Biblio

As the core infrastructure of cloud data operation, exchange and storage, data centerneeds to ensure its security and reliability, which are the important prerequisites for the development of cloud computing. Due to various illegal accesses, attacks, viruses and other security threats, it is necessary to protect the boundary of cloud data center through security gateway. Since the traffic growing up to gigabyte level, the secure gateway must ensure high transmission efficiency and different network services to support the cloud services. In addition, data center is gradually evolving from IPv4 to IPv6 due to excessive consumption of IP addresses. Packet classification algorithm, which can divide packets into different specific streams, is very important for QoS, real-time data stream application and firewall. Therefore, it is necessary to design a high performance IPv6 packet classification algorithm suitable for security gateway.AsIPv6 has a128-bitIP address and a different packet structure compared with IPv4, the traditional IPv4 packet classification algorithm is not suitable properly for IPv6 situations. This paper proposes a fast packet classification algorithm for IPv6 - PCHA (packet classification based on hash andAdelson-Velsky-Landis Tree). It adopts the three flow classification fields of source IPaddress(SA), destination IPaddress(DA) and flow label(FL) in the IPv6 packet defined by RFC3697 to implement fast three-tuple matching of IPv6 packet. It is through hash matching of variable length IPv6 address and tree matching of shorter flow label. Analysis and testing show that the algorithm has a time complexity close to O(1) in the acceptable range of space complexity, which meets the requirements of fast classification of IPv6 packetsand can adapt well to the changes in the size of rule sets, supporting fast preprocessing of rule sets. Our algorithm supports the storage of 500,000 3-tuple rules on the gateway device and can maintain 75% of the performance of throughput for small packets of 78 bytes.

The threats from side-channel attacks to modern processors has become a serious problem, especially under the enhancement of the microarchitecture characteristics with multicore and resource sharing. Therefore, the research and measurement of the vulnerability of the side-channel attack of the system is of great significance for computer designers. Most of the current evaluation methods proposed by researchers are only for typical cache side-channel attacks. In this paper, we propose a method to measure systems' vulnerability to side-channel attacks caused by port contention called pcSVF. We collected the traces of the victim and attacker and computed the correlation coefficient between them, thus we can measure the vulnerability of the system against side-channel attack. Then we analyzed the effectiveness of the method through the results under different system defense schemes.

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.

In this study, we investigate the performance of FSO communication systems under more realistic channel model considering atmospheric turbulence, pointing errors and channel estimation errors together. For this aim, we first derived the composite probability density function (PDF) of imperfect Málaga turbulence channel with pointing errors. Then using this PDF, we obtained bit-error-rate (BER) and ergodic channel capacity (ECC) expressions in closed forms. Additionally, we present the BER and ECC metrics of imperfect Gamma-Gamma and K turbulence channels with pointing errors as special cases of Málaga channel. We further verified our analytic results through Monte-Carlo simulations.

The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.

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.

Underwater acoustic (UWA) channel is complex because of its multipath environment, Doppler shift and rapidly changing characteristics. Many of the UWA communication- based applications demand high data rates and reliable communication. The orthogonal frequency division multiplexing (OFDM) system is very effective in UWA channels and provides high data rate with low equalization complexity. It is a challenging task to achieve reliability over these channels. The low-density parity-check (LDPC) codes give a better error performance than turbo codes, for UWA channels. The spatially-coupled low-density parity-check (SC-LDPC) codes have been shown to have the capacity-achieving performance over terrestrial communication. In this paper, we have studied by simulation, the performance of protograph based SC-LDPC codes over shallow water acoustic environment with a communication range of 1000 m and channel bandwidth of 10 KHz. Our results show that SC-LDPC codes give 1 dB performance improvement over LDPC codes at a Bit Error Rate (BER) of 10-3 for the same latency constraints.

Tunnel approach to the security and privacy aspects of communication networks has been an issue since the inception of networking technologies. Neither the technology nor the regulatory and legal frame works proactively play a significant role towards addressing the ever escalating security challenges. As we have move to ubiquitous computing paradigm where information secrecy and privacy is coupled with new challenges of human to machine and machine to machine interfaces, a transformational model for security should be visited. This research is attempted to highlight the peripheral view of IoT based miniature device security paradigm with focus on standardization, regulations, user adaptation, software and applications, low computing resources and power consumption, human to machine interface and privacy.

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.

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.

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.

We propose a photonic compressive sampling scheme based on multimode fiber for radio spectrum sensing, which shows high accuracy and stability, and low complexity and cost. Pulse overlapping is utilized for a fast detection. © 2020 The Author(s).

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.

This paper studies physical-layer security over slow fading channels, considering the impact of finite-blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate the secrecy throughput (ST) of a legitimate user pair coexisting with an eavesdropper. Specifically, we devise both adaptive and non-adaptive optimization schemes to maximize the ST, where we derive optimal parameters including the transmission policy, blocklength, and code rates based on the instantaneous and statistical channel state information of the legitimate pair, respectively. Various important insights are provided. In particular, 1) increasing blocklength improves both reliability and secrecy with our transmission policy; 2) ST monotonically increases with blocklength; 3) ST initially increases and then decreases with secrecy rate, and there exists a critical secrecy rate that maximizes the ST. Numerical results are presented to verify theoretical findings.

The ever-growing advances in science and technology have led to a rapid increase in the complexity of most engineered systems. Cyber-physical Systems (CPSs) are the result of this technology advancement that involves new paradigms, architectures and functionalities derived from different engineering domains. Due to the nature of CPSs, which are composed of many heterogeneous components that constantly interact one another and with the environment, it is difficult to study, explain hypothesis and evaluate design alternatives without using Modeling and Simulation (M&S) approaches. M&S is increasingly used in the CPS domain with different objectives; however, its adoption is not easy and straightforward but can lead to pitfalls that need to be recognized and addressed. This paper identifies some important pitfalls deriving from the application of M&S approaches to the CPS study and presents remedies, which are already available in the literature, to prevent and face them.

In this paper, a planning method of transportation-power coupled system based on road expansion model is proposed. First of all, based on the Wardrop equilibrium state, the traffic flow is distributed, to build the road expansion model and complete the traffic network modeling. It is assumed that the road charging demand is directly proportional to the road traffic flow, and the charging facilities will cause a certain degree of congestion on the road. This mutual influence relationship to establish a coupling system of transportation network and power network is used for the planning. In the planning method, the decision variables include the location of charging facilities, the setting of energy storage systems and the road expansion scheme. The planning goal is to minimize the investment cost and operation cost. The CPLEX solver is used to solve the mixed integer nonlinear programming problem. Finally, the simulation analysis is carried out to verify the validity and feasibility of the planning method, which can comprehensively consider the road expansion cost and travel time cost, taking a coupled system of 5-node traffic system and IEEE14 node distribution network as example.

Recently, deep neural network technology has been developed and used in various fields. The image recognition model can be used for automatic safety checks at the electric factory. However, as the deep neural network develops, the importance of security increases. A poisoning attack is one of security problems. It is an attack that breaks down by entering malicious data into the training data set of the model. This paper generates adversarial data that modulates feature values to different targets by manipulating less RGB values. Then, poisoning attacks in one of the image recognition models, the show and tell model. Then use autoencoder to defend adversarial data.

Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks.

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: "polynomially coded sharding" scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.

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.

With the wild applications of machine learning (ML) technology, automatic speech recognition (ASR) has made great progress in recent years. Despite its great potential, there are various evasion attacks of ML-based ASR, which could affect the security of applications built upon ASR. Up to now, most studies focus on white-box attacks in ASR, and there is almost no attention paid to black-box attacks where attackers can only query the target model to get output labels rather than probability vectors in audio domain. In this paper, we propose an evasion attack against ASR in the above-mentioned situation, which is more feasible in realistic scenarios. Specifically, we first train a substitute model by using data augmentation, which ensures that we have enough samples to train with a small number of times to query the target model. Then, based on the substitute model, we apply Differential Evolution (DE) algorithm to craft adversarial examples and implement black-box attack against ASR models from the Speech Commands dataset. Extensive experiments are conducted, and the results illustrate that our approach achieves untargeted attacks with over 70% success rate while still maintaining the authenticity of the original data well.

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.