobile devices which could change the locations and configure themselves without a centralized base point. Mobile Ad hoc Networks are vulnerable to attacks due to its dynamic infrastructure. The routing attacks are one among the possible attacks that causes damage to MANET. This paper gives a new method of risk aware response technique which is combined version the Dijkstra's shortest path algorithm and Destination Sequenced Distance Vector (DSDV) algorithm. This can reduce black hole attacks. Dijkstra's algorithm finds the shortest path from the single source to the destination when the edges have positive weights. The DSDV is an improved version of the conventional technique by adding the sequence number and next hop address in each routing table.

Risk assessment of cyber-physical systems, such as power plants, connected devices and IT-infrastructures has always been challenging: safety (i.e., absence of unintentional failures) and security (i. e., no disruptions due to attackers) are conditions that must be guaranteed. One of the traditional tools used to help considering these problems is attack trees, a tree-based formalism inspired by fault trees, a well-known formalism used in safety engineering. In this paper we define and implement the translation of attack-fault trees (AFTs) to a new extension of timed automata, called parametric weighted timed automata. This allows us to parametrize constants such as time and discrete costs in an AFT and then, using the model-checker IMITATOR, to compute the set of parameter values such that a successful attack is possible. Using the different sets of parameter values computed, different attack and fault scenarios can be deduced depending on the budget, time or computation power of the attacker, providing helpful data to select the most efficient counter-measure.

Textual deception constitutes a major problem for online security. Many studies have argued that deceptiveness leaves traces in writing style, which could be detected using text classification techniques. By conducting an extensive literature review of existing empirical work, we demonstrate that while certain linguistic features have been indicative of deception in certain corpora, they fail to generalize across divergent semantic domains. We suggest that deceptiveness as such leaves no content-invariant stylistic trace, and textual similarity measures provide a superior means of classifying texts as potentially deceptive. Additionally, we discuss forms of deception beyond semantic content, focusing on hiding author identity by writing style obfuscation. Surveying the literature on both author identification and obfuscation techniques, we conclude that current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text. We propose that future work in style transformation should pay particular attention to disallowing semantically drastic changes.

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical Systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin – to make systems adaptable to social policies – is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles.Abbreviations: CPS: Cyber-physical systems; H-CPS: Human-cyber-physical systems; CV: Connected vehicle; II: Industrial Internet; IoT: Internet of Things

Graphical passwords are most widely used as a mechanism for authentication in today's mobile computing environment. This methodology was introduced to enhance security element and overcome the vulnerabilities of textual passwords, pins, or other trivial password methodologies which were difficult to remember and prone to external attacks. There are many graphical password schemes that are proposed over time, however, most of them suffer from shoulder surfing and could be easily guessed which is quite a big problem. The proposed technique in this paper allows the user to keep the ease-to-use property of the pattern lock while minimizing the risk of shoulder surfing and password guessing. The proposed technique allows the user to divide a picture into multiple chunks and while unlocking, selecting the previously defined chunks results successfully in unlocking the device. This technique can effectively resist the shoulder surfing and smudge attacks, also it is resilient to password guessing or dictionary attacks. The proposed methodology can significantly improve the security of the graphical password system with no cost increase in terms of unlocking time.

Nowadays, secure transmission of multimedia files has become more significant concern with the evolution of technologies. Cryptography is the well-known technique to safeguard the files from various destructive hacks. In this work, a colour image encryption scheme is suggested using chaos and Deoxyribo Nucleic Acid (DNA) coding. The encryption scheme is carried out in two stages namely confusion and diffusion. As the first stage, chaos aided inter-planar row and column shuffling are performed to shuffle the image pixels completely. DNA coding and decoding operations then diffuse the resultant confused image with the help of eight DNA XOR rules. This confusion-diffusion process has achieved the entropy value equal to 7.9973 and correlation coefficient nearer to zero with key space of 10140. Various other analyses are also done to ensure the effectiveness of the developed algorithm. The results show that the proposed scheme can withstand different attacks and better than the recent state-of-art methods.

The applications of Radio Frequency Identification (RFID) technology has been rapidly developed to be used in different fields that require automatic identification of objects and managing information. The advantage of employing RFID systems is to facilitate automatic identification of objects from distance without any interaction with tagged objects and without using a line of sight as compared with barcode. However, security and privacy constitute a challenge to RFID system as RFID systems use the wireless communication. Many researchers have introduced elliptical curve cryptographic (ECC) solutions to the security and privacy in RFID system as an ideal cryptosystem to be implemented with RFID technology. However, most of these solutions do not have provide adequate protection. Moreover, in terms of integrity and confidentiality level, most of these authentication protocols still vulnerable to some of security and privacy attacks. Based on these facts, this paper proposes a mutual authentication protocol that aims at enhancing an existing RFID authentication protocol that suffers from tracking attack and man-in-the-middle attack (MITM). The enhancement is accomplished by improving the security and privacy level against MITM, tracking attack and other related attacks. The proposed protocol is dependent on use the elliptical curve version of Schnorr identification protocol in combination with Keccak hash function. This combination leads to enhance the confidentiality and integrity level of the RFID authentication system and increase the privacy protection.

Road accidents are challenging threat in the present scenario. In India there are 5, 01,423 road accidents in 2015. A day 400 hundred deaths are forcing to India to take car safety sincerely. The common cause for road accidents is driver's distraction. In current world the people are dominated by the tablet PC and other hand held devices. The VANET technology is a vehicle-to-vehicle communication; here the main challenge will be to deliver qualified communication during mobility. The paper proposes a standard new restricted lightweight authentication protocol utilizing key agreement theme for VANETs. Inside the planned topic, it has three sorts of validations: 1) V2V 2) V2CH; and 3) CH and RSU. Aside from this authentication, the planned topic conjointly keeps up mystery keys between RSUs for the safe communication. Thorough informal security analysis demonstrates the planned subject is skilled to guard different malicious attack. In addition, the NS2 Simulation exhibits the possibility of the proposed plan in VANET background.

Distributed Denial of Service(DDoS) attacks have become most important network security threat as the number of devices are connected to internet increases exponentially and reaching an attack volume approximately very high compared to other attacks. To make the network safe and flexible a new networking infrastructure such as Software Defined Networking (SDN) has come into effect, which relies on centralized controller and decoupling of control and data plane. However due to it's centralized controller it is prone to DDoS attacks, as it makes the decision of forwarding of packets based on rules installed in switch by OpenFlow protocol. Out of all different DDoS attacks, UDP (User Datagram Protocol) flooding constitute the most in recent years. In this paper, we have proposed an entropy based DDoS detection and rate limiting based mitigation for efficient service delivery. We have evaluated using Mininet as emulator and Ryu as controller by taking switch as OpenVswitch and obtained better result in terms of bandwidth utilization and hit ratio which consume network resources to make denial of service.