Biblio

Physical Unclonable Functions (PUFs) are vulnerable to various modelling attacks. The chaotic behaviour of oscillating systems can be leveraged to improve their security against these attacks. We have integrated an Arbiter PUF implemented on a FPGA with Chua's oscillator circuit to obtain robust final responses. These responses are tested against conventional Machine Learning and Deep Learning attacks for verifying security of the design. It has been found that such a design is robust with prediction accuracy of nearly 50%. Moreover, the quality of the PUF architecture is evaluated for uniformity and uniqueness metrics and Monte Carlo analysis at varying temperatures is performed for determining reliability.

Nowadays, physical health of equipment controlled by Cyber-Physical Systems (CPS) is a significant concern. This paper reports a work, in which, a hardware is placed between Programmable Logic Controller (PLC) and the actuator as a solution. The proposed hardware operates in two conditions, i.e. passive and active. Operation of the proposed solution is based on the repetitive operational profile of the actuators. The normal operational profile of the actuator is fed to the protective hardware and is considered as the normal operating condition. In the normal operating condition, the middleware operates in its passive mode and simply monitors electronic signals passing between PLC and Actuator. In case of any malicious operation, the proposed hardware operates in its active mode and both slowly stops the actuator and sends an alert to SCADA server initiating execution of the actuator's emergency profile. Thus, the proposed hardware gains control over the actuator and prevents any physical damage on the operating devices. Two sample experiments are reported in which, results of implementing the proposed solution are reported and assessed. Results show that once the PLC sends incorrect data to actuator, the proposed hardware detects it as an anomaly. Therefore, it does not allow the PLC to send incorrect and unauthorized data pattern to its actuator. Significance of the paper is in introducing a solution to prevent destruction of physical devices apart from source or purpose of the encountered anomaly and apart from CPS functionality or PLC model and operation.

A frequent problem of Internet services are Sybil attacks, i.e., malicious users create numerous fake identities for themselves. To avoid this, many services employ obstacles like Captchas to force (potentially malicious) users to invest human attention in creating new identities for the service. However, this only makes it more difficult but not impossible to create fake identities. Sybil attacks are especially encountered as a problem in decentralized systems since no single trust anchor is available to judge new users as honest or malicious. The avoidance of a single centralized trust-anchor, however, is desirable in many cases. As a consequence, various decentralized Sybil detection approaches have been proposed. The most promising ones are based on leveraging the trust relationships embedded within social graphs. While most of these approaches are focusing on detecting large existing groups of Sybil identities, our approach Detasyr instead restricts the creation of numerous Sybil identities. For that, tickets are distributed through the social graph and have to be collected, allowing for decentralized and privacy preserving authorization. Additionally, it offers a proof of authorization to users that are considered to be honest, allowing them to display their authorization towards others.

We consider the possibility of detecting malicious behaviors of the advanced persistent threat (APT) at endpoints during incident response or forensics investigations. Specifically, we study the case where third-party sensors are not available; our observables are obtained solely from inherent digital artifacts of Windows operating systems. What is of particular interest is an artifact called the Application Compatibility Cache (Shimcache). As it is not apparent from the Shimcache when a file has been executed, we propose an algorithm of estimating the time of file execution up to an interval. We also show guarantees of the proposed algorithm's performance and various possible extensions that can improve the estimation. Finally, combining this approach with methods of machine learning, as well as information from other digital artifacts, we design a prototype system called XTEC and demonstrate that it can help hunt for the APT in a real-world case study.

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it's critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.

Distributed Denial of Service (DDoS) strike is a malevolent undertaking to irritate regular action of a concentrated on server, organization or framework by overwhelming the goal or its incorporating establishment with a flood of Internet development. DDoS ambushes achieve feasibility by utilizing different exchanged off PC structures as wellsprings of strike action. Mishandled machines can join PCs and other masterminded resources, for instance, IoT contraptions. From an anomalous express, a DDoS attack looks like a vehicle convergence ceasing up with the road, shielding standard action from meeting up at its pined for objective.

Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.

Data security is a major requirement of smart meter communication to control server through Advanced Metering infrastructure. Easy access of smart meters and multi-faceted nature of AMI communication network are the main reasons of smart meter facing large number of attacks. The different topology, bandwidth and heterogeneity in communication network prevent the existing security mechanisms in satisfying the security requirements of smart meter. Hence, advanced security mechanisms are essential to encrypt smart meter data before transmitting to control server. The emerging biocryptography technique has several advantages over existing techniques and is most suitable for providing security to communication of low processing devices like smart meter. In this paper, a lightweight encryption scheme using DNA sequence with suitable key management scheme is proposed for secure communication of smart meter in an efficient way. The proposed 2-phase DNA cryptography provides confidentiality and integrity to transmitted data and the authentication of keys is attained by exchanging through Diffie Hellman scheme. The strength of proposed encryption scheme is analyzed and its efficiency is evaluated by simulating an AMI communication network using Simulink/Matlab. Comparison of simulation results with various techniques show that the proposed scheme is suitable for secure communication of smart meter data.

We developed a virtualization-based infringement incident response tool for cyber security training system using Cloud. This tool was developed by applying the concept of attack and defense which is the basic of military war game modeling and simulation. The main purpose of this software is to cultivate cyber security experts capable of coping with various situations to minimize the damage in the shortest time when an infringement incident occurred. This tool acquired the invaluable certificate from Korean government agency. This tool shall provide CBT type remote education such as scenario based infringement incident response training, hacking defense practice, and vulnerability measure practice. The tool works in Linux, Window operating system environments, and uses Korean e-government framework and secure coding to construct a situation similar to the actual information system. In the near future, Internet and devices connected to the Internet will be greatly enlarged, and cyber security threats will be diverse and widespread. It is expected that various kinds of hacking will be attempted in an advanced types using artificial intelligence technology. Therefore, we are working on applying the artificial intelligence technology to the current infringement incident response tool to cope with these evolving threats.

We demonstrate secure fiber-optic transmission utilizing quantum-noise signal masking by 217-level random phase modulation. Masking of 157 signal phase levels at a BER of HD-FEC threshold is achieved without significant impacts on the transmission performance.

Digital forensics is process of identifying, preserving, analyzing and preserving digital evidence. Due to increasing cybercrimes now a days, it is important for all organizations to have a well-managed digital forensics cell. So to overcome this, we propose a framework called digital forensics capability analyser. [1]The main advantage of developing digital analyzer is cost minimization. This tool will provide fundamental information for setting up a digital forensic cell and will also offer services like online sessions. [2] [3]It will help organizations by providing them with a perfect solution according to their requirements to start a digital forensic cell in their respective lnstitution.[4] [5].

The world is continuously developing, and people's needs are increasing as well; so too are the number of thieves increasing, especially electronic thieves. For that reason, companies and individuals are always searching for experts who will protect them from thieves, and these experts are called digital investigators. Digital forensics has a number of branches and different parts, and image forensics is one of them. The budget for the images branch goes up every day in response to the need. In this paper we offer some information about images and image forensics, image components and how they are stored in digital devices and how they can be deleted and recovered. We offer general information about digital forensics, focusing on image forensics.

Internet of Things (IoT) is a key emerging technology which aims to connect objects over the internet. Software Defined Networking (SDN) is another new intelligent technology within networking domain which increases the network performance and provides better security, reliability, and privacy using dynamic software programs. In this paper, we have proposed a distributed secure Black SDN-IoT architecture with NFV implementation for smart cities. We have incorporated Black SDN that is highly secured SDN which gives better result in network performances, security, and privacy and secures both metadata and payload within each layer. This architecture also tried to introduce an approach which is more effective for building a cluster by means of Black SDN. Black SDN-loT with NFV concept brings benefits to the related fields in terms of energy savings and load balancing. Moreover, Multiple distributed controller have proposed to improve availability, integrity, privacy, confidentiality and etc. In the proposed architecture, the Black network provides higher security of each network layer comparative to the conventional network. Finally, this paper has discussed the architectural design of distributed secure Black SDN-IoT with NFV for smart cities and research challenges.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

Botnet has been evolving over time since its birth. Nowadays, P2P (Peer-to-Peer) botnet has become a main threat to cyberspace security, owing to its strong concealment and easy expansibility. In order to effectively detect P2P botnet, researchers often focus on the analysis of network traffic. For the sake of enriching P2P botnet detection methods, the author puts forward a new sight of applying distributed threat intelligence sharing system to P2P botnet detection. This system aims to fight against distributed botnet by using distributed methods itself, and then to detect botnet in real time. To fulfill the goal of botnet detection, there are 3 important parts: the threat intelligence sharing and evaluating system, the BAV quantitative TI model, and the AHP and HMM based analysis algorithm. Theoretically, this method should work on different types of distributed cyber threat besides P2P botnet.

The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.

Shortest path queries on road networks are widely used in location-based services (LBS), e.g., finding the shortest route from my home to the airport through Google Maps. However, when there are a large number of path queries arrived concurrently or in a short while, an LBS provider (e.g., Google Maps) has to endure a high workload and then may lead to a long response time to users. Therefore, path caching services are utilized to accelerate large-scale path query processing, which try to store the historical path results and reuse them to answer the coming queries directly. However, most of existing path caches are organized based on nodes of paths; hence, the underlying road network topology is still needed to answer a path query when its querying origin or destination lies on edges. To overcome this limitation, we propose an edge-based shortest path cache in this paper that can efficiently handle queries without needing any road information, which is much more practical in the real world. We achieve this by designing a totally new edge-based path cache structure, an efficient R-tree-based cache lookup algorithm, and a greedy-based cache construction algorithm. Extensive experiments on a real road network and real point-of-interest datasets are conducted, and the results show the efficiency, scalability, and applicability of our proposed caching techniques.

Accountable authority identity-based encryption (A-IBE), as an attractive way to guarantee the user privacy security, enables a malicious private key generator (PKG) to be traced if it generates and re-distributes a user private key. Particularly, an A-IBE scheme achieves full black-box security if it can further trace a decoder box and is secure against a malicious PKG who can access the user decryption results. In PKC'11, Sahai and Seyalioglu presented a generic construction for full black-box A-IBE from a primitive called dummy identity-based encryption, which is a hybrid between IBE and attribute-based encryption (ABE). However, as the complexity of ABE, their construction is inefficient and the size of private keys and ciphertexts in their instantiation is linear in the length of user identity. In this paper, we present a new efficient generic construction for full black-box A-IBE from a new primitive called token-based identity-based encryption (TB-IBE), without using ABE. We first formalize the definition and security model for TB-IBE. Subsequently, we show that a TB-IBE scheme satisfying some properties can be converted to a full black-box A-IBE scheme, which is as efficient as the underlying TB-IBE scheme in terms of computational complexity and parameter sizes. Finally, we give an instantiation with the computational complexity as O(1) and the constant size master key pair, private keys, and ciphertexts.

A hierarchical key management scheme for mobile agents in e-medicine system enables users, such as patients, doctors, nurses and health visitors, to conveniently and securely access a remote hierarchical medical database system via public networks. Efficient hierarchical key management schemes do not require heavy computations even if the hierarchical structure has too many levels and participants. Chen et al. recently developed a hierarchical key management scheme with date-constraint for mobile agents. The key management scheme of Chen et al. is based the Elliptic Curve Cryptosystem and allows each secret key to be partnered with a validity period by using one-way hash chains. However, the scheme of Chen et al. fails to execute correctly, violates authenticated key security, and requires hundreds of hash functional operations. This investigation discusses these limitations, and proposes an efficient date-constraint hierarchical key management scheme for mobile agents in e-medicine system, which provides a fast key validation and expiration check phase to rapidly check whether the secret keys are valid and time-expired or not. The proposed key management scheme not only provides more security properties and rapidly checks the validation of secret keys, but also reduces the computational cost..

Emails are the fundamental unit of web applications. There is an exponential growth in sending and receiving emails online. However, spam mail has turned into an intense issue in email correspondence condition. There are number of substance based channel systems accessible to be specific content based filter(CBF), picture based sifting and many other systems to channel spam messages. The existing technological solution consists of a combination of porter stemer algorithm(PSA) and k means clustering which is adaptive in nature. These procedures are more expensive in regard of the calculation and system assets as they required the examination of entire spam message and calculation of the entire substance of the server. These are the channels must additionally not powerful in nature life on the grounds that the idea of spam block mail and spamming changes much of the time. We propose a starting point based spam mail-sifting system benefit, which works considering top head notcher data of the mail message paying little respect to the body substance of the mail. It streamlines the system and server execution by increasing the precision, recall and accuracy than the existing methods. To design an effective and efficient of autonomous and efficient spam detection system to improve network performance from unknown privileged user attacks.

Big data processing systems are becoming increasingly more present in cloud workloads. Consequently, they are starting to incorporate more sophisticated mechanisms from traditional database and distributed systems. We focus in this work on the use of caching policies, which for big data raise important new challenges. Not only they must respond to new variants of the trade-off between hit rate, response time, and the space consumed by the cache, but they must do so at possibly higher volume and velocity than web and database workloads. Previous caching policies have not been tested experimentally with big data workloads. We address these challenges in this work. We propose the Read Density family of policies, which is a principled approach to quantify the utility of cached objects through a family of utility functions that depend on the frequency of reads of an object. We further design the Approximate Histogram, which is a policy-based technique based on an array of counters. This technique promises to achieve runtime-space efficient computation of the metric required by the cache policy. We evaluate through trace-based simulation the caching policies from the Read Density family, and compare them with over ten state-of-the-art alternatives. We use two workload traces representative for big data processing, collected from commercial Spark and MapReduce deployments. While we achieve comparable performance to the state-of-art with less parameters, meaningful performance improvement for big data workloads remain elusive.

Elliptical curve cryptography (ECC) is being used more and more in public key cryptosystems. Its main advantage is that, at a given security level, key sizes are much smaller compared to classical asymmetric cryptosystems like RSA. Smaller keys imply less power consumption, less cryptographic computation and require less memory. Besides performance, security is another major problem in embedded devices. Cryptosystems, like ECC, that are considered mathematically secure, are not necessarily considered safe when implemented in practice. An attacker can monitor these interactions in order to mount attacks called fault attacks. A number of countermeasures have been developed to protect Montgomery Scalar Multiplication algorithm against fault attacks. In this work, we proposed an efficient countermeasure premised on duplication scheme and the scrambling technique for Montgomery Scalar Multiplication algorithm against fault attacks. Our approach is simple and easy to hardware implementation. In addition, we perform injection-based error simulations and demonstrate that the error coverage is about 99.996%.