Biblio

In our proposed work the web security has been enhanced using additional security code and an enhanced frame work. Administrator of site is required to specify the security code for particular date and time. On user end user would be capable to login and view authentic code allotted to them during particular time slot. This work would be better in comparison of tradition researches in order to prevent sql injection attack and cross script because proposed work is not just considering the security, it is also focusing on the performance of security system. This system is considering the lot of security dimensions. But in previous system there was focus either on sql injection or cross script. Proposed research is providing versatile security and is available with low time consumption with less probability of unauthentic access.

The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art results on resource-constrained edge devices is challenging due to the stringent memory and power/energy constraints. Moreover, these systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to further improve the performance (latency) and the energy efficiency of Edge AI systems through HW/SW-level optimizations, such as pruning, quantization, and approximation. To address reliability threats (like permanent and transient faults), we highlight cost-effective mitigation techniques, like fault-aware training and mapping. Moreover, we briefly discuss effective detection and protection techniques to address security threats (like model and data corruption). Towards the end, we discuss how these techniques can be combined in an integrated cross-layer framework for realizing robust and energy-efficient Edge AI systems.

Networked embedded systems (which include IoT, CPS, etc.) are vulnerable. Even though we know how to secure these systems, their heterogeneity and the heterogeneity of security policies remains a major problem. Designers face ever more sophisticated attacks while they are not always security experts and have to get a trade-off on design criteria. We propose in this paper the CLASA architecture (Cross-Layer Agent Security Architecture), a generic, integrated, inter-operable, decentralized and modular architecture which relies on cross-layering.

Software vulnerabilities have become a major problem for the security analysts, since the number of new vulnerabilities is constantly growing. Thus, there was a need for a categorization system, in order to group and handle these vulnerabilities in a more efficient way. Hence, the MITRE corporation introduced the Common Weakness Enumeration that is a list of the most common software and hardware vulnerabilities. However, the manual task of understanding and analyzing new vulnerabilities by security experts, is a very slow and exhausting process. For this reason, a new automated classification methodology is introduced in this paper, based on the vulnerability textual descriptions from National Vulnerability Database. The proposed methodology, combines textual analysis and tree-based machine learning techniques in order to classify vulnerabilities automatically. The results of the experiments showed that the proposed methodology performed pretty well achieving an overall accuracy close to 80%.

Security databases such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) maintain diverse high-quality security concepts, which are treated as security entities. Meanwhile, security entities are documented with many potential relation types that profit for security analysis and comprehension across these three popular databases. To support reasoning security entity relationships, translation-based knowledge graph representation learning treats each triple independently for the entity prediction. However, it neglects the important semantic information about the neighbor entities around the triples. To address it, we propose a text-enhanced graph attention network model (text-enhanced GAT). This model highlights the importance of the knowledge in the 2-hop neighbors surrounding a triple, under the observation of the diversity of each entity. Thus, we can capture more structural and textual information from the knowledge graph about the security databases. Extensive experiments are designed to evaluate the effectiveness of our proposed model on the prediction of security entity relationships. Moreover, the experimental results outperform the state-of-the-art by Mean Reciprocal Rank (MRR) 0.132 for detecting the missing relationships.

Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches.

Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.

In this paper we discuss distributed denial of service attacks on software defined networks, software defined networking is a network architecture approach that enables the network to be intelligently and centrally controlled using software applications. These days the usage of internet is increased because high availability of internet and low cost devices. At the same time lot of security challenges are faced by network monitors and administrators to tackle the frequent network access by the users. The main idea of SDN is to separate the control plane and the data plane, as a result all the devices in the data plane becomes forwarding devices and all the decision making activities transferred to the centralized system called controller. Openflow is the standardized and most important protocol among many SDN protocols. In this article given the overview of distributed denial of service attacks and prevention mechanisms to these malicious attacks.

Distributed Denial of Service (DDoS) attacks became a true threat to network infrastructure. DDoS attacks are capable of inflicting major disruption to the information communication technology infrastructure. DDoS attacks aim to paralyze networks by overloading servers, network links, and network devices with illegitimate traffic. Therefore, it is important to detect and mitigate DDoS attacks to reduce the impact of DDoS attacks. In traditional networks, the hardware and software to detect and mitigate DDoS attacks are expensive and difficult to deploy. Software-Defined Network (SDN) is a new paradigm in network architecture by separating the control plane and data plane, thereby increasing scalability, flexibility, control, and network management. Therefore, SDN can dynamically change DDoS traffic forwarding rules and improve network security. In this study, a DDoS attack detection and mitigation system was built on the SDN architecture using the random forest machine-learning algorithm. The random forest algorithm will classify normal and attack packets based on flow entries. If packets are classified as a DDoS attack, it will be mitigated by adding flow rules to the switch. Based on tests that have been done, the detection system can detect DDoS attacks with an average accuracy of 98.38% and an average detection time of 36 ms. Then the mitigation system can mitigate DDoS attacks with an average mitigation time of 1179 ms and can reduce the average number of attack packets that enter the victim host by 15672 packets and can reduce the average number of CPU usage on the controller by 44,9%.

Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.

During this period of high-speed internet, there are a number of serious challenges for software security protection of software design, especially throughout the life cycle of the process of software design, in which there are various risks involving information interaction. Significant information leakage can result from a lack of technical support and software security protection. One major problem with regard to creating software that includes security is the way that secure software is defined and the methods that are used for the measurement of security. The point of this research work is on the software engineers' perspective regarding security in the stage of software design. The tools for the measurement of the metrics are employed for the evaluation of the software's security. In this case study, a metric category of design are used, which are assumed to provide quantitative data about the software's security.

Internet of Things (IoT) wireless devices has the capability to interconnect small footprint devices and its key purpose is to have seamless connection without operational barriers. It is built upon a three-layer (Perception, Transportation and Application) protocol stack architecture. A multitude of security principles must be imposed at each layer for the proper and efficient working of various IoT applications. In the forthcoming years, it is anticipated that IoT devices will be omnipresent, bringing several benefits. The intrinsic security issues in conjunction with the resource constraints in IoT devices enables the proliferation of security vulnerabilities. The absence of specifically designed IoT frameworks, specifications, and interoperability issues further exacerbate the challenges in the IoT arena. This paper conducts an investigation in IoT wireless security with a focus on the major security challenges and considerations from an IoT protocol stack perspective. The vulnerabilities in the IoT protocol stack are laid out along with a gap analysis, evaluation, and the discussion on countermeasures. At the end of this work, critical issues are highlighted with the aim of pointing towards future research directions and drawing conclusions out of it.

People are dependent on Trusted Third Party (TTP) administration based Centralized systems for content sharing having a deficit of security, faith, immutability, and clearness. This work has proposed a file-sharing environment based on Blockchain by clouting the Interplanetary File System (IPFS) and Public Key Infrastructure (PKI) systems, advantages for overcoming these troubles. The smart contract is implemented to control the access privilege and the modified version of IPFS software is utilized to enforce the predefined access-control list. An application framework on a secure decentralized file sharing system is presented in combination with IPFS and PKI to secure file sharing. PKI having public and private keys is used to enable encryption and decryption of every file transaction and authentication of identities through Metamask to cryptographically recognize account ownership in the Blockchain system. A gas consumption-based result analysis is done in the private Ethereum network and it attains transparency, security managed access, and quality of data indicating better efficacy of this work.

Searchable symmetric encryption enables users with the secret key to conduct keyword search on encrypted data without decryption. Recently, dynamic searchable symmetric encryption (DSSE) which provides secure functionalities for adding or deleting documents has been studied extensively. Many DSSE schemes construct indexes in order to efficiently conduct keyword search. On the other hand, the indexes constructed in DSSE are complicated and independent to indexes supported by database management systems (DBMSs). Plug-in developments over DBMSs are often restricted, and therefore it is not easy to develop softwares which can deploy DSSE schemes to DBMSs. In this paper, we propose a DBMS-friendly searchable symmetric encryption scheme which can generate indexes suitable for DBMSs. Our index can narrow down encrypted data which should be conducted keyword search, and be combined with well-used indexes supported by many DBMSs. Our index consists of a small portion of an output value of a cryptographic deterministic function (e.g. pseudo-random function or hash function). We also show an experiment result of our scheme deployed to DBMSs.

Autonomous robots are increasingly widespread in our society. These robots need to be safe, reliable, respectful of privacy, not manipulable by external agents, and capable of offering explanations of their behavior in order to be accountable and acceptable in our societies. Companies offering robotic services will need to provide mechanisms to address these issues using High Performance Computing (HPC) facilities, where logs and off-line forensic analysis could be addressed if required, but these solutions are still not available in software development frameworks for robots. The aim of this paper is to discuss the implications and interactions among cybersecurity, safety, and explainability with the goal of making autonomous robots more trustworthy.

Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.

The pervasive presence of smart objects in almost every corner of our everyday life urges the security of such embedded systems to be the point of attention. Memory vulnerabilities in the embedded program code, such as buffer overflow, are the entry point for powerful attack paradigms such as Code-Reuse Attacks (CRAs), in which attackers corrupt systems’ execution flow and maliciously alter their behavior. Control-Flow Integrity (CFI) has been proven to be the most promising approach against such kinds of attacks, and in the literature, a wide range of flow monitors are proposed, both hardware-based and software-based. While the formers are hardly applicable as they impose design alteration of underlying hardware modules, on the contrary, software solutions are more flexible and also portable to the existing devices. Real-Time Operating Systems (RTOS) and their key role in application development for embedded systems is the main concern regarding the application of the CFI solutions.This paper discusses the still open challenges and issues regarding the implementation of control-flow integrity policies on operating systems for embedded systems, analyzing the solutions proposed so far in the literature, highlighting possible limits in terms of performance, applicability, and protection coverage, and proposing possible improvement directions.

The prevalence of memory corruption bugs in the past decades resulted in numerous defenses, such as stack canaries, control flow integrity (CFI), and memory-safe languages. These defenses can prevent entire classes of vulnerabilities, and help increase the security posture of a program. In this paper, we show that memory corruption defenses can be bypassed using speculative execution attacks. We study the cases of stack protectors, CFI, and bounds checks in Go, demonstrating under which conditions they can be bypassed by a form of speculative control flow hijack, relying on speculative or architectural overwrites of control flow data. Information is leaked by redirecting the speculative control flow of the victim to a gadget accessing secret data and acting as a side channel send. We also demonstrate, for the first time, that this can be achieved by stitching together multiple gadgets, in a speculative return-oriented programming attack. We discuss and implement software mitigations, showing moderate performance impact.

Modern power grids are dependent on communication systems for data collection, visualization, and control. Distributed Network Protocol 3 (DNP3) is commonly used in supervisory control and data acquisition (SCADA) systems in power systems to allow control system software and hardware to communicate. To study the dependencies between communication network security, power system data collection, and industrial hardware, it is important to enable communication capabilities with real-time power system simulation. In this paper, we present the integration of new functionality of a power systems dynamic simulation package into our cyber-physical power system testbed that supports real-time power system data transfer using DNP3, demonstrated with an industrial real-time automation controller (RTAC). The usage and configuration of DNP3 with real-world equipment in to achieve power system monitoring and control of a large-scale synthetic electric grid via this DNP3 communication is presented. Then, an exemplar of DNP3 data collection and control is achieved in software and hardware using the 2000-bus Texas synthetic grid.

Supervisory control and data acquisition (SCADA) systems are used with monitoring and control purposes for the process not to fail in industrial control systems. Today, the increase in the use of standard protocols, hardware, and software in the SCADA systems that can connect to the internet and institutional networks causes these systems to become a target for more cyber-attacks. Intrusion detection systems are used to reduce or minimize cyber-attack threats. The use of deep learning-based intrusion detection systems also increases in parallel with the increase in the amount of data in the SCADA systems. The unsupervised feature learning present in the deep learning approaches enables the learning of important features within the large datasets. The features learned in an unsupervised way by using deep learning techniques are used in order to classify the data as normal or abnormal. Architectures such as convolutional neural network (CNN), Autoencoder (AE), deep belief network (DBN), and long short-term memory network (LSTM) are used to learn the features of SCADA data. These architectures use softmax function, extreme learning machine (ELM), deep belief networks, and multilayer perceptron (MLP) in the classification process. In this study, anomaly-based intrusion detection systems consisting of convolutional neural network, autoencoder, deep belief network, long short-term memory network, or various combinations of these methods on the SCADA networks in the literature were analyzed and the positive and negative aspects of these approaches were explained through their attack detection performances.

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing \$L\$∞ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (Fe-FETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.

One of the most innovative directions in the Internet is Information Centric Networks, in particular the Named Data Network. This approach should make it easier to find and retrieve the desired information on the network through name-based addressing, intranet caching and other schemes. This article presents Named Data Network modeling, results and performance evaluation of proposed caching policies for Named Data Network research, taking into account the influence of external factors on base of Zipf's law and uniform distribution.

The 5G network systems are evolving and have complex network infrastructures. There is a great deal of work in this area focused on meeting the stringent service requirements for the 5G networks. Within this context, security requirements play a critical role as 5G networks can support a range of services such as healthcare services, financial and critical infrastructures. 3GPP and ETSI have been developing security frameworks for 5G networks. Our work in 5G security has been focusing on the design of security architecture and mechanisms enabling dynamic establishment of secure and trusted end to end services as well as development of mechanisms to proactively detect and mitigate security attacks in virtualised network infrastructures. The focus of this paper is on the latter, namely the facilities and mechanisms, and the design of a security architecture providing facilities and mechanisms to detect and mitigate specific security attacks. We have developed a simplified version of the security architecture using Software Defined Networks (SDN) and Network Function Virtualisation (NFV) technologies. The specific security functions developed in this architecture can be directly integrated into the 5G core network facilities enhancing its security.

Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR).

This paper introduces a scheme for achieving trustworthy computing on SoCs that use an outsourced AXI interconnect for on-chip communication. This is achieved through component guarding, data tagging, event verification, and consequently responding dynamically to an attack. Experimental results confirm the ability of the proposed scheme to detect HT attacks and respond to them at run-time. The proposed scheme extends the state-of-art in trustworthy computing on untrustworthy components by focusing on the issue of an untrusted on-chip interconnect for the first time, and by developing a scheme that is independent of untrusted third-party IP.