Biblio

The cloud storage system is a very big boon for the organizations and individuals who are all in the need of storage space to accommodate huge volume of digital data. The cloud storage space can handle various types of digital data like text, image, video and audio. Since the storage space can be shared among different users, it is possible to have duplicate copies of data in the storage space. An efficient mechanism is required to identify the digital data uniquely in order to check the duplicity. There are various ways by which the digital data can be identified. One among such technique is hash-based identification. Using cryptographic hashing algorithms, every data can be uniquely identified. The unique property of hashing algorithm helps to identify the data uniquely. In this research work, we are going to discuss the advantage of using cryptographic hashing algorithm for digital data identification and the comparison of various hashing algorithms.

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.

Supervisory control and data acquisition (SCADA) networks provide high situational awareness and automation control for industrial control systems, whilst introducing a wide range of access points for cyber attackers. To address these issues, a line of machine learning or deep learning based intrusion detection systems (IDSs) have been presented in the literature, where a large number of attack examples are usually demanded. However, in real-world SCADA networks, attack examples are not always sufficient, having only a few shots in many cases. In this paper, we propose a novel few-shot learning based IDS, named FS-IDS, to detect cyber attacks against SCADA networks, especially when having only a few attack examples in the defenders’ hands. Specifically, a new method by orchestrating one-hot encoding and principal component analysis is developed, to preprocess SCADA datasets containing sufficient examples for frequent cyber attacks. Then, a few-shot learning based preliminary IDS model is designed and trained using the preprocessed data. Last, a complete FS-IDS model for SCADA networks is established by further training the preliminary IDS model with a few examples for cyber attacks of interest. The high effectiveness of the proposed FS-IDS, in detecting cyber attacks against SCADA networks with only a few examples, is demonstrated by extensive experiments on a real SCADA dataset.

The impact of malware grows for IT (information technology) systems day by day. The number, the complexity, and the cost of them increase rapidly. While researchers are developing new and better detection algorithms, attackers are also evolving malware to fail the current detection techniques. Therefore malware detection becomes one of the most challenging tasks in cyber security. To increase the performance of the detection techniques, researchers benefit from different approaches. But some of them might cost a lot both in time and hardware resources. This situation puts forward fast and cheap detection methods. In this context, static analysis provides these utilities but it is important to keep detection accuracy high while reducing resource consumption. Opcodes (operational codes) are commonly used in static analysis but sometimes feature extraction from opcodes might be difficult since an opcode sequence might have a great length. Furthermore, most of the malware developers use obfuscation and encryption techniques to avoid detection methods based on static analysis. This kind of malware is called packed malware and according to common belief, packed malware should be either unpacked or analyzed dynamically in order to detect them. In this study, a graph-based malware detection method has been proposed to overcome these problems. The proposed method relies on obtaining the opcode graph of every executable file in the dataset and using them for future extraction. In this way, the proposed method reaches up to 98% detection accuracy. In addition to the accuracy rate, the proposed method makes it possible to detect packed malware without the need for unpacking or dynamic analysis.

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.

Convergence of operation technology (OT) and information technology (IT) in industrial automation is currently being adopted as an accelerating trend. The Industrial Internet of Things (IIoT) consists of heterogeneous sensing, computing and actuation nodes that are meshed through a layer of communication protocols, and represents a key enabler for this convergence. Experimental test beds are required to validate complex system designs in terms of scalability, latency, real-time operation and security. We use the open source Coaty - distributed industrial systems framework to present a smart industry application integrating field devices and controllers over the OPCUA and MQTT protocols. The experimental evaluation, using both proprietary automation components and open software modules, serves as a reference tool for building robust systems and provides practical insights for interoperability.

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.

Machine learning-based malware detection systems are often vulnerable to evasion attacks, in which a malware developer manipulates their malicious software such that it is misclassified as benign. Such software hides some properties of the real class or adopts some properties of a different class by applying small perturbations. A special case of evasive malware hides by repackaging a bonafide benign mobile app to contain malware in addition to the original functionality of the app, thus retaining most of the benign properties of the original app. We present a novel malware detection system based on metamorphic testing principles that can detect such benign-seeming malware apps. We apply metamorphic testing to the feature representation of the mobile app, rather than to the app itself. That is, the source input is the original feature vector for the app and the derived input is that vector with selected features removed. If the app was originally classified benign, and is indeed benign, the output for the source and derived inputs should be the same class, i.e., benign, but if they differ, then the app is exposed as (likely) malware. Malware apps originally classified as malware should retain that classification, since only features prevalent in benign apps are removed. This approach enables the machine learning model to classify repackaged malware with reasonably few false negatives and false positives. Our training pipeline is simpler than many existing ML-based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification. We pre-trained our classifier model on 3 million apps collected from the widely-used AndroZoo dataset.1 We perform an extensive study on other publicly available datasets to show our approach's effectiveness in detecting repackaged malware with more than 94% accuracy, 0.98 precision, 0.95 recall, and 0.96 F1 score.

The increased use of Convolutional Neural Networks (CNN) in mission-critical systems has increased the need for robust and resilient networks in the face of both naturally occurring faults as well as security attacks. The lack of robustness and resiliency can lead to unreliable inference results. Current methods that address CNN robustness require hardware modification, network modification, or network duplication. This paper proposes MILR a software-based CNN error detection and error correction system that enables recovery from single and multi-bit errors. The recovery capabilities are based on mathematical relationships between the inputs, outputs, and parameters(weights) of the layers; exploiting these relationships allows the recovery of erroneous parameters (iveights) throughout a layer and the network. MILR is suitable for plaintext-space error correction (PSEC) given its ability to correct whole-weight and even whole-layer errors in CNNs.

With the processes of collecting, analyzing, and transmitting the data in the Internet of Things (IoT), the Internet of Medical Things (IoMT) comprises the medical equipment and applications connected to the healthcare system and offers an entity with real time, remote measurement, and analysis of healthcare data. However, the IoMT ecosystem deals with some great challenges in terms of security, such as privacy leaking, eavesdropping, unauthorized access, delayed detection of life-threatening episodes, and so forth. All these negative effects seriously impede the implementation of the IoMT ecosystem. To overcome these obstacles, this article presents an efficient, outsourced online/offline revocable ciphertext policy attribute-based encryption scheme with the aid of cloud servers and blockchains in the IoMT ecosystem. Our proposal achieves the characteristics of fine-grained access control, fast encryption, outsourced decryption, user revocation, and ciphertext verification. It is noteworthy that based on the chameleon hash function, we construct the private key of the data user with collision resistance, semantically secure, and key-exposure free to achieve revocation. To the best of our knowledge, this is the first protocol for a revocation mechanism by means of the chameleon hash function. Through formal analysis, it is proven to be secure in a selectively replayable chosen-ciphertext attack (RCCA) game. Finally, this scheme is implemented with the Java pairing-based cryptography library, and the simulation results demonstrate that it enables high efficiency and practicality, as well as strong reliability for the IoMT ecosystem.

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.

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.

With the rapid development of mobile communication technology, the reconnaissance on terminal capability and identity information is not only an important guarantee to maintain the normal order of mobile communication, but also an essential means to ensure the electromagnetic space security. According to the characteristics of 5G mobile communication terminal's transporting capability and identity information, the smart jamming is first used to make the target terminal away from the 5G network, and then the jamming is turned off at once. Next the terminal will return to the 5G network. Through the time-frequency matching detection method, interactive signals of random access process and network registration between the terminal and the base station are quickly captured in this process, and the scheduling information in Physical Downlink Control Channel (PDCCH) and the capability and identity information in Physical Uplink Shared Channel (PUSCH) are demodulated and decoded under non-cooperative conditions. Finally, the experiment is carried out on the actual 5G communication terminal of China Telecom. The capability and identity information of this terminal are extracted successfully in the Stand Alone (SA) mode, which verifies the effectiveness and correctness of the method. This is a significant technical foundation for the subsequent development on the 5G terminal control equipment.

The presence of the Information Technology System (ITS) has become one of the components for basic needs that must be met in navigating through the ages. Organizational programs in responding to the industrial era 4.0 make the use of ITS is a must in order to facilitate all processes related to quality service in carrying out the main task of protecting and serving the community. The implementation of ITS is actually not easy forthe threat of challenges and disturbances in the form of risks haunts ITS's operations. These conditions must be able to be identified and analyzed and then action can be executed to reduce the negative impact, so the risks are acceptable. This research will study about ITS risk management using the the guideline of Information Technology Infrastructure Library (ITIL) to formulate an operational strategy in order ensure that STI services at the Papa Oscar Cikeas Data Center (DC) can run well in the form of recommendations. Based on a survey on the implementing elements of IT function, 82.18% of respondents considered that the IT services provided by DC were very important, 86.49% of respondents knew the importance of having an emergency plan to ensure their products and services were always available, and 67.17% of respondents believes that DC is well managed. The results of the study concludes that it is necessary to immediately form a structural DC organization to prepare a good path for the establishment of a professional data center in supporting public service information technology systems.

As more and more visible light communication (VLC) and visible light sensing (VLS) systems are mounted on today’s light fixtures, how to guarantee the authenticity of the visible light (VL) signal in these systems becomes an urgent problem. This is because almost all of today’s light fixtures are unprotected and can be openly accessed by almost anyone, and hence are subject to tampering and substitution attacks. In this paper, by exploiting the intrinsic linear superposition characteristics of visible light, we propose VL-Watchdog, a scalable and always-on signal-level spoofing detection framework that is applicable to both VLC and VLS systems. VL-Watchdog is based on redundant orthogonal encoding of the transmitted visible light, and can be implemented as a small hardware add-on to an existing VL system. The effectiveness of the proposed framework was validated through extensive numerical evaluations against a comprehensive set of factors.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

MANET stands for Mobile ad-hoc network, which is also known as a wireless network. It provides a routable networking environment which does not have a centralized infrastructure. MANET is used in many important sectors like economic sector (corporate field), security sector (military field), education sector (video conferences and lectures), law sector (law enforcement) and many more. Even though it plays a vital role in different sectors and improves its economic growth, security is a major concern in MANET. Due to lack of inbuilt security, several attacks like data traffic attack, control traffic attack. The wormhole is a kind of control traffic attack which forms wormhole link between nodes. In this paper, we have proposed an approach to detect and get rid of the wormhole attack. The proposed approach is based on trust values, which will decide whether nodes are affected by using parameters like receiving time and data rate. On evaluation, we have concluded that the wormhole attack decreases the network's performance while using trusted approach its value increases. Means PDR and throughput return best results for the affected network while in case of end to end delay it returns similar results as of unaffected network.

Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.

Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multi-path secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods.

Particle filtering is an indispensable method for non-Gaussian state estimation, but it has some problems, such as particle degradation and requiring a large number of particles to ensure accuracy. Biota intelligence algorithms led by Cuckoo (CS) and Firefly (FA) have achieved certain results after introducing particle filtering, respectively. This paper respectively in the two kinds of bionic algorithm convergence factor and adaptive step length and random mobile innovation, seized the cuckoo algorithm (CS) in the construction of the initial value and the firefly algorithm (FA) in the iteration convergence advantages, using the improved after the update mechanism of cuckoo algorithm optimizing the initial population, and will be updated after optimization way of firefly algorithm combined with particle filter. Experimental results show that this method can ensure the diversity of particles and greatly reduce the number of particles needed for prediction while improving the filtering accuracy.

In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.

In order to guarantee the normal operation of the power Internet of things devices, the zero-trust idea was used for studying the security protection strategies of devices from four aspects: user authentication, equipment trust, application integrity and flow baselines. Firstly, device trust is constructed based on device portrait; then, verification of device application integrity based on MD5 message digest algorithm to achieve device application trustworthiness. Next, the terminal network traffic baselines are mined from OpenFlow, a southbound protocol in SDN. Finally, according to the dynamic user trust degree attribute access control model, the comprehensive user trust degree was obtained by weighting the direct trust degree. It obtained from user authentication and the trust degree of user access to terminal communication traffic. And according to the comprehensive trust degree, users are assigned the minimum authority to access the terminal to realize the security protection of the terminal. According to the comprehensive trust degree, the minimum permissions for users to access the terminal were assigned to achieve the security protection of the terminal. The research shows that the zero-trust mechanism is applied to the terminal security protection of power Internet of Things, which can improve the reliability of the safe operation of terminal equipment.

Connected smart cars enable new attacks that may have serious consequences. Thus, the development of new cars must follow a cybersecurity engineering process as defined for example in ISO/SAE 21434. A central part of such a process is the threat and risk assessment including an attack feasibility rating. In this paper, we present an attack surface assessment with focus on the attack feasibility rating compliant to ISO/SAE 21434. We introduce a reference architecture with assets constituting the attack surface, the attack feasibility rating for these assets, and the application of this rating on typical use cases. The attack feasibility rating assigns attacks and assets to an evaluation of the attacker dimensions such as the required knowledge and the feasibility of attacks derived from it. Our application of sample use cases shows how this rating can be used to assess the feasibility of an entire attack path. The attack feasibility rating can be used as a building block in a threat and risk assessment according to ISO/SAE 21434.