Data Modification Attacks Research Articles

Exploiting Zero Knowledge Proof and Blockchains Towards the Enforcement of Anonymity, Data Integrity and Privacy (ADIP) in the IoT

In recent years, the Internet of Things (IoT) has been contemplated as the next technological advancement in the era of data communication and networking. However, although hundreds of new IoT platforms are introduced to the market every few months, the security of IoT ecosystems is still not fully understood. This paper discloses the architecture of a multilayer, multimode security system for the IoT. The proposed system is capable of providing multiple security solutions that support anonymous authentication, device privacy, data integrity, device sybil attack detection and IoT server spoofing attack detection. For IoT access control and authentication, our system can support two modes of operations, with one mode endorsing device privacy protection over the network and the second mode relinquishing device identity to establish data tracing during safety-critical IoT events. The new security system includes two innovative crypto approaches, zero knowledge proof (ZKP) and blockchains. IoT device anonymity was achieved via the multimode ZKP protocol, while data integrity and protection against sybil and IoT spoofing attacks were maintained via blockchains. Our threat analysis models showed that data modification and data injection attacks are not feasible. Probabilistic modeling of an IoT spoofing attack was performed in this paper, and the results show that our security system provides high resiliency against such attacks, with a probability approaching 1.

Providing a Framework for Security Management in Internet of Things

With the advent of Internet of Things technology, tremendous changes are taking place. Perhaps what humans never even imagined will come in the near future, and just as the Internet surrounds all aspects of people's daily lives, intelligent objects will autonomously take over all aspects of people's lives. So far, a lot of research and development has been done in the field of Internet of Things, but there are still many challenges in this field. One of the most important challenges is the issue of security in the Internet of Things. Therefore, in this paper, while reviewing the requirements, models and security architectures of the Internet of Things, a framework for security management in the Internet of Things is proposed, which takes into account various aspects and requirements. The proposed framework uses various ideas such as cryptography, encryption, anomaly detection, intrusion detection, and behavior pattern analysis and can be considered as a basis for future research. The purpose of this research is to determine security requirements and provide a method to improve security management in the Internet of Things. Based on the tests, the proposed method is completely 100% resistant against data modification attacks. Against impersonation attacks up to 97% and against denial of service attacks up to 89% resistant detection accuracy.

Artificial intelligence in a rugged design based on multi-bit rules

In this paper, the technologies for training large artificial neural networks are considered: the first technology is based on the use of multilayer “deep” neural networks; the second technology involves the use of a “wide” single-layer network of neurons giving 256 private binary solutions. A list of attacks aimed at the simplest one-bit neural network decision rule is given: knowledge extraction attacks and software data modification attacks; their content is considered. All single-bit decision rules are unsafe for applying. It is necessary to use other decision rules. The security of applying neural network decision rules in relation to deliberate hacker attacks is significantly reduced if you use a decision rule of a large number of output bits. The most important property of neural network transducers is that when it is trained using 20 examples of the “Friend” image, the “Friend” output code of 256 bits long is correctly reproduced with a confidence level of 0.95. This means that the entropy of the “Friend” output codes is close to zero. A well-trained neural network virtually eliminates the ambiguity of the “Friend” image data. On the contrary, for the “Foe” images, their initial natural entropy is enhanced by the neural network. The considered works made it possible to create a draft of the second national standard for automatic training of networks of quadratic neurons with multilevel quantizers.

Exploring New Paradigm for COVID-19 Contact Tracing Through the Eye of IoT: A Blockchain Approach

— Existing contact tracing technologies for the novel Coronavirus were built to take advantage of the user’s cellphone embedded features. Specifically, exposure notification system and Bluetooth proximity were widely adopted by many of today’s Covid-19 contact tracing technologies. This paper proposes a new technological paradigm that supports contact tracing capability for the Covid-19 virus. The proposed system’s models can be used to determine potential virus outbreaks within a controlled community. In this paradigm, we primarily focus on the enrichment of contact tracing capability for communities that have limited access to smartphones (e.g. children's schools, senior homes, and factories/warehouses). Serval technological and crypto innovations were incorporated into the proposed system’s model design: (i) Internet-of-Things contact tracing data logger (ii) Two-factor Authentication (iii) User’s anonymity and data integrity of the contact tracing data (iv) detection and localization of outbreaks in a community. Contact tracing data for Covid-19 are collected using usersauthorized IoT devices. Accesses to the Covid-19 contact tracing system were established by a new two-factor authentication. The proposed authentication scheme combined public key crypto, a blockchain validation approach, and a random token generator. Meanwhile, user anonymity was achieved via the exchange of randomly generated IDs, computed by light-weight crypto hash for supporting data logging of Covid-19 traces. To preserve the integrity of Covid-19 contacts tracing data, we have adapted a blockchain methodology. The proposed approach protects user data against data injection and data modification attacks. Finally, we have analyzed the resiliency of the proposed system’s models against two well-known IoT-based attack models: (i) Sybil attack (ii) Replay attacks. Two probabilistic models were introduced in this research that present system resiliency under different scales of threats

An Autoencoder-Based Network Intrusion Detection System for the SCADA System

The intrusion detection system (IDS) is the main tool to do security monitoring that is one of the security strategies for the supervisory control and data acquisition (SCADA) system. In this paper, we develop an IDS based on the autoencoder deep learning model (AE-IDS) for the SCADA system. The target SCADA communication protocol of the detection model is the Distributed Network Protocol 3 (DNP3), which is currently the most commonly utilized communication protocol in the power substation. Cyberattacks that we consider are data injection or modification attacks, which are the most critical attacks in the SCADA systems. In this paper, we extracted 17 data features from DNP3 communication, and use them to train the autoencoder network. We measure accuracy and loss of detection and compare them with different supervised deep learning algorithms. The unsupervised AE-IDS model shows better performance than the other deep learning IDS models.

ECS‐CP‐ABE: A lightweight elliptic curve signcryption scheme based on ciphertext‐policy attribute‐based encryption to secure downlink multicast communication in edge envisioned advanced metering infrastructure networks

AbstractAs an integral part of the smart grid, advanced metering infrastructure (AMI) implements two‐way communication to enable the smart grid achieving the desired performance improvements over the legacy grid. Integrating communication in addition to the inherited weakness of the power grid paves the way to countless number of security threats to target its communication networks, therefore ensuring secure data exchange is a key concern for AMI networks. In this regard, many schemes are proposed in the literature to secure this two‐way communication, however, very little effort has been focused on multicast transmissions carried out between the control center and a group of smart meters. In order to empower efficient, authentic, and confidential data exchange, this article proposes a new signcryption scheme for multicast downlink communication in edge envisioned AMI networks. The proposed scheme is constructed based on ciphertext‐policy attribute‐based encryption to attain secure fine‐grained access control for the multirecipient communication between the utility control center and a group of smart meters. The novelty of our scheme lies behind the fact that it is an elliptic curve cryptosystem that is pairing free, in opposite to most ABE schemes. Instead of the complex bilinear pairing operations, we exploited faster elliptic curve point multiplication operations to construct the proposed signcryption model. we demonstrate the strength of our protocol in defending against passive, collusion, reply, data modification, and signature forgery attacks. Simulation results prove the efficiency of our protocol in terms of messages size and computation complexity when compared with other schemes.

Network Intrusion Detection based on Deep Neural Networks for the SCADA system

Security monitoring is one of the security strategies for the supervisory control and data acquisition (SCADA) systems, and the intrusion detection system (IDS) is a main tool to do security monitoring. Main task of security monitoring is to develop the SCADA-specific IDS, which reflects the semantics of the SCADA domain. In this paper, we work on developing IDS based on deep learning models for the SCADA system. The target SCADA communication protocol of the detection model is the DNP3, which is currently the most commonly utilized communication protocol in the power substation. The attack of major concern is data injection or modification attacks, which is most critical attack in the SCADA system. We extract 12 data features from distributed network protocol 3 (DNP3) packets, and use them to train the deep neural network. We measure the accuracy and loss of the detection system trained based on different deep learning algorithms, and show the comparison of the results.

ELPC-Trust Framework for Wireless Sensor Networks

Wireless Sensor Network (WSN) and its security concern play a vital part in its effecting functioning. WSN routing layer attacks pose a great threat to its functionality, whereas if a situation arises in which multiple attacks co-exist, then the scenario will become worse. In such cases the reliability of the network deployed is greatly questionable. Thus an Intrusion detection cum Trust based framework is mandatory to detect such co existing malicious activities in order to maintain the network reliability. Energy packed Cluster heads and sink nodes are utilized for this kind of trust computation process. To achieve this, an energy based abnormality detection of malicious nodes is proposed, upon which based on the packet rate and the lifetime of sensor nodes a trust value is computed. The trust framework involves two phases namely Threat Detection phase which performs attack categorization based on Energy, Packet Count and Z scores followed by the second phase which computes the trust values for each node. Trust scores are computed based on the intensity of the attack in terms of their network performance degradation. Attacks such as Gray hole, Black hole, Wormhole, Flooding and Data Modification attacks are addressed. Data Modification attacks are detected uniquely based on the Z scores values of the sensed data set and upon the location of the nodes. If any node is found to consume abnormal energy either below or above certain threshold, identified nodes are categorized into two groups based on their threshold. Hierarchical decision making is introduced, which implies only the abnormal energy consuming nodes (among the total sensor nodes deployed in the network) are considered for further packet count based attack categorization. This way we can reduce the computational overhead of traditional trust models in which each node is checked for its reliability against multiple trust factors. Upon detecting the attacks and computing the trust values of nodes, only the nodes which have a nominal trust value are included in the routing process. Simulations were carried out in NS2 Mannasim Framework with static and mobile sensor nodes. Proposed Energy-Lifetime-Packet Count (ELPC)-Trust framework was tested under five different types of attack scenarios in a co-existing manner. Performance of the network by including the proposed ELPC trust and by ignoring the detected malicious nodes increases the throughput by 500 and PDR by 40% when compared with the attack scenario. But on the other hand introduces a delay of 0.5 seconds. Network Performance is improved in case of static networks. Under mobile scenarios proposed ELPC trust incurs high amount of delay.

Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing

In recent years, the area of fog computing is receiving maximum focus due to the potential improvements in the cloud computing field. Fog computing is capable of resolving issues that includes location awareness, inadequate mobility support and high latency in the cloud computing environment. The internet of things (IoT) comprises of a collection of IoT equipments connected to fog nodes in order to aid the cloud service center for storing and processed a portion of data in prior. This process of storing and processing greatly minimizes the pressure in data processing with enhanced service and real time quality. However, data modification attacks and false data injection attacks introduce significant challenges over the fog nodes during the event of data processing. In this paper, Hybrid GSW (Gentry, Sahai, and Waters) and DM (Ducas and Micciancio) based fully homomorphic encryption (HGSW–DM–FHE) scheme was proposed for handling false data injection attacks under privacy preserving data aggregation in fog computing. This proposed HGSW–DM–FHE scheme is highly fault tolerant, since the process of data aggregation from other devices is not influenced even when the fog devices fails in operation. In addition, the proposed HGSW–DM–FHE scheme is determined to be efficient and secure by preventing the data injected from legal IoT devices are unaltered and protected immaterial to the honesty and maliciousness of the cloud control center and fog nodes.

Modeling software architecture design on data storage security in cloud computing environments

Cloud-based computation is known as the source architecture of the upcoming generation of IT enterprise. In context to up-coming trade solutions, the Information Technology sections are established under logical, personnel, and physical control, it transfers application software and large database to appropriate data centers, where security and management of database with services are not trustworthy fully. So this process may face many challenges towards society and organizations and that not been well understood over a while duration. This becomes one of the major challenges days today. So in this research, it focuses on security-based data storage using cloud, which plays one of the important aspects bases on qualities of services. To assure user data correctness in the cloud system, a flexible and effective distributed technique with two different salient features was examined by utilizing the token called homomorphic with erasure-coded data for distributed verification, based on this technique it achieved error data localization and integration of storage correctness. Also, it identifies server misbehaving, efficient, and security-based dynamic operations on data blocking such as data append, delete, and update methods. Performance analysis and security show the proposed method is more effective resilient and efficient against Byzantine failure, even server colluding attacks and malicious data modification attacks.

Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET.

Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

Distributed scheme to authenticate data storage security in cloud computing

Cloud Computing is the revolution in current generation IT enterprise. Cloud computing displaces database and application software to the large data centers, where the management of services and data may not be predictable, where as the conventional solutions, for IT services are under proper logical, physical and personal controls. This aspect attribute, however comprises different security challenges which have not been well understood. It concentrates on cloud data storage security which has always been an important aspect of quality of service (QOS). In this paper, we designed and simulated an adaptable and efficient scheme to guarantee the correctness of user data stored in the cloud and also with some prominent features. Homomorphic token is used for distributed verification of erasure – coded data. By using this scheme, we can identify misbehaving servers. In spite of past works, our scheme supports effective and secure dynamic operations on data blocks such as data insertion, deletion and modification. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centers, where the data management and services may not be absolutely truthful. This effective security and performance analysis describes that the proposed scheme is extremely flexible against malicious data modification, convoluted failures and server clouding attacks.

Secure overlay communication and control model for decentralized autonomous control of smart micro-grids

Abstract The smart grid is expected to self-heal, self-optimize, self-configure, and self-protect from physical and cyber-disturbances. These requirements are expected to be built into intelligent electronic devices (IEDs), which would enable autonomous control of power distribution buses in the smart grid. Power distribution buses are used to redistribute electric loads across power generation sources, control voltage/VAR, satisfy economic dispatch objectives and improve the resiliency of the grid against physical disturbance and cyber-attacks. The current power grid has a centralized communication system with a centralized human-in-the-loop controller (such as in a typical SCADA system) in order to adjust operating configurations of equipment within a distribution zone. Implementing autonomous functions by moving from a centralized control architecture to a decentralized architecture can empower bus controllers with greater control over local equipment and operations. We present the secure overlay communication model (SOCOM) for smart microgrids as a solution to decentralizing the traditional centralized control architecture and a decentralized communication platform for peer bus controllers to operate and optimize operations of the grid. We demonstrate the utility of this overlay model in an 11-Bus microgrid network to achieve self-healing. Using this model, we were able to accurately detect data modification attacks that target the controller’s state-estimation and malicious control of switching devices.

Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing.

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Cloud Storage Auditing by Utilizing Provable Data Possession Method

Cloud storage permits users to remotely store their knowledge and revel in the on-demand top quality cloud applications while not the burden of native hardware and software package management. Though the advantages are clear, such a service is addition relinquishing users’ physical possession of their outsourced knowledge, which necessarily poses new security risks towards the correctness of the knowledge in cloud. In order to deal with this new drawback and additional succeed a secure and dependable cloud storage service, we tend to propose during this paper we introduce a model for provable data possession (PDP) that allows a client that has Stored data at an untrusted server to verify that the server possesses the original data without capturing it.The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs and also propose a versatile distributed storage integrity auditing mechanism, implementing the homomorphic token and distributed erasure-coded information. The planned style permits users to audit the cloud storage with terribly light-weight communication and computation value. The auditing result not solely ensures robust cloud storage correctness guarantee, but at the same time at identical time achieves fast information error localization, i.e., the recognition of misbehaving server. Considering the cloud information are energetic in nature, the proposed design further supports secure and desirable dynamic operations on outsourced information, including block modification, deletion, and append. Analysis shows the planned theme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and continuous server colluding attacks.

Towards a formally verified zero watermarking scheme for data integrity in the Internet of Things based-wireless sensor networks

The Internet of Things (IoT) is an emerging paradigm in which billions of devices communicate, thus producing and exchanging information related to real world objects (things). Sensor nodes are specialized nodes for handling transmission of a large volume of data in situations where the source nodes communicate with base stations (BS) via a set of intermediate nodes. Applications based on WSN claim that integrity and trustworthiness are the key aspects as the data received from source nodes is the major source for BS to take critical decisions. To establish the trustworthiness between sensor node and BS, a novel zero watermarking scheme is proposed in this paper. In order to ensure integrity and trustworthiness, our proposed scheme embeds a watermark in original data before it is transmitted to BS which is responsible for verifying the watermark embedded with data. We have compared proposed scheme with existing Asymmetric Cryptography (ACT) and Reversible Watermarking (RW) schemes based on the performance parameters such as computational overhead and the energy utilization. Analysis results suggest that proposed scheme can handle multiple attacks on data and watermark such as data deletion, data modification, and data insertion attack. Moreover, our experimental results demonstrate that the presented scheme is lightweight, computationally efficient, and better in energy utilization. A formal verification for proof of correctness using high level Petri nets (HLPNs) is also provided to verify the claims of our work.

A Proxy-Based Solution for Asynchronous Telemedical Systems

Asynchronous telemedicine systems face many challenges related to information security as the patient's sensitive information and data on medicine dosage is transmitted over a network when monitoring patients and controlling asynchronous telemedical IoT devices. This information may be modified or spied on by a malicious adversary. To make asynchronous telemedicine systems more secure, the authors present a proxy-based solution against data modification and spying attacks in web-based telemedical applications. By obfuscating the executable code of a web application and by continuously dynamically changing obfuscation, the authors' solution makes it more difficult for a piece of malware to attack its target. They use a constructive research approach. They characterize the threat and present an outline of a proposed solution. The benefits and limitations of the proposed solution are discussed. Cyber-attacks targeted at the information related to patient's care are a serious threat in today's telemedicine. If disregarded, these attacks have negative implications on patient safety and quality of care.

Classification and technical analysis of Network Intrusion Detection Systems

System and network technology provide great convenience for rapid development. Network infrastructure devices help greatly in managing various spines of any enterprise like bank accounts, online transaction, transportation, social insurance, protection, correspondence and computer networks systems; but at the same time may suffer from unauthorized access, data theft, network abuse, data modification and DOS (denial of service) attacks which prevent assurance of continuous service to legitimate users of the network. For these reasons, The detection of attacks has been an important aspect of the efforts of uncovered system scam. Network security experts need much more alert accuracy and overall threat analysis in the number of ways to secure their network within cyberspace. Improvements in network intrusion detection and a more comprehensive approach can be made possible from several different hybrid sources. Network intrusion detection (NID) is the answer to all these threats and has regularly been studied in both educational and industrially by various renowned authors. This paper presents the findings and conclusions obtained from detailed literature review and gives a survey on the state of art of this research. Some conclusions on research trends are also discussed.

Secure Wireless Cloud Data Storage using Hierarchical-Attribute based Encryption with Identity Based Encryption

Cloud Computing is a new paradigm for the provisioning of every network accessible Storage as a Service (SaaS). In these services provided by cloud, we consider Storage-as-a-Service which offered by cloud computing allows the user to outsource their sensitive data on cloud servers. When the users are outsourcing their data on the cloud, they want their data to be guaranteed with Confidentiality, Integrity and proper access control. In the present work we focus on a cloud based storage system where users can benefit from the facilities offered by the encryption algorithm. In cloud computing cloud users and cloud services providers are almost differ from trust domains. Wired data storage does not have that much authentication than wireless. Wireless have more interactive with everyone. We have proposed wireless cloud storage, it accessed by authenticated user only. In this paper, we are going to analysis various encryption algorithm for privacy and more security. It have more possible solutions for their limitations, that consist of Attribute based encryption (ABE), KP (key policy)-ABE, CP (cipher text)-ABE, H(Hierarchical)-ABE. This technique is more secured even though the storage of cloud is not trustworthy. The existing system which is Attribute Based Encryption systems which labels the encoded data into user's keys. The proposed design supports continued safe and efficient dynamic activities, including block modification, deletion and append. The proposed system is very effective against data modification attack using H-ABE with combination of Identity Based Encryption (IBE).

Distributed Scheme to Authenticate Data Storage Security in Cloud Computing

Cloud Computing is the revolution in current generation IT enterprise. Cloud computing displaces database and application software to the large data centres, where the management of services and data may not be predictable, where as the conventional solutions, for IT services are under proper logical, physical and personal controls. This aspect attribute, however comprises different security challenges which have not been well understood. It concentrates on cloud data storage security which has always been an important aspect of quality of service (QOS). In this paper, we designed and simulated an adaptable and efficient scheme to guarantee the correctness of user data stored in the cloud and also with some prominent features. Homomorphic token is used for distributed verification of erasure – coded data. By using this scheme, we can identify misbehaving servers. In spite of past works, our scheme supports effective and secure dynamic operations on data blocks such as data insertion, deletion and modification. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centres, where the data management and services may not be absolutely truthful. This effective security and performance analysis describes that the proposed scheme is extremely flexible against malicious data modification, convoluted failures and server clouding attacks.