Biblio

This paper presents a computational study of Thai legal documents using text mining and network analytic approach. Thai legal systems rely much on the existing judicial rulings. Thus, legal documents contain complex relationships and require careful examination. The objective of this study is to use text mining to model relationships between these legal documents and draw useful insights. A structure of document relationship was found as a result of the study in forms of a network that is related to the meaningful relations of legal documents. This can potentially be developed further into a document retrieval system based on how documents are related in the network.

In order to cope with the network attack of industrial control system, this paper proposes a quantifiable attack-defense tree model. In order to reduce the influence of subjective factors on weight calculation and the probability of attack events, the Fuzzy Analytic Hierarchy Process and the Attack-Defense Tree model are combined. First, the model provides a variety of security attributes for attack and defense leaf nodes. Secondly, combining the characteristics of leaf nodes, a fuzzy consistency matrix is constructed to calculate the security attribute weight of leaf nodes, and the probability of attack and defense leaf nodes. Then, the influence of defense node on attack behavior is analyzed. Finally, the network risk assessment of typical airport oil supply automatic control system has been undertaken as a case study using this attack-defense tree model. The result shows that this model can truly reflect the impact of defense measures on the attack behavior, and provide a reference for the network security scheme.

Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.

Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, which is neither accurate (i.e., high false positive rate) nor efficient (i.e., large runtime overhead). It is hard to specify taint rules for each operation while covering all corner cases correctly. Moreover, the overtaint and undertaint errors can accumulate during the propagation of taint information across multiple operations. Finally, rule-based propagation requires each operation to be inspected before applying the appropriate rules resulting in prohibitive performance overhead on large real-world applications.In this work, we propose Neutaint, a novel end-to-end approach to track information flow using neural program embeddings. The neural program embeddings model the target's programs computations taking place between taint sources and sinks, which automatically learns the information flow by observing a diverse set of execution traces. To perform lightweight and precise information flow analysis, we utilize saliency maps to reason about most influential sources for different sinks. Neutaint constructs two saliency maps, a popular machine learning approach to influence analysis, to summarize both coarse-grained and fine-grained information flow in the neural program embeddings.We compare Neutaint with 3 state-of-the-art dynamic taint analysis tools. The evaluation results show that Neutaint can achieve 68% accuracy, on average, which is 10% improvement while reducing 40× runtime overhead over the second-best taint tool Libdft on 6 real world programs. Neutaint also achieves 61% more edge coverage when used for taint-guided fuzzing indicating the effectiveness of the identified influential bytes. We also evaluate Neutaint's ability to detect real world software attacks. The results show that Neutaint can successfully detect different types of vulnerabilities including buffer/heap/integer overflows, division by zero, etc. Lastly, Neutaint can detect 98.7% of total flows, the highest among all taint analysis tools.

Deep neural networks (DNNs) are known for their inability to utilize underlying hardware resources due to hard-ware susceptibility to sparse activations and weights. Even in finer granularities, many of the non-zero values hold a portion of zero-valued bits that may cause inefficiencies when executed on hard-ware. Inspired by conventional CPU simultaneous multithreading (SMT) that increases computer resource utilization by sharing them across several threads, we propose non-blocking SMT (NB-SMT) designated for DNN accelerators. Like conventional SMT, NB-SMT shares hardware resources among several execution flows. Yet, unlike SMT, NB-SMT is non-blocking, as it handles structural hazards by exploiting the algorithmic resiliency of DNNs. Instead of opportunistically dispatching instructions while they wait in a reservation station for available hardware, NB-SMT temporarily reduces the computation precision to accommodate all threads at once, enabling a non-blocking operation. We demonstrate NB-SMT applicability using SySMT, an NB-SMT-enabled output-stationary systolic array (OS-SA). Compared with a conventional OS-SA, a 2-threaded SySMT consumes 1.4× the area and delivers 2× speedup with 33% energy savings and less than 1% accuracy degradation of state-of-the-art CNNs with ImageNet. A 4-threaded SySMT consumes 2.5× the area and delivers, for example, 3.4× speedup and 39%×energy savings with 1% accuracy degradation of 40%-pruned ResNet-18.

Since the number of processors and cores on a single chip is increasing, the interconnection among them becomes significant. Network-on-Chip (NoC) has direct access to all resources and information within a System-on-Chip (SoC), rendering it appealing to attackers. Malicious attacks targeting NoC are a major cause of performance depletion and they can cause arbitrary behavior of links or routers, that is, Byzantine faults. Byzantine faults have been thoroughly investigated in the context of Distributed systems however not in Very Large Scale Integration (VLSI) systems. Hence, in this paper we propose a novel fault model followed by the design and implementation of lightweight algorithms, based on Software Defined Network-on-Chip (SDNoC) architecture. The proposed algorithms can be used to build highly available NoCs and can tolerate Byzantine faults. Additionally, a set of different scenarios has been simulated and the results demonstrate that by using the proposed algorithms the packet loss decreases between 65% and 76% under Transpose traffic, 67% and 77% under BitReverse and 55% and 66% under Uniform traffic.

Internet of Things (IoT) development and the addition of billions of computationally limited devices prohibit the use of classical security measures such as Intrusion Detection Systems (IDS). In this paper, we study the influence of the implementation of different feed-forward type of Neural Networks (NNs) on the detection Rate of the Negative Selection Neural Network (NSNN) algorithm. Feed-forward and cascade forward NN structures with different number of neurons and different number of hidden layers are tested. For training and testing the NSNN algorithm the labeled KDD NSL dataset is applied. The detection rates provided by the algorithm with several NN structures to determine the optimal solution are calculated and compared. The results show how these different feed-forward based NN architectures impact the performance of the NSNN algorithm.

An essential ingredient of the smart grid is software-based services. Increasingly, software is used to support control strategies and services that are critical to the grid's operation. Therefore, its correct operation is essential. For various reasons, software and its configuration needs to be updated. This update process represents a significant overhead for smart grid operators and failures can result in financial losses and grid instabilities. In this paper, we present a framework for determining the root causes of software rollout failures in the smart grid. It uses distributed sensors that indicate potential issues, such as anomalous grid states and cyber-attacks, and a causal inference engine based on a formalism called evidential networks. The aim of the framework is to support an adaptive approach to software rollouts, ensuring that a campaign completes in a timely and secure manner. The framework is evaluated for a software rollout use-case in a low voltage distribution grid. Experimental results indicate it can successfully discriminate between different root causes of failure, supporting an adaptive rollout strategy.

A new ONU authentication method based on POTS key matching is proposed, which makes use of ONU's own FXS resources and connects with a pots phone by dialing the corresponding LOID service key and authentication code that will be sent to ONU. The key combined with LOID service key and authentication code received by ONU will be filtered and then the LOID authentication code is obtained, which is put to match with DigitMap preset into the database of ONU. The LOID authentication code will be transmitted to OLT so as to achieve the purpose of ONU authentication and authorization if the match result is successful.

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

With the rapid growth of the cloud computing and strengthening of security requirements, encrypted cloud services are of importance and benefit. For the huge ciphertext data stored in the cloud, many secure searchable methods based on cryptography with keywords are introduced. In all the methods, attribute-based searchable encryption is considered as the truthful and efficient method since it supports the flexible access policy. However, the attribute-based system suffers from two defects when applied in the cloud storage. One of them is that the huge data in the cloud makes the users process all the relevant files related to the certain keyword. For the other side, the users and users' attributes inevitably change frequently. Therefore, attribute revocation is also an important problem in the system. To overcome these drawbacks, an attribute-based ranked searchable encryption scheme with revocation is proposed. We rank the ciphertext documents according to the TF×IDF principle, and then only return the relevant top-k files. Besides the decryption sever, an encryption sever is also introduced. And a large number of computations are outsourced to the encryption server and decryption server, which reduces the computing overhead of the client. In addition, the proposed scheme uses a real-time revocation method to achieve attribute revocation and delegates most of the update tasks to the cloud, which also reduces the calculation overhead of the user side. The performance evaluations show the scheme is feasible and more efficient than the available ones.

Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.

Distributed optimization algorithms are proposed to, potentially, reduce the computational time of large-scale optimization problems, such as security-constrained economic dispatch (SCED). While various geographical decomposition strategies have been presented in the literature, we proposed a temporal decomposition strategy to divide the SCED problem over the considered scheduling horizon. The proposed algorithm breaks SCED over the scheduling time and takes advantage of parallel computing using multi-core machines. In this paper, we investigate how to partition the overall time horizon. We study the effect of the number of partitions (i.e., SCED subproblems) on the overall performance of the distributed coordination algorithm and the effect of partitioning time interval on the optimal solution. In addition, the impact of system loading condition and ramp limits of the generating units on the number of iterations and solution time are analyzed. The results show that by increasing the number of subproblems, the computational burden of each subproblem is reduced, but more shared variables and constraints need to be modeled between the subproblems. This can result in increasing the total number of iterations and consequently the solution time. Moreover, since the load behavior affects the active ramping between the subproblems, the breaking hour determines the difference between shared variables. Hence, the optimal number of subproblems is problem dependent. A 3-bus and the IEEE 118-bus system are selected to analyze the effect of the number of partitions.

In an era of technological revolution in which everything becomes smarter and connected, the blockchain can introduce a new model for energy transactions able to grant more simplicity, security and transparency for end-users. The blockchain technology is characterized by a distributed architecture without a trusted and centralized authority, and, therefore, it appears as the perfect solutions for managing exchanges between peers. In this paper, a market algorithm that can be easily transferred to a smart contract for maximizing the match between produced and consumed energy in a micro-grid is presented. The algorithm supports energy transactions between peers (both producers and consumers) and could be one of the main executables implemented using a blockchain platform. The case study presented in this paper shows how the end-users through the blockchain could select among the possible energy transactions those more suitable to offer specific ancillary services to the grid operator without involving the grid operator itself or a third-party aggregator.

Wireless technology is widely used today and is growing rapidly. One of the wireless technologies is VANET where the network can communicate with vehicles (V2V) which can prevent accidents on the road. Energy is also a problem in VANET so it needs to be used efficiently. The presence of malicious nodes or nodes can eliminate and disrupt the process of data communication. The routing protocol used in this study is AODV. The purpose of this study is to analyze the comparison of blackhole attack and flooding attack against energy-efficient AODV on VANET. This research uses simulation methods and several supporting programs such as OpenStreetMap, SUMO, NS2, NAM, and AWK to test the AODV routing protocol. Quality of service (QOS) parameters used in this study are throughput, packet loss, and end to end delay. Energy parameters are also used to examine the energy efficiency used. This study uses the number of variations of nodes consisting of 20 nodes, 40 nodes, 60 nodes, and different network conditions, namely normal network conditions, network conditions with black hole attacks, and network conditions with flooding attacks. The results obtained can be concluded that the highest value of throughput when network conditions are normal, the greatest value of packet loss when there is a black hole attack, the highest end to end delay value and the largest remaining energy when there is a flooding attack.

The advancement in technologies like IoT, device-to-device communication lead to concepts like smart home and smart cities, etc. In smart home architecture, different devices such as home appliances, personal computers, surveillance cameras, etc. are connected to the Internet and enable the user to monitor and control irrespective of time and location. IPv6-enabled 6LoWPAN is a low-power, low-range communication protocol designed and developed for the short-range IoT applications. 6LoWPAN is based on IEEE 802.15.4 protocol and IPv6 network protocol for low range wireless applications. Although 6LoWPAN supports different routing protocols, RPL is the widely used routing protocol for low power and lossy networks. In this work, we have taken an IoT enabled smart home environment, in which 6LoWPAN is used as a communication and RPL as a routing protocol. The performance of this proposed network model is analyzed based on the different performance metrics such as latency, PDR, and throughput. The proposed model is simulated using Cooja simulator running over the Contiki OS. Along with the Cooja simulator, the network analyzer tool Wireshark is used to analyze the network behaviors.

In traditional virtual asset trading market, several risks, e.g. scams, cheating users, and market reach, have been pushed to users (sellers/buyers). Users need to decide who to trust; otherwise, no business. This fact impedes the growth of virtual asset trading market. In the past few years, several virtual asset marketplaces have embraced blockchain and smart contract technology to alleviate such risks, while trying to address privacy and scalability issues. To attain both speed and non-repudiation property for all transactions, existing blockchain-based exchange systems still cannot fully accomplish. In real-life trading, users use traditional contract to provide non-repudiation to achieve accountability in all committed transactions, so-called thorough non-repudiation. This is essential when dispute happens. To achieve similar thorough non-repudiation as well as privacy and scalability, we propose PEX, Privacy-preserved, multi-tier EXchange framework for cross platform virtual assets trading. PEX creates a smart contract for each virtual asset trading request. The key to address the challenges is to devise two-level distributed ledgers with two different types of quorums where one is for public knowledge in a global ledger and the other is for confidential information in a private ledger. A private quorum is formed to process individual smart contract and record the transactions in a private distributed ledger in order to maintain privacy. Smart contract execution checkpoints will be continuously written in a global ledger to strengthen thorough non-repudiation. PEX smart contract can be executed in parallel to promote scalability. PEX is also equipped with our reputation-based network to track contribution and discourage malicious behavior nodes or users, building healthy virtual asset ecosystem.

This paper presents the development of a control circuit to enhance the performances of LED lamps. In this direction, a comparison between the luminous intensity of normal LED based lamps and mid-power ones, for both continuous and switching conditions has been made. The already well know control technologies were analyzed and a study was conducted to increase the lighting performances by rising the operating frequency and magnifying the contribution of remanence effect and thus increasing the efficiency of the light source. To achieve this, in the first stage of the project the power and control circuits have been modeled, related to desired parameters and tested in simulation software. In the second stage, the proposed circuit was implemented by functional blocks and in the last stage, tests were made on the circuit and on light sources in order to process the results. The power consumption has been decreased nearly to a half of it and the luminous flux raised with 15% due to overcurrent and remanence effect that we used.

A frequency domain (FD) time-reversal (TR) pre-coder is proposed to perform physical layer security (PLS) in single-input single-output (SISO) system using orthogonal frequency-division multiplexing (OFDM). To maximize the secrecy of the communication, the design of an artificial noise (AN) signal well-suited to the proposed FD TR-based OFDM SISO system is derived. This new scheme guarantees the secrecy of a communication toward a legitimate user when the channel state information (CSI) of a potential eavesdropper is not known. In particular, we derive an AN signal that does not corrupt the data transmission to the legitimate receiver but degrades the decoding performance of the eavesdropper. A closed-form approximation of the AN energy to inject is defined in order to maximize the secrecy rate (SR) of the communication. Simulation results are presented to demonstrate the security performance of the proposed secure FD TR SISO OFDM system.

Over the last few decades, and thanks to the advancement of embedded systems and wireless technologies, the wireless sensors network (WSN) are increasingly used in many fields. Many researches are being done on the use of WSN in Wireless body Area Network (WBAN) systems to facilitate and improve the quality of care and remote patient monitoring.The broadcast nature of wireless communications makes it difficult to hide transmitted signals from unauthorized users. To this end, Physical layer security is emerging as a promising paradigm to protect wireless communications against eavesdropping attacks. The primary contribution of this paper is achieving a minimum secrecy outage probability by using the jamming technique which can be used by the legitimate communication partner to increase the noise level of the eavesdropper and ensure higher secure communication rate. We also evaluate the effect of additional jammers on the security of the WBAN system.

Optical wireless communications (OWC) and its potential to solve physical layer security (PLS) issues are becoming important research areas in 6G communications systems. In this paper, an overview of PLS in visible light communications (VLC), is presented. Then, two new PLS techniques based on generalized space shift keying (GSSK) modulation with spatial constellation design (SCD) and non-orthogonal multiple access (NOMA) cooperative relaying are introduced. In the first technique, the PLS of the system is enhanced by the appropriate selection of a precoding matrix for randomly activated light emitting diodes (LEDs). With the aid of a legitimate user's (Bob's) channel state information (CSI) at the transmitter (CSIT), the bit error ratio (BER) of Bob is minimized while the BER performance of the potential eavesdroppers (Eves) is significantly degraded. In the second technique, superposition coding with uniform signaling is used at the transmitter and relays. The design of secure beamforming vectors at the relay nodes along with NOMA techniques is used to enhance PLS in a VLC system. Insights gained from the improved security levels of the proposed techniques are used to discuss how PLS can be further improved in future generation communication systems by using VLC.

We present our journey in constructing the first integrated data warehouse for Philippine transportation research in the hopes of developing a Transportation Decision Support System for impact studies and policy making. We share how we collected data from diverse sources, processed them into a homogeneous format and applied them to our multimodal platform. We also list the challenges we encountered, including bureaucratic delays, data privacy concerns, lack of software, and overlapping datasets. The data warehouse shall serve as a public resource for researchers and professionals, and for government officials to make better-informed policies. The warehouse will also function within our multi-modal platform for measurement, modelling, and visualization of road transportation. This work is our contribution to improve the transportation situation in the Philippines, both in the local and national levels, to boost our economy and overall quality of life.

In the 2010s, there has been significant interest in developing methods, such as searchable encryption for exact matching and order-preserving/-revealing encryption for range search, to perform search on encrypted data. However, the symmetric searchable encryption method has been steadily used not only in databases but also in full-text search engine because of its quick performance and high security against intruders and system administrators. Contrarily, order-preserving/-revealing encryption is rarely employed in practice: almost all related schemes suffer from inference attacks, and some schemes are secure but impractical because they require exponential storage size or communication complexity. In this study, we define the new security models based on order-revealing encryption (ORE) for performing range search, and explain that previous techniques are not satisfied with our weak security model. We present two generic constructions of ORE using the searchable encryption method. Our constructions offer practical performance such as the storage size of O(nb) and computation complexity of O(n2), where the plaintext space is a set of n-bit binaries and b denotes the block size of the ciphertext generated via searchable encryption. The first construction gives the comparison result to the server, and the security considers a weak security model. The second construction hides the comparison result from the server, and only the secret-key owner can recover it.

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

In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.