Biblio

Cloud computing facilitates the access of applications and data from any location by using any device with an internet connection. It enables multiple applications and users to access the same data resources. Cloud based information sharing is a technique that allows researchers to communicate and collaborate, that leads to major new developments in the field. It also enables users to access data over the cloud easily and conveniently. Privacy, authenticity and confidentiality are the three main challenges while sharing data in cloud. There are many methods which support secure data sharing in cloud environment such as Attribute Based Encryption(ABE), Role Based Encryption, Hierarchical Based Encryption, and Identity Based Encryption. ABE provides secure access control mechanisms for integrity. It is classified as Key Policy Attribute Based Encryption(KP-ABE) and Ciphertext Policy Attribute Based Encryption(CP-ABE) based on access policy integration. In KPABE, access structure is incorporated with user's private key, and data are encrypted over a defined attributes. Moreover, in CPABE, access structure is embedded with ciphertext. This paper reviews CP-ABE methods that have been developed so far for achieving secured data sharing in cloud environment.

The signcryption technique was first proposed by Y. Zheng, where two cryptographic operations digital signature and message encryption are made combinedly. We cryptanalyze the technique and observe that the signature and encryption become vulnerable if the forged public keys are used. This paper proposes an improvement using modified DSS (Digital Signature Standard) version of ElGamal signature and DHP (Diffie-Hellman key exchange protocol), and shows that the vulnerabilities in both the signature and encryption methods used in Zheng's signcryption are circumvented. DHP is used for session symmetric key establishment and it is combined with the signature in such a way that the vulnerabilities of DHP can be avoided. The security and performance analysis of our signcryption technique are provided and found that our scheme is secure and designed using minimum possible operations with comparable computation cost of Zheng's scheme.

Software Defined Networking (SDN) is a networking paradigm that has been very popular due to its advantages over traditional networks with regard to scalability, flexibility, and its ability to solve many security issues. Nevertheless, SDN networks are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML). Different than other approaches found in literature which use the native flow features only for attack detection, our model extends the native features. The extended flow features are the average flow packet size, the number of flows to the same host as the current flow in the last 5 seconds, and the number of flows to the same host and port as the current flow in the last 5 seconds. Six ML algorithms were evaluated, namely Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experiments showed that RF is the best performing ML algorithm. Also, results showed that our model is able to detect attacks accurately and quickly, with a low probability of dropping normal traffic.

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.

In this paper, we propose a methodology for post-silicon validation through the evaluation of security assertions for systems-on-chip (SoC). The methodology is centered around a security architecture in which a "security capsule" is attached to each IP core in the SoC. The security capsule consists of a set of on-line and off-line assertion monitors, a dynamic trace-buffer to trace selected groups of signals, and a dynamic trace controller. The architecture is supported by a trace signal selection and grouping algorithm and a dynamic signal tracing method to evaluate the off-chip monitors. This paper presents the security capsule architecture, the signal selection and grouping algorithm, and the run-time signal tracing method. Results of using the methodology on two SoC architectures based on the OpenRISC-1200 and RISC-V processors are presented.

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.

Proofs in simulation-based frameworks have the greatest rigor when they are machine checked. But the level of details in these proofs surpasses what the formal-methods community can handle with existing tools. Existing formal results consider streamlined versions of simulation-based frameworks to cope with this complexity. Hence, a central question is how to abstract details from composability results and enable their formal verification.In this paper, we focus on the modeling of system communication in composable security statements. Existing formal models consider fixed communication patterns to reduce the complexity of their proofs. However, as we will show, this can affect the reusability of security statements. We propose an abstract approach to modeling system communication in Constructive Cryptography that avoids this problem. Our approach is suitable for mechanized verification and we use CryptHOL, a framework for developing mechanized cryptography proofs, to implement it in the Isabelle/HOL theorem prover. As a case study, we formalize the construction of a secure channel using Diffie-Hellman key exchange and a one-time-pad.

A promising paradigm in protocol design is to hold parties accountable for misbehavior, instead of postulating that they are trustworthy. Recent approaches in defining this property, called accountability, characterized malicious behavior as a deviation from the protocol that causes a violation of the desired security property, but did so under the assumption that all deviating parties are controlled by a single, centralized adversary. In this work, we investigate the setting where multiple parties can deviate with or without coordination in a variant of the applied-π calculus.We first demonstrate that, under realistic assumptions, it is impossible to determine all misbehaving parties; however, we show that accountability can be relaxed to exclude causal dependencies that arise from the behavior of deviating parties, and not from the protocol as specified. We map out the design space for the relaxation, point out protocol classes separating these notions and define conditions under which we can guarantee fairness and completeness. Most importantly, we discover under which circumstances it is correct to consider accountability in the single-adversary setting, where this property can be verified with off-the-shelf protocol verification tools.

To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier.

We propose a secure and efficient implementation of fungible tokens on Bitcoin. Our technique is based on a small extension of the Bitcoin script language, which allows the spending conditions in a transaction to depend on the neighbour transactions. We show that our implementation is computationally sound: that is, adversaries can make tokens diverge from their ideal functionality only with negligible probability.

Today's Internet is built on decades-old networking protocols that lack scalability, reliability, and security. In response, the networking community has developed path-aware Internet architectures that solve these issues while simultaneously empowering end hosts. In these architectures, autonomous systems construct authenticated forwarding paths based on their routing policies. Each end host then selects one of these authorized paths and includes it in the packet header, thus allowing routers to efficiently determine how to forward the packet. A central security property of these architectures is path authorization, requiring that packets can only travel along authorized paths. This property protects the routing policies of autonomous systems from malicious senders.The fundamental role of packet forwarding in the Internet and the complexity of the authentication mechanisms employed call for a formal analysis. In this vein, we develop in Isabelle/HOL a parameterized verification framework for path-aware data plane protocols. We first formulate an abstract model without an attacker for which we prove path authorization. We then refine this model by introducing an attacker and by protecting authorized paths using (generic) cryptographic validation fields. This model is parameterized by the protocol's authentication mechanism and assumes five simple verification conditions that are sufficient to prove the refinement of the abstract model. We validate our framework by instantiating it with several concrete protocols from the literature and proving that they each satisfy the verification conditions and hence path authorization. No invariants must be proven for the instantiation. Our framework thus supports low-effort security proofs for data plane protocols. The results hold for arbitrary network topologies and sets of authorized paths, a guarantee that state-of-the-art automated security protocol verifiers cannot currently provide.

Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.

Information flow type systems enforce the security property of noninterference by detecting unauthorized data flows at compile-time. However, they require precise type annotations, making them difficult to use in practice as much of the legacy infrastructure is written in untyped or dynamically-typed languages. Gradual typing seamlessly integrates static and dynamic typing, providing the best of both approaches, and has been applied to information flow control, where information flow monitors are derived from gradual security types. Prior work on gradual information flow typing uncovered tensions between noninterference and the dynamic gradual guarantee- the property that less precise security type annotations in a program should not cause more runtime errors.This paper re-examines the connection between gradual information flow types and information flow monitors to identify the root cause of the tension between the gradual guarantees and noninterference. We develop runtime semantics for a simple imperative language with gradual information flow types that provides both noninterference and gradual guarantees. We leverage a proof technique developed for FlowML and reduce noninterference proofs to preservation proofs.

Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.

Microservices-based architectures gain more and more attention in industry and academia due to their tremendous advantages such as providing resiliency, scalability, composability, etc. To benefit from these advantages, a proper architectural design is very important. The decomposition model of services into microservices and the granularity of these microservices affect the different aspects of the system such as flexibility, maintainability, performance, and security. An inappropriate service decomposition into microservices (improper granularity) may increase the attack surface of the system and lower its security level. In this paper, first, we study the probability of compromising services before and after decomposition. Then we formulate the impacts of possible service decomposition models on confidentiality, integrity, and availability attributes of the system. To do so, we provide equations for measuring confidentiality, integrity, and availability risks of the decomposed services in the system. It is also shown that the number of entry points to the decomposed services and the size of the microservices affect the security attributes of the system. As a use case, we propose three different service decomposition models for the 5G NRF (Network Repository Function) and calculate the impacts of these decomposition models on the confidentiality, integrity, and availability of the system using the provided equations.

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.

We explore a new type of malicious script attacks: the persistent parasite attack. Persistent parasites are stealthy scripts, which persist for a long time in the browser's cache. We show to infect the caches of victims with parasite scripts via TCP injection. Once the cache is infected, we implement methodologies for propagation of the parasites to other popular domains on the victim client as well as to other caches on the network. We show how to design the parasites so that they stay long time in the victim's cache not restricted to the duration of the user's visit to the web site. We develop covert channels for communication between the attacker and the parasites, which allows the attacker to control which scripts are executed and when, and to exfiltrate private information to the attacker, such as cookies and passwords. We then demonstrate how to leverage the parasites to perform sophisticated attacks, and evaluate the attacks against a range of applications and security mechanisms on popular browsers. Finally we provide recommendations for countermeasures.

In November 2019, the government of Iran enforced a week-long total Internet blackout that prevented the majority of Internet connectivity into and within the nation. This work elaborates upon the Iranian Internet blackout by characterizing the event through Internet-scale, near realtime network traffic measurements. Beginning with an investigation of compromised machines scanning the Internet, nearly 50 TB of network traffic data was analyzed. This work discovers 856,625 compromised IP addresses, with 17,182 attributed to the Iranian Internet space. By the second day of the Internet shut down, these numbers dropped by 18.46% and 92.81%, respectively. Empirical analysis of the Internet-of-Things (IoT) paradigm revealed that over 90% of compromised Iranian hosts were fingerprinted as IoT devices, which saw a significant drop throughout the shutdown (96.17% decrease by the blackout's second day). Further examination correlates BGP reachability metrics and related data with geolocation databases to statistically evaluate the number of reachable Iranian ASNs (dropping from approximately 1100 to under 200 reachable networks). In-depth investigation reveals the top affected ASNs, providing network forensic evidence of the longitudinal unplugging of such key networks. Lastly, the impact's interruption of the Bitcoin cryptomining market is highlighted, disclosing a massive spike in unsuccessful (i.e., pending) transactions. When combined, these network traffic measurements provide a multidimensional perspective of the Iranian Internet shutdown.

Hardware counters are included in processors to count microarchitecture level events affecting performance. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. Embedded systems also have enhanced need for variable anomaly detection time in order to meet the system response time requirements. We propose a novel repurposing of Patt-Yeh two level branch predictor data structure for abstracting/hashing HW counter signatures to support such variable anomaly detection times. The proposed anomaly detection mechanism is evaluated on some generic benchmark programs and ArduPilot - a popular autopilot software. Experimental evaluation encompasses both Intel X86 and ARM Cortex M processors. DWT within Cortex M provides sufficiently interesting program level event counts to capture these control flow anomalies. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per anomaly check.

Industry 4.0 connectivity requires ensuring end-to-end (E2E) security for industrial data. This requirement is critical when retrieving data from the OT network. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) guarantees E2E security by encrypting data according to a policy and generating user keys according to attributes. To use this encryption scheme in manufacturing environments, policies must be updatable. This paper proposes a Multi-Layered Policy Key Encapsulation Method for CP-ABE that allows flexible policy update and revocation without modifying the original CP-ABE scheme.

Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is composed of supervised and unsupervised modules, namely, a Deep Neural Network (DNN) and the K-Nearest Neighbors (KNN) algorithm, respectively. The proposed system is near-autonomous since the intervention of the expert is minimized through the active learning (AL) approach. A query strategy for the labeling process is presented, it aims at teaching the supervised module to detect unknown attacks and improve the detection of the already-known attacks. This teaching is achieved through sliding windows (SW) in an incremental fashion where the DNN is retrained when the data is available over time, thus rendering the IDS adaptive to cope with the evolutionary aspect of the network traffic. A set of experiments was conducted on the CICIDS2017 dataset in order to evaluate the performance of the IDS, promising results were obtained.

The network security problem brought by the cloud computing has become an important issue to be dealt with in information construction. Since anomaly detection and attack detection in cloud environment need to find the vulnerability through the reverse analysis of data flow, it is of great significance to carry out the reverse analysis of unknown network protocol in the security application of cloud environment. To solve this problem, an improved mining method on bitstream protocol association rules with unknown type and format is proposed. The method combines the location information of the protocol framework to make the frequent extraction process more concise and accurate. In addition, for the frame separation problem of unknown protocol, we design a hierarchical clustering algorithm based on Jaccard distance and a frame field delimitation method based on the proximity of information entropy between bytes. The experimental results show that this technology can correctly resolve the protocol format and realize the purpose of anomaly detection in cloud computing, and ensure the security of cloud services.

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so- called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the communication. Accordingly, it is of crucial interest for the legitimate users to detect such DoS attacks. Turing machines provide the fundamental limits of today's digital computers and therewith of the traditional signal processing. It has been shown that these are incapable of detecting DoS attacks. This stimulates the question of how powerful the signal processing must be to enable the detection of DoS attacks. This paper investigates the general computation framework of Blum-Shub-Smale machines which allows the processing and storage of arbitrary reals. It is shown that such real number signal processing then enables the detection of DoS attacks.

Recently, the physical layer security (PLS) is becoming an important research area for visible light communication (VLC) systems. In this paper, the secrecy rate performance is investigated for an indoor multi-user visible light communication (VLC) system using artificial noise (AN). In the considered model, all users simultaneously communicate with the legitimate receiver under wiretap channels. The legitimate receiver uses the minimum mean squared error (MMSE) equalizer to detect the received signals. Both lower bound and upper bound of the secrecy rate are obtained for the case that users' signals are uniformly distributed. Simulation results verify the theoretical findings and show the system secrecy rate performance for various positions of illegal eavesdropper.

Observational non-interference (ONI) is a generic information-flow policy for side-channel leakage. Informally, a program is ONI-secure if observing program leakage during execution does not reveal any information about secrets. Formally, ONI is parametrized by a leakage function l, and different instances of ONI can be recovered through different instantiations of l. One popular instance of ONI is the cryptographic constant-time (CCT) policy, which is widely used in cryptographic libraries to protect against timing and cache attacks. Informally, a program is CCT-secure if it does not branch on secrets and does not perform secret-dependent memory accesses. Another instance of ONI is the constant-resource (CR) policy, a relaxation of the CCT policy which is used in Amazon's s2n implementation of TLS and in several other security applications. Informally, a program is CR-secure if its cost (modelled by a tick operator over an arbitrary semi-group) does not depend on secrets.In this paper, we consider the problem of preserving ONI by compilation. Prior work on the preservation of the CCT policy develops proof techniques for showing that main compiler optimisations preserve the CCT policy. However, these proof techniques critically rely on the fact that the semi-group used for modelling leakage satisfies the property: l1+ l1' = l2+l2'$\Rightarrow$l1=l2$\wedge$ l1' = l2' Unfortunately, this non-cancelling property fails for the CR policy, because its underlying semi-group is ($\backslash$mathbbN, +) and it is currently not known how to extend existing techniques to policies that do not satisfy non-cancellation.We propose a methodology for proving the preservation of the CR policy during a program transformation. We present an implementation of some elementary compiler passes, and apply the methodology to prove the preservation of these passes. Our results have been mechanically verified using the Coq proof assistant.