Biblio

With the progress of cryptography computer science, designing cryptographic algorithms using deep learning is a very innovative research direction. Google Brain designed a communication model using generation adversarial network and explored the encrypted communication algorithm based on machine learning. However, the encrypted communication model it designed lacks quantitative evaluation. When some plaintexts and keys are leaked at the same time, the security of communication cannot be guaranteed. This model is optimized to enhance the security by adjusting the optimizer, modifying the activation function, and increasing batch normalization to improve communication speed of optimization. Experiments were performed on 16 bits and 64 bits plaintexts communication. With plaintext and key leak rate of 0.75, the decryption error rate of the decryptor is 0.01 and the attacker can't guess any valid information about the communication.

In this paper, we quantify elements representing video features and we propose the bitrate prediction of compressed encoding video using deep learning. Particularly, to overcome disadvantage that we cannot predict bitrate of compression video by using Constant Rate Factor (CRF), we use deep learning. We can find element of video feature with relationship of bitrate when we compress the video, and we can confirm its possibility to find relationship through various deep learning techniques.

With the continuous development of the Internet, artificial intelligence, 5G and other technologies, various issues have started to receive attention, among which the network security issue is now one of the key research directions for relevant research scholars at home and abroad. This paper researches on the basis of traditional Internet technology to establish a security identification system on top of the network physical layer of the Internet, which can effectively identify some security problems on top of the network infrastructure equipment and solve the identified security problems on the physical layer. This experiment is to develop a security identification system, research and development in the network physical level of the Internet, compared with the traditional development of the relevant security identification system in the network layer, the development in the physical layer, can be based on the physical origin of the protection, from the root to solve part of the network security problems, can effectively carry out the identification and solution of network security problems. The experimental results show that the security identification system can identify some basic network security problems very effectively, and the system is developed based on the physical layer of the Internet network, and the protection is carried out from the physical device, and the retransmission symbol error rates of CQ-PNC algorithm and ML algorithm in the experiment are 110 and 102, respectively. The latter has a lower error rate and better protection.

Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn from a limited amount of labelled data together with a vast amount of unlabelled data. However, due to their reliance on the known labels to infer the unknown labels, these algorithms are sensitive to data quality. It is therefore essential to study the potential threats related to the labelled data, more specifically, label poisoning. In this paper, we propose a novel data poisoning method which efficiently approximates the result of label inference to identify the inputs which, if poisoned, would produce the highest number of incorrectly inferred labels. We extensively evaluate our approach on three classification problems under 24 different experimental settings each. Compared to the state of the art, our influence-driven attack produces an average increase of error rate 50% higher, while being faster by multiple orders of magnitude. Moreover, our method can inform engineers of inputs that deserve investigation (relabelling them) before training the learning model. We show that relabelling one-third of the poisoned inputs (selected based on their influence) reduces the poisoning effect by 50%. ACM Reference Format: Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, and Yves Le Traon. 2022. Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3522664.3528606

In this paper we use a seeded modular coding scheme for implementing physical layer security in a wiretap scenario. This modular scheme consists of a traditional coding layer and a security layer. For the traditional coding layer, we use a polar code. We evaluate the performance of the seeded modular coding scheme in an experimental setup with software defined radios and compare these results to simulation results. In order to assess the secrecy level of the scheme, we employ the distinguishing security metric. In our experiments, we compare the distinguishing error rate for different seeds and block lengths.

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.

Human behavior analysis is the process that consists of activity monitoring and behavior recognition and has become the core component of intelligent applications such as security surveillance and fall detection. Generally, the techniques involved in behavior recognition include sensor and vision-based processing. During the process, the activity information is typically required to ensure a good recognition performance. On the other hand, the privacy issue attracts much attention and requires a limited range of activity monitoring accordingly. We study behavior analysis for such privacy-oriented applications. A local object tracking (LOT) technique based on an infrared sensor array is developed in a limited monitoring range and is further realized to a practical bed-exit system in the clinical test environment. The experimental results show a correct recognition rate of 99% for 6 bedside activities. In addition, 89% of participants in a satisfaction survey agree on its effectiveness.

With the development of artificial intelligence and cryptography, intelligent cryptography will be the trend of encrypted communications in the future. Abadi designed an encrypted communication model based on a generative adversarial network, which can communicate securely when the adversary knows the ciphertext. The communication party and the adversary fight against each other to continuously improve their own capabilities to achieve a state of secure communication. However, this model can only have a better communication effect under the 16 bits communication length, and cannot adapt to the length of modern encrypted communication. Combine the neural network structure in DCGAN to optimize the neural network of the original model, and at the same time increase the batch normalization process, and optimize the loss function in the original model. Experiments show that under the condition of the maximum 2048-bit communication length, the decryption success rate of communication reaches about 0.97, while ensuring that the adversary’s guess error rate is about 0.95, and the training speed is greatly increased to keep it below 5000 steps, ensuring safety and efficiency Communication.

Based on the structure of turbo-polar codes, a secure symmetric encryption scheme is proposed to enhance information transmission security in this paper. This scheme utilizes interleaving at information bits and puncturing at parity bits for several times in the encoder. Correspondingly, we need to do the converse interleaving and fill zeros accurately at punctured position. The way of interleaving and puncturing is controlled by the private key of symmetric encryption, making sure the security of the system. The security of Secure Turbo-Polar Codes (STPC) is analyzed at the end of this paper. Simulation results are given to shown that the performance and complexity of Turbo-Polar Codes have little change after symmetric encryption. We also investigate in depth the influence of different remaining parity bit ratios on Frame Error Rate (FER). At low Signal to Noise Rate (SNR), we find it have about 0.6dB advantage when remaining parity bit ratio is between 1/20 and 1/4.

This paper represents the experimental implementation of an encryption-based visible light communication system for indoor communication over 14m, two single LED transmitters as the data source, and four receivers considered as data receivers for performance evaluation.

Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24×7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

In recent years, there are more and more attacks and exploitation aiming at network security vulnerabilities. It is effective for us to prevent criminals from exploiting vulnerabilities for attacks and help security analysts maintain equipment security that knows vulnerabilities and threats on time. With the knowledge graph, we can organize, manage, and utilize the massive information effectively in cyberspace. In this paper we construct the vulnerability ontology after analyzing multi-source heterogeneous databases. And the vulnerability knowledge graph is established. Experimental results show that the accuracy of entity recognition for extracting vendor names reaches 89.76%. The more rules used in entity recognition, the higher the accuracy and the lower the error rate.

This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring function of enrollment and testing utterance pairs. Then we leverage the fast gradient sign method (FGSM) to optimize testing utterances for adversarial samples generation. These adversarial samples are used to attack both GMM i-vector and x-vector systems. We measure the system vulnerability by the degradation of equal error rate and false acceptance rate. Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples are proved to be transferable and pose threats to neural network speaker embedding based systems (e.g. x-vector systems).

Automated techniques for rigorous floating-point round-off error analysis are a prerequisite to placing important activities in HPC such as precision allocation, verification, and code optimization on a formal footing. Yet existing techniques cannot provide tight bounds for expressions beyond a few dozen operators-barely enough for HPC. In this work, we offer an approach embedded in a new tool called SATIHE that scales error analysis by four orders of magnitude compared to today's best-of-class tools. We explain how three key ideas underlying SATIHE helps it attain such scale: path strength reduction, bound optimization, and abstraction. SATIHE provides tight bounds and rigorous guarantees on significantly larger expressions with well over a hundred thousand operators, covering important examples including FFT, matrix multiplication, and PDE stencils.

In recent years, the demand for high reliability in industrial wireless communication has been increasing. To meet the strict requirement, many researchers have studied various bit interleaving coding schemes for long packet transmission in industrial wireless networks. Current research shows that the use of bit interleaving structure can improve the performance of the communication system for long packet transmission, but to improve reliability of industrial wireless communications by combining the bit interleaving and channel coding for short packets still requires further analysis. With this aim, bit interleaving structure is applied to convolution code coding scheme for short packet transmission in this paper. We prove that the use of interleaver fail to improve the reliability of data transmission under the circumstance of short packet transmission.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

Certificate Transparency (CT) is an emerging system for enabling the rapid discovery of malicious or misissued certificates. Initially standardized in 2013, CT is now finally beginning to see widespread support. Although CT provides desirable security benefits, web browsers cannot begin requiring all websites to support CT at once, due to the risk of breaking large numbers of websites. We discuss challenges for deployment, analyze the adoption of CT on the web, and measure the error rates experienced by users of the Google Chrome web browser. We find that CT has so far been widely adopted with minimal breakage and warnings. Security researchers often struggle with the tradeoff between security and user frustration: rolling out new security requirements often causes breakage. We view CT as a case study for deploying ecosystem-wide change while trying to minimize end user impact. We discuss the design properties of CT that made its success possible, as well as draw lessons from its risks and pitfalls that could be avoided in future large-scale security deployments.

HTTPS provides authentication, data confidentiality, and integrity for secure web applications in the Internet. In order to establish secure connections with the target website but not a man-in-the-middle or impersonation attacker, a browser shows security warnings to users, when different HTTPS errors happen (e.g., it fails to build a valid certificate chain, or the certificate subject does not match the domain visited). Each browser implements its own design of warnings on HTTPS errors, to balance security and usability. This paper presents a list of common HTTPS errors, and we investigate the browser behaviors on each error. Our study discloses browser defects on handling HTTPS errors in terms of cryptographic algorithm, certificate verification, name validation, HPKP, and HSTS.

Logic encryption is a powerful hardware protection technique that uses extra key inputs to lock a circuit from piracy or unauthorized use. The recent discovery of the SAT-based attack with Distinguishing Input Pattern (DIP) generation has rendered all traditional logic encryptions vulnerable, and thus the creation of new encryption methods. However, a critical question for any new encryption method is whether security against the DIP-generation attack means security against all other attacks. In this paper, a new high-level SAT-based attack called SigAttack has been discovered and thoroughly investigated. It is based on extracting a key-revealing signature in the encryption. A majority of all known SAT-resilient encryptions are shown to be vulnerable to SigAttack. By formulating the condition under which SigAttack is effective, the paper also provides guidance for the future logic encryption design.

This paper provides a proof of concept for using SRAM based Physically Unclonable Functions (PUFs) to generate private keys for IoT devices. PUFs are utilized, as there is inadequate protection for secret keys stored in the memory of the IoT devices. We utilize a custom-made Arduino mega shield to extract the fingerprint from SRAM chip on demand. We utilize the concepts of ternary states to exclude the cells which are easily prone to flip, allowing us to extract stable bits from the fingerprint of the SRAM. Using the custom-made software for our SRAM device, we can control the error rate of the PUF to achieve an adjustable memory-based PUF for key generation. We utilize several fuzzy extractor techniques based on using different error correction coding methods to generate secret keys from the SRAM PUF, and study the trade-off between the false authentication rate and false rejection rate of the PUF.

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.

Genetic Algorithms are group of mathematical models in computational science by exciting evolution in AI techniques nowadays. These algorithms preserve critical information by applying data structure with simple chromosome recombination operators by encoding solution to a specific problem. Genetic algorithms they are optimizer, in which range of problems applied to it are quite broad. Genetic Algorithms with its global search includes basic principles like selection, crossover and mutation. Data structures, algorithms and human brain inspiration are found for classification of data and for learning which works using Neural Networks. Artificial Intelligence (AI) it is a field, where so many tasks performed naturally by a human. When AI conventional methods are used in a computer it was proved as a complicated task. Applying Neural Networks techniques will create an internal structure of rules by which a program can learn by examples, to classify different inputs than mining techniques. This paper proposes a phishing websites classifier using improved polynomial neural networks in genetic algorithm.

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.