Biblio

Wireless relay network is a solution for transmitting information from a source node to a sink node far away by installing a relay in between. The broadcasting nature of wireless communication allows the sink node to receive part of the data sent by the source node. In this way, the relay does not need to receive the whole piece of data from the source node and it does not need to forward everything it received. In this paper, we consider the application of batched network coding, a practical form of random linear network coding, for a better utilization of such a network. The amount of innovative information at the relay which is not yet received by the sink node, called the innovative rank, plays a crucial role in various applications including the design of the transmission scheme and the analysis of the throughput. We present a visualization of the innovative rank which allows us to understand and derive formulae related to the innovative rank with ease.

With the advancement of VLSI technology, Tiled Chip Multicore Processors (TCMP) with packet switched Network-on-Chip (NoC) have been emerged as the backbone of the modern data intensive parallel systems. Due to tight time-to-market constraints, manufacturers are exploring the possibility of integrating several third-party Intellectual Property (IP) cores in their TCMP designs. Presence of malicious Hardware Trojan (HT) in the NoC routers can adversely affect communication between tiles leading to degradation of overall system performance. In this paper, we model an HT mounted on the input buffers of NoC routers that can alter the destination address field of selected NoC packets. We study the impact of such HTs and analyse its first and second order impacts at the core level, cache level, and NoC level both quantitatively and qualitatively. Our experimental study shows that the proposed HT can bring application to a complete halt by stalling instruction issue and can significantly impact the miss penalty of L1 caches. The impact of re-transmission techniques in the context of HT impacted packets getting discarded is also studied. We also expose the unrealistic assumptions and unacceptable latency overheads of existing mitigation techniques for packet header attacks and emphasise the need for alternative cost effective HT management techniques for the same.

Wireless sensor networks (WSN) hold numerous battery operated, compact sized, and inexpensive sensor nodes, which are commonly employed to observe the physical parameters in the target environment. As the sensor nodes undergo arbitrary placement in the open areas, there is a higher possibility of affected by distinct kinds of attacks. For resolving the issue, intrusion detection system (IDS) is developed. This paper presents a new optimal Support Vector Machine (OSVM) based IDS in WSN. The presented OSVM model involves the proficient selection of optimal kernels in the SVM model using whale optimization algorithm (WOA) for intrusion detection. Since the SVM kernel gets altered using WOA, the application of OSVM model can be used for the detection of intrusions with proficient results. The performance of the OSVM model has been investigated on the benchmark NSL KDDCup 99 dataset. The resultant simulation values portrayed the effectual results of the OSVM model by obtaining a superior accuracy of 94.09% and detection rate of 95.02%.

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

High-assurance cryptography leverages methods from program verification and cryptography engineering to deliver efficient cryptographic software with machine-checked proofs of memory safety, functional correctness, provable security, and absence of timing leaks. Traditionally, these guarantees are established under a sequential execution semantics. However, this semantics is not aligned with the behavior of modern processors that make use of speculative execution to improve performance. This mismatch, combined with the high-profile Spectre-style attacks that exploit speculative execution, naturally casts doubts on the robustness of high-assurance cryptography guarantees. In this paper, we dispel these doubts by showing that the benefits of high-assurance cryptography extend to speculative execution, costing only a modest performance overhead. We build atop the Jasmin verification framework an end-to-end approach for proving properties of cryptographic software under speculative execution, and validate our approach experimentally with efficient, functionally correct assembly implementations of ChaCha20 and Poly1305, which are secure against both traditional timing and speculative execution attacks.

The utilization of "cloud storage services (CSS)", empowering people to store their data in cloud and avoid from maintenance cost and local data storage. Various data integrity auditing (DIA) frameworks are carried out to ensure the quality of data stored in cloud. Mostly, if not all, of current plans, a client requires to utilize his private key (PK) to generate information authenticators for knowing the DIA. Subsequently, the client needs to have hardware token to store his PK and retain a secret phrase to actuate this PK. In this hardware token is misplaced or password is forgotten, the greater part of existing DIA plans would be not able to work. To overcome this challenge, this research work suggests another DIA without "private key storage (PKS)"plan. This research work utilizes biometric information as client's fuzzy private key (FPK) to evade utilizing hardware token. In the meantime, the plan might in any case viably complete the DIA. This research work uses a direct sketch with coding and mistake correction procedures to affirm client identity. Also, this research work plan another mark conspire that helps block less. Verifiability, yet in addition is viable with linear sketch Keywords– Data integrity auditing (DIA), Cloud Computing, Block less Verifiability, fuzzy biometric data, secure cloud storage (SCS), key exposure resilience (KER), Third Party Auditor (TPA), cloud audit server (CAS), cloud storage server (CSS), Provable Data Possession (PDP)

In order to protect user privacy and provide better access control in Internet of Things (IoT) environments, designing an appropriate two-party authentication and key exchange protocol is a prominent challenge. In this paper, we propose a lightweight certificateless non-interactive authentication and key exchange (CNAKE) protocol for mutual authentication between remote users and smart devices. Based on elliptic curves, our lightweight protocol provides high security performance, realizes non-interactive authentication between the two entities, and effectively reduces communication overhead. Under the random oracle model, the proposed protocol is provably secure based on the Computational Diffie-Hellman and Bilinear Diffie-Hellman hardness assumption. Finally, through a series of experiments and comprehensive performance analysis, we demonstrate that our scheme is fast and secure.

Modern digital signature schemes can provide more guarantees than the standard notion of (strong) unforgeability, such as offering security even in the presence of maliciously generated keys, or requiring to know a message to produce a signature for it. The use of signature schemes that lack these properties has previously enabled attacks on real-world protocols. In this work we revisit several of these notions beyond unforgeability, establish relations among them, provide the first formal definition of non re-signability, and a transformation that can provide these properties for a given signature scheme in a provable and efficient way.Our results are not only relevant for established schemes: for example, the ongoing NIST PQC competition towards standardizing post-quantum signature schemes has six finalists in its third round. We perform an in-depth analysis of the candidates with respect to their security properties beyond unforgeability. We show that many of them do not yet offer these stronger guarantees, which implies that the security guarantees of these post-quantum schemes are not strictly stronger than, but instead incomparable to, classical signature schemes. We show how applying our transformation would efficiently solve this, paving the way for the standardized schemes to provide these additional guarantees and thereby making them harder to misuse.

The globalized supply chain in the semiconductor industry raises several security concerns such as IC overproduction, intellectual property piracy and design tampering. Logic locking has emerged as a Design-for-Trust countermeasure to address these issues. Original logic locking proposals provide a high degree of output corruption – i.e., errors on circuit outputs – unless it is unlocked with the correct key. This is a prerequisite for making a manufactured circuit unusable without the designer’s intervention. Since the introduction of SAT-based attacks – highly efficient attacks for retrieving the correct key from an oracle and the corresponding locked design – resulting design-based countermeasures have compromised output corruption for the benefit of better resilience against such attacks. Our proposed logic locking scheme, referred to as SKG-Lock, aims to thwart SAT-based attacks while maintaining significant output corruption. The proposed provable SAT-resilience scheme is based on the novel concept of decoy key-inputs. Compared with recent related works, SKG-Lock provides higher output corruption, while having high resistance to evaluated attacks.

Cloud storage technology enables users to outsource local data to cloud service provider (CSP). In spite of its copious advantages, how to ensure the integrity of data has always been a significant issue. A variety of provable data possession (PDP) scheme have been proposed for cloud storage scenarios. However, the participation of centralized trusted third-party auditor (TPA) in most of the previous work has brought new security risks, because the TPA is prone to the single point of failure. Furthermore, the existing schemes do not consider the fair arbitration and lack an effective method to punish the malicious behavior. To address the above challenges, we propose a novel blockchain-based decentralized data integrity auditing scheme without the need for a centralized TPA. By using smart contract technique, our scheme supports automatic compensation mechanism. DO and CSP must first pay a certain amount of ether for the smart contract as deposit. The CSP gets the corresponding storage fee if the integrity auditing is passed. Otherwise, the CSP not only gets no fee but has to compensate DO whose data integrity is destroyed. Security analysis shows that the proposed scheme can resist a variety of attacks. Also, we implement our scheme on the platform of Ethereum to demonstrate the efficiency and effectiveness of our scheme.

In this paper, we propose a new provably secure cryptosystem for two party communication that provides security in the face of new technological breakthroughs. Most of the practical cryptosystems in use today can be breached in the future with new sophisticated methods. This jeopardizes the security of older but highly confidential messages. Our protocol is based on the bounded storage model first introduced in [1]. The protocol is secure as long as there is bound on the storage, however large it may be. We also suggest methods to extend the protocol to unbounded storage models where access to adversary is limited. Our protocol is a substantial improvement over previously known protocols and uses short key and optimal number of public random bits size of which is independent of message length. The smaller and constant length of key and public random string makes the scheme more practical. The protocol generates key using elements of the additive group \$\textbackslashtextbackslashmathbbZ\_\textbackslashtextbackslashmathrmn\$. Our protocol is very generalized and the protocol in [1] is a special case of our protocol. Our protocol is a step forward in making provably secure cryptosystems practical. An important open problem raised in [2] was designing an algorithm with short key and size of public random string \$O(\textbackslashtextbackslashmathcalB)\$ where \$\textbackslashtextbackslashmathcalB\$ bounds the storage of adversary. Our protocol satisfies the conditions and is easy to implement.

The 5G technology brings the substantial improvement on the quality of services (QoS), such as higher throughput, lower latency, more stable signal and more ultra-reliable data transmission, triggering a revolution for the wireless mobile network. But in a general traffic channel in the 5G-based wireless mobile network, an attacker can detect a message transmitted over a channel, or even worse, forge or tamper with the message. Building a secure channel over the two parties is a feasible solution to this uttermost data transmission security challenge in 5G-based wireless mobile network. However, how to authentication the identities of the both parties before establishing the secure channel to fully ensure the data confidentiality and integrity during the data transmission has still been a open issue. To establish a fully secure channel, in this paper, we propose a strongly secure pairing-free certificateless authenticated key agreement (PF-CL-AKA) protocol with two-way identity-based authentication before extracting the secure session key. Our protocol is provably secure in the Lippold model, which means our protocol is still secure as long as each party of the channel has at least one uncompromised partial private term. Finally, By the theoretical analysis and simulation experiments, we can observe that our scheme is practical for the real-world applications in the 5G-based wireless mobile network.

To improve efficiency of stegosystem on blockchain and balance the time consumption of Encode and Decode operations, we propose a new blockchain-based steganography scheme, called Keys as Secret Messages (KASM), where a codebook of mappings between bitstrings and public keys can be pre-calculated by both sides with some secret parameters pre-negotiated before covert communication. By applying properties of elliptic curves and pseudorandom number generators, we realize key derivation of codebook item, and we construct the stegosystem with provable security under chosen hiddentext attack. By comparing KASM with Blockchain Covert Channel (BLOCCE) and testing on Bitcoin protocol, we conclude that our proposed stegosystem encodes hiddentexts faster than BLOCCE does and can decode stegotexts in highly acceptable time. The balanced time consumption of Encode and Decode operations of KASM make it applicable in the scene of duplex communication. At the same time, KASM does not leak sender’s private keys, so sender’s digital currencies can be protected.

A standard requirement for a signature scheme is that it is existentially unforgeable under chosen message attacks (EUF-CMA), alongside other properties of interest such as strong unforgeability (SUF-CMA), and resilience against key substitution attacks.Remarkably, no detailed proofs have ever been given for these security properties for EdDSA, and in particular its Ed25519 instantiations. Ed25519 is one of the most efficient and widely used signature schemes, and different instantiations of Ed25519 are used in protocols such as TLS 1.3, SSH, Tor, ZCash, and WhatsApp/Signal. The differences between these instantiations are subtle, and only supported by informal arguments, with many works assuming results can be directly transferred from Schnorr signatures. Similarly, several proofs of protocol security simply assume that Ed25519 satisfies properties such as EUF-CMA or SUF-CMA.In this work we provide the first detailed analysis and security proofs of Ed25519 signature schemes. While the design of the schemes follows the well-established Fiat-Shamir paradigm, which should guarantee existential unforgeability, there are many side cases and encoding details that complicate the proofs, and all other security properties needed to be proven independently.Our work provides scientific rationale for choosing among several Ed25519 variants and understanding their properties, fills a much needed proof gap in modern protocol proofs that use these signatures, and supports further standardisation efforts.

In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.

‘Malicious software’ or malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. In the past, many solutions have proved ineffective and have resulted in many false positives. Having the ability to identify and classify malware will help prevent them from spreading and evolving. In this paper, we study the effectiveness of the proposed classification of the malware family using a pixel level as features. This study has implemented well-known machine learning and deep learning classifiers such as K-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree, and Random Forest. A binary file of 25 malware families is converted into a fixed grayscale image. The grayscale images were then extracted transforming the size 100x100 into a single format into 100000 columns. During this phase, none of the columns are removed as to remain the patterns in each malware family. The experimental results show that our approach achieved 92% accuracy in Random Forest, 88% in SVM, 81% in Decision Tree, 80% in k-NN and 56% in Naïve Bayes classifier. Overall, the pixel-based feature also reveals a promising technique for identifying the family of malware with great accuracy, especially using the Random Forest classifier.

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

The proliferation of malware attacks causes a hindrance to cybersecurity thus, posing a significant threat to our devices. The variety and number of both known as well as unknown malware makes it difficult to detect it. Research suggests that the ramifications of malware are only becoming worse with time and hence malware analysis becomes crucial. This paper proposes a visual malware classification technique to convert malware executables into their visual representations and obtain grayscale images of malicious files. These grayscale images are then used to classify malicious files into their respective malware families by passing them through deep convolutional neural networks (CNN). As part of deep CNN, we use various ImageNet models and compare their performance.

Malware classification helps to understand its purpose and is also an important part of attack detection. And it is also an important part of discovering attacks. Due to continuous innovation and development of artificial intelligence, it is a trend to combine deep learning with malware classification. In this paper, we propose an improved malware image rescaling algorithm (IMIR) based on local mean algorithm. Its main goal of IMIR is to reduce the loss of information from samples during the process of converting binary files to image files. Therefore, we construct a neural network structure based on VGG model, which is suitable for image classification. In the real world, a mass of malware family labels are inaccurate or lacking. To deal with this situation, we propose a novel method to train the deep neural network by Semi-supervised Generative Adversarial Network (SGAN), which only needs a small amount of malware that have accurate labels about families. By integrating SGAN with weak coupling, we can retain the weak links of supervised part and unsupervised part of SGAN. It improves the accuracy of malware classification by making classifiers more independent of discriminators. The results of experimental demonstrate that our model achieves exhibiting favorable performance. The recalls of each family in our data set are all higher than 93.75%.

Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware executables into image-based representations. These images are then classified into different malware families using an autoencoder enhanced deep convolutional neural network (AE-DCNN). In particular, we propose a novel training mechanism wherein a DCNN classifier is trained with the help of an encoder. We conjecture that using an encoder in the proposed way provides the classifier with the extra information that is perhaps lost during the forward propagation, thereby leading to better results. The proposed approach eliminates the use of feature engineering, reverse engineering, disassembly, and other domain-specific techniques earlier used for malware classification. On the standard Malimg dataset, we achieve a 10-fold cross-validation accuracy of 99.38% and F1-score of 99.38%. Further, due to the texture-based analysis of malware files, the proposed technique is resilient to several obfuscation techniques.

Malicious software (malware) creators are constantly mutating malware files in order to avoid detection, resulting in hundreds of millions of new malware every year. Therefore, most malware files are unlabeled due to the time and cost needed to label them manually. This makes it very challenging to perform malware detection, i.e., deciding whether a file is malware or not, and malware classification, i.e., determining the family of the malware. Most solutions use supervised learning (e.g., ResNet and VGG) whose accuracy degrades significantly with the lack of abundance of labeled data. To solve this problem, this paper proposes a semi-supervised learning model for image-based malware classification. In this model, malware files are represented as grayscale images, and semi-supervised learning is carefully selected to handle the plethora of unlabeled data. Our proposed model is an enhanced version of the ∏-model, which makes it more accurate and consistent. Experiments show that our proposed model outperforms the original ∏-model by 4% in accuracy and three other supervised models by 6% in accuracy especially when the ratio of labeled samples is as low as 20%.

Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.

A malware detection model based on semi-supervised learning is proposed in the paper. Our model includes mainly three parts: malware visualization, feature extraction, and classification. Firstly, the malware visualization converts malware into grayscale images; then the features of the images are extracted to reflect the coding patterns of malware; finally, a collaborative learning model is applied to malware detections using both labeled and unlabeled software samples. The proposed model was evaluated based on two commonly used benchmark datasets. The results demonstrated that compared with traditional methods, our model not only reduced the cost of sample labeling but also improved the detection accuracy through incorporating unlabeled samples into the collaborative learning process, thereby achieved higher classification performance.

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

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.