174 research outputs found
CryptoCliqIn: Graph-Theoretic Cryptography Using Clique Injection
No full textBecause encryption is a fundamental security building block, existing encryption techniques like AES, Twofish, Blowfish, and Triple DES are constantly under the threat of being compromised. We introduce a simple graph-theoretic encryption method named CryptoCliqIn using clique injection and prove that the decryption of this encryption without the appropriate key is #P-complete. We have shown that the proposed model does not introduce delays in encryption and decryption times and provides a more secure mechanism compared to some of the existing encryption mechanisms. Finally, an adaptation of CryptoCliqIn in an intelligent system is discussed under the setup of intelligent and smart building.Srinibas Swain, Deepak Puthal, Elisa Bertin
A 2-Colorable DODAG Structured Hybrid Mode of Operations Architecture for RPL Protocol to Reduce Communication Overhead
No full textPart 3: Networking and Communications Technology for IoT (NCT)International audienceThe Routing Protocol for low-power and lossy Networks (RPL) is the established standard for packet-level communications in the Internet of Things (IoT). The efficiency of point-to-point (P2P) communications in RPL relies on the mode of operation employed by the network nodes. Hybrid modes of operation (MOP) have gained attention as they combine the advantages of both standard non-storing and storing operation modes. However, existing hybrid MOPs have struggled to achieve a balance between reduced communication overhead and storage overhead. To address this, we propose the application of the 2-colorable graph property to enable a hybrid mode of operation among IoT network nodes. By mapping the two-colorable property to the standard MOPs, we conduct experiments comparing the proposed hybrid MOP with existing approaches. Results demonstrate that our proposed hybrid MOP achieves a significant balance between storing and non-storing nodes, while also exhibiting improved average path length compared to existing hybrid MOPs. This research contributes to optimizing P2P communications in RPL-based IoT networks and highlights the potential benefits of utilizing the 2-colorable graph property in achieving an efficient hybrid mode of operation
Shadow AI: Cyber Security Implications, Opportunities and Challenges in the Unseen Frontier
No full textThe progression of artificial intelligence (AI) technologies has reached a level that greatly enhances the different organizational sectors by facilitating them with the means to advance and improve systems and processes. Shadow AI implies the usage of AI tools and systems by individuals within an entity, respectively, without permission thereby implying that these tools were not directly monitored or controlled by the centralized IT or security department. It also contributes to significant cyber risks such as data and security breaches, abuse of compliance, and, in general, an increased threat landscape. This paper highlights into the emerging global security trends and Shadow AI while also covering the unique positioning within the threat landscape concerning unauthorized computation of sensitive data, safety vulnerabilities of the unmonitored AI models, and model poisoning alongside data leakage-marked out. Moreover, this paper covers how Shadow AI distracts the attack landscape while increasing the level of security problem for the organization. Shadow AI, however, can be employed to increase the ability to respond to threats, locate irregularities, and increase the range of options available for cyber solutions even with all its risks
Lattice-modeled information flow control of big sensing data streams for smart health application
No full text© 2014 IEEE. Internet of Things (IoT) provides a promising opportunity to build powerful data analytics systems with real time event detection for smart health, and therefore wearable IoT has become a rising source of big data streams for smart health, for which security needs to be assured by detecting real-time event to avoid malicious activities, and meanwhile to control the information leakage of big sensing data streams. I refer to this as an information flow control problem. To address this problem, this paper proposes a static lattice model for information flow control over big sensing data streams. I initialize two static lattices, i.e., sensor lattice for wearable sensors and user lattice for users, and then static lattices aim to process the flow control model faster, because I am dealing with high volume and velocity of data streams. The experimental evaluation and results of the information flow model show that it can excellently handle the incoming big data streams with low latency and buffer requirement
Towards efficient and lightweight security architecture for big sensing data streams
No full textUniversity of Technology Sydney. Faculty of Engineering and Information Technology.A large number of mission critical applications from disaster management to health monitoring are contributing to the Internet of Things (IoT) by deploying a number of smart sensing devices in a heterogeneous environment. Resource constrained sensing devices are being used widely to build and deploy self-organising wireless sensor networks for a variety of critical applications. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. The key requirement of such applications is the need for near real-time stream data processing in large scale sensing networks. This trend gives birth to an area called big sensing data streams. One of the key problems in big data is to ensure end-to-end security where a Data Stream Manager (DSM) must always verify the security of the data before executing a query to ensure data security (i.e., confidentiality, integrity, authenticity, availability and freshness) as the medium of communication is untrusted. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to ensure the trustworthiness of collected data so that any decisions are made on the correct data, followed by protecting the data streams from information leakage and unauthorised access. This thesis considers end-to-end means from source sensors to cloud data centre. Although some security issues are not new, the situation is aggravated due to the features of the five Vs of big sensing data streams: Volume, Velocity, Variety, Veracity and Value. Therefore, it is still a significant challenge to achieve data security in big sensing data streams. Providing data security for big sensing data streams in the context of near real time analytics is a challenging problem.
This thesis mainly investigates the problems and security issues of big sensing data streams from the perspectives of efficient and lightweight processing. The big data streams computing advantages including real-time processing in efficient and lightweight fashion are exploited to address the problem, aiming at gaining high scalability and effectiveness. Specifically, the thesis examines three major properties in the lifecycle of security in big data streams environments. The three properties include authenticity, integrity and confidentiality also known as the AIC triad, which is different to CIA triad used in general data security. Accordingly, a lightweight security framework is proposed to maintain data integrity and a selective encryption technique to maintain data confidentiality over big sensing data streams. These solutions provide data security from source sensing devices to the processing layer of cloud data centre. The thesis also explore a further proposal on a lattice based information flow control model to protect data against information leakage and unauthorised access after performing the security verification at DSM. By integrating the access control model, this thesis provides an end-to-end security of big sensing data streams i.e. source sensing device to the cloud data centre processing layer. This thesis demonstrates that our solutions not only strengthen the data security but also significantly improve the performance and efficiency of big sensing data streams compared with existing approaches
Secure data collection and critical data transmission technique in mobile sink wireless sensor networks
No full textIn Mobile sink wireless sensor networks (MSWSN) Sensor nodes are low cost tiny devices with limited storage,computational capability and power except the sink node. Mobile sink has no resource limitation. It has wide range of application in the real world problem like military and civilian domain etc. The nodes in the network are unattended and unprotected so energy efficient and security are two major issues of sensor network. The sensors have limited battery power and low computational capability, requires a security mechanism that must be energy efficient. In this proposed system model mobile sink traverse the network to collect the data. Here we proposed energy efficient secure data collection techniques with mobile sink wireless sensor networks based on symmetric key cryptography. In proposed data collection technique mobile sink traverse network and collect data from one hop neighbors. Proposed cryptosystem is time based as after each fixed amount of time sink generates a large prime number. Using the prime number all nodes in the network update their key to avoid replay attack keep. Data collection MSWSN is three step process. At each new position mobile sink broadcast a beacon frame to alert the static sensors about its presence, secondly sensors send their sensed data towards sink node and finaly mobile sink broad cast another beacon frame to stop the data transmission by sensors. Sensor authenticate the mobile sink with the shared key concept, if it finds that sink is the legitimate node then sensor encrypt their data and transmit it to the sink. A static sensor sense some critical information and sink is not within its range, that that time sensor needs to transmit its data towards sink immediately. It cannot wait till sink come to its range. For that we proved an existing protocol Sensor Protocol for Information via Negation (SPIN) is efficient for critical data transmission to the mobile sink. Then we make it as the secured protocol by using symmetric key cryptography. Here we use the previous assumption to make it as the secure protocol. All the simulation has been carried out with NS 2.34. This thesis is supported bythe literature survey in the area of Mobile Sink Wireless Sensor Networks to make it complete
Blockchain-Based Secure Noninvasive Glucometer and Automatic Insulin Delivery System for Diabetes Management
No full textPart 2: Blockchain for IoT-Driven Systems (BIoT)International audiencePost Pandemic there has been a boost in smart health management. Internet-of-Medical-Things (IoMT) has given an end-to-end control system from the diagnosis of the disease to the cure. Security breaches in this type of hardware security can have fatal effects. The paper discusses Insulin delivery system which includes a security model of glucose measurement device along with an automated insulin pump of IoMT framework. The proposed model is used to monitor and control the glucose levels of a diabetes patient. The blockchain-based security solution is developed for the non-invasive glucometer and insulin pump for safe insulin secretion. It is helpful to mitigate challenges which are present in automatic insulin delivery. With the help of machine learning models, several results can be produced with a futuristic approach along with better understanding of the insulin to be pumped accurately
An Efficient and Secure Mechanism for Ubiquitous Sustainable Computing System
No full textPart 2: Blockchain for IoT-Driven Systems (BIoT)International audienceInternet of Things (IoT) devices are frequently utilized to collect information around humans’ daily routine, producing a need for them to regularly pair with each other. With the increasing interest in digitizing human’s natural environment and evolution of advanced application scenarios, the wireless communication networks have turned into a key player for IoT devices. As IoT devices are resource-constrained and transmit the perceived information regularly to its corresponding participant, it is mandatory for the devices to adopt a lightweight authentication scheme to overcome their limited energy availability and avoid security and privacy issues in ubiquitous sustainable computing system. Researchers have proposed protocols for IoT devices in wireless communication networks, many of which neglect numerous serious security weaknesses such as loss of identity preservation, vulnerability to replay, Man-in-the-Middle, eavesdropping attacks, and loss of key secrecy. Additionally, various security and efficiency threats in the proximity-based device authentication scheme, such as device cloning and identity loss, have a large signaling overhead. Furthermore, we evaluate the performance of the proposed schemes in terms of computation, communication and storage overhead. The results illustrate the implementation advantages and suitability of the proposed schemes for low-powered devices compared to existing protocols
Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques
No full textPart 6: Cyber Security/Privacy/Trust for IoT and CPS (SPT)International audienceSystems that use secret keys or personal details are seriously at risk from side-channel attacks, especially if they rely on power analysis. Attackers can use unintentional sources like power consumption and electromagnetic waves to extract sensitive information. Recently, machine learning has become a promising approach for executing power side-channel attacks that are efficient and effective for single and cross-device environments. This paper reviews various machine learning-based power side-channel attacks, including feature extraction techniques, classification methods, and countermeasures. This survey investigates same-device and cross-device attacks that use multiple devices for training an artificial intelligence model for this purpose. It examines the strengths and limitations of various machine learning algorithms and suggests areas for future research to address challenges
Shrew Distributed Denial-of-Service (DDoS) Attack in IoT Applications: A Survey
No full textPart 2: Energy-Aware Security for IoT (EAS)International audienceWith the rise of IoT and cloud computing, DDoS attacks have become increasingly harmful. This paper presents a survey of techniques for detecting and preventing DDoS attacks, specifically focusing on Shrew DDoS or low-rate DDoS attacks. We explore the use of machine learning for DDoS detection and prevention and introduce a new potential technique that simplifies the process of detecting and preventing DDoS attacks originating from multiple infected machines, typically known as zombie machines. As a future direction, we discuss a new technique to simplify the detection and prevention of shrew DoS attacks originating from multiple infected machines, commonly known as botnets. The insights presented in this paper will be valuable for researchers and practitioners in cybersecurity
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