Biblio

In this digital era, the usage of technology has increased rapidly and led to the deployment of more innovative technologies for storing and transferring the generated data. The most important aspect of the emerging communication technologies is to ensure the safety and security of the generated huge amount of data. Hence, cryptography is considered as a pathway that can securely transfer and save the data. Cryptography comprises of ciphers that act like an algorithm, where the data is encrypted at the source and decrypted at the destination. This paper comprises of two ciphers namely PRESENT and AES ciphers. In the real-time applications, AES is no more relevant especially for segmenting the organizations that leverage RFID, Sensors and IoT devices. In order to overcome the strategic issues faced by these organization, PRESENT ciphers work appropriately with its super lightweight block figure, which has the equivalent significance to both security and equipment arrangements. This paper compares the AES (Advance encryption standard) symmetric block cipher with PRESENT symmetric block cipher to leverage in the industries mentioned earlier, where the huge consumption of resources becomes a significant factor. For the comparison of different ciphers, the results of area, timing analysis and the waveforms are taken into consideration.

Encryption has become an important need for each and every data transmission. Large amount of delicate data is transferred regularly through different computer networks such as e-banking, email applications and file exchange. Cryptanalysis is study of analyzing the hidden information in the system. The process of cryptanalysis could be done by various features such as power, sound, electromagnetic radiation etc. Lightweight cryptography plays an important role in the IoT devices. It includes various appliances, vehicles, smart sensors and RFID-tags (RFID). PRESENT is one such algorithm, designed for resource constrained devices. This requires less memory and consumes less power. The project propounds a model in which the cryptographic keys are analyzed by the trace of power.

Source location privacy (SLP) protection ensures security of assets in monitoring wireless sensor networks (WSNs). Also, low end-to-end delay (EED) and high packet delivery ratio (PDR) guarantee high packet delivery reliability. Therefore, it is important to ensure high levels of SLP protection, low EED, and high PDR in mission-critical monitoring applications. Thus, this study proposes a new angle-based agent node routing protocol (APr) which is capable of achieving high levels of SLP protection, low EED, and high PDR. The proposed APr protocol employs multiple routing strategies to enable a dynamic agent node selection process and creation of obfuscating routing paths. Analysis results reveal that the APr protocol achieves high packet delivery reliability to outperform existing intermediate node-based protocols such as the AdrR and tree-based protocols such as the TbR. Furthermore, the APr protocol achieves significantly high levels of SLP protection to outperform the AdrR protocol.

Wireless network is an alternative communication to cable, where radio wave is used as transmission media instead of copper medium. However, wireless network more vulnerable to risk in security compared to cable network. Wireless network mostly used by Internet of Things node as communication media between nodes. Hence, these nodes exposed to risk of flooding attack from third party person. Hence, a system which capability to detect flooding attack at IoT node is required. Many researches have been done before, but most of the research only focus to IPv4 and signature-based detection. IPv6-based attacks undetectable by the current research, due to different datagram structure. This paper proposed a conceptual detection method with reinforcement learning algorithm to detect IPv6-based attack targeting IoT nodes. This reward will decide whether the detection system is good or not. The assessment calculation equation is used to turn reward-based score into detection accuracy.

The number of Mobile device users in the word has evolved rapidly. Many internet users currently want to connect the internet for all utilities automatically. One of the technologies in the IPv6 network, which supports data access from moving users, is IPv6 Mobile protocol. In its mobility, the users on a range of networks can move the range to another network. High demand for this technology will interest to a hacker or a cracker to carry out an attack. One of them is a DoS attack that compromises a target to denial its services. A firewall is usually used to protect networks from external attacks. However, since the firewall based on the attacker database, the unknown may not be detected. In order to address the obstacle, a detection tool could be used. In this research, IDS as an intrusion detection tool was integrated with a firewall to be implemented in IPv6 Mobile to stop the DoS attack. The results of some experiments showed that the integration system could block the attack at 0.9 s in Correspondent Node and 1.2 s in Home Agent. The blocked attack can decrease the network throughput up to 27.44% when a Mobile Node in Home Agent, 28,87% when the Mobile Node in a Foreign Network. The final result of the blocked attack is reducing the average CPU utilization up to 30.99%.

The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.

Industrial Control Systems (ICS) have evolved in the last decade, shifting from proprietary software/hardware to contemporary embedded architectures paired with open-source operating systems. In contrast to the IT world, where continuous updates and patches are expected, decommissioning always-on ICS for security assessment can incur prohibitive costs to their owner. Thus, a solution for routinely assessing the cybersecurity posture of diverse ICS without affecting their operation is essential. Therefore, in this paper we introduce IFFSET, a platform that leverages full system emulation of Linux-based ICS firmware and utilizes fuzzing for security evaluation. Our platform extracts the file system and kernel information from a live ICS device, building an image which is emulated on a desktop system through QEMU. We employ fuzzing as a security assessment tool to analyze ICS specific libraries and find potential security threatening conditions. We test our platform with commercial PLCs, showcasing potential threats with no interruption to the control process.

In the last couple of years ARM-based servers have been gradually adopted by cloud service providers and utilized in the data centers. Such tendency may provide great business opportunities for various companies in the industry. Hence, the ability to timely track the development trend of the ARM-based server ecosystem (ASE) from technical perspective is of great importance. In this paper the level of development of the ASE is quantitatively assessed based on open-source data analysis. In particular, statistical data is extracted from 42 Linux kernels to analyze the development process of the ASE. Furthermore, an estimate of the development trend of the ASE in the next 10 years is made based on the statistical data. The estimated results provide insight on when the ASE may become as mature as today's x86-based server ecosystem.

It is predicted that, the number of connected Internet of Things (IoT) devices will rise to 38.6 billion by 2025 and an estimated 50 billion by 2030. The increased deployment of IoT devices into diverse areas of our life has provided us with significant benefits such as improved quality of life and task automation. However, each time a new IoT device is deployed, new and unique security threats emerge or are introduced into the environment under which the device must operate. Instantaneous detection and mitigation of every security threat introduced by different IoT devices deployed can be very challenging. This is because many of the IoT devices are manufactured with no consideration of their security implications. In this paper therefore, we review existing literature and present IoT threat detection research advances with a focus on the various IoT security challenges as well as the current developments towards combating cyber security threats in IoT networks. However, this paper also highlights several future research directions in the IoT domain.

Deception technology is a useful approach to improve the security posture of IoT systems. The deployment of replication techniques as a deception tactic is presented with a summary of our research progress towards quantifying the defensive improvement as part of overall risk management considerations.

In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.

Nowadays, Internet of Things (IoT) has gained considerable significance and concern, consequently, and in particular with widespread usage and adoption of the IoT applications and projects in various industries, the consideration of the IoT Security has increased dramatically too. Therefore, this paper presents a concise and a precise review for the current state of the IoT security models and frameworks. The paper also proposes a new unified criteria and characteristics, namely Formal, Inclusive, Future, Agile, and Compliant with the standards (FIFAC), in order to assure modularity, reliability, and trust for future IoT security models, as well as, to provide an assortment of adaptable controls for protecting the data consistently across all IoT layers.

As the scale of integrated circuits continues to expand, electronic design automation (EDA) and intellectual property (IP) reuse play an increasingly important role in the integrated circuit design process. Although many Web-EDA platforms have begun to provide online EDA software to reduce the threshold for the use of EDA tools, IP protection on the Web- EDA platform is an issue. This article uses blockchain technology to design an IP trading system for the Web-EDA platform to achieve mutual trust and transactions between IP owners and users. The structure of the IP trading system is described in detail, and a blockchain wallet for the Web-EDA platform is developed.

Recent advances in resistive synaptic devices have enabled the emergence of brain-inspired smart chips. These chips can execute complex cognitive tasks in digital signal processing precisely and efficiently using an efficient neuromorphic system. The neuromorphic synapses used in such chips, however, are different from the traditional integrated circuit architectures, thereby weakening their resistance to malicious transformation and intellectual property (IP) counterfeiting. Accordingly, in this paper, we propose an effective hybrid encryption methodology for IP core protection in neuromorphic computing systems, in-corporating elliptic curve cryptography and SM4 simultaneously. Experimental results confirm that the proposed method can implement real-time encryption of any number of crossbar arrays in neuromorphic systems accurately, while reducing the time overhead by 14.40%-26.08%.

The real-time information of enemy locations is capable to transform the outcome of combat operations. Such information gathered using connected soldiers on the Internet of Battlefield Things (IoBT) is highly beneficial to create situational awareness (SA) and to plan an effective war strategy. This article presents the novel enemy localization method that uses the soldier's own locations and their gunshot direction. The hardware prototype has been developed that uses a triangulation for an enemy localization in two soldiers and a single enemy scenario. 4.24±1.77 m of average localization error and ±4° of gunshot direction error has been observed during this prototype testing. This basic model is further extended using three-stage software simulation for multiple soldiers and multiple enemy scenarios with the necessary assumptions. The effective algorithm has been proposed, which differentiates between the ghost and true predictions by analyzing the groups of subsequent shooting intents (i.e., frames). Four different complex scenarios are tested in the first stage of the simulation, around three to six frames are required for the accurate enemy localization in the relatively simple cases, and nine frames are required for the complex cases. The random error within ±4° in gunshot direction is included in the second stage of the simulation which required almost double the number of frames for similar four cases. As the number of frames increases, the accuracy of the proposed algorithm improves and better ghost point elimination is observed. In the third stage, two conventional clustering algorithms are implemented to validate the presented work. The comparative analysis shows that the proposed algorithm is faster, computationally simple, consistent, and reliable compared with others. Detailed analysis of hardware and software results for various scenarios has been discussed in this article.

As a typical representative of a new generation military information technology, the value and significance of Internet of Battlefield Things (IoBT) has been widely recognized by the world's military forces. At the same time, Internet of Battlefield Things (IoBT) is facing serious scalability and security challenges. This paper presents the basic concept and six-domain model of IoBT, explains the integration security framework of IoBT and blockchain. Furthermore, we design and build a novel IoT framework called IoBTChain based on blockchain and smart contracts, which adopts a credit-based resource management system to control the amount of resources that an IoBT device can obtain from a cloud server based on pre-defined priority rules, application types, and behavior history. We illustrate the deployment procedure of blockchain and smart contracts, the device registration procedure on blockchain, the IoBT behavior regulation workflow and the pricing-based resource allocation algorithm.

MIL-STD-1553 is a military standard that defines the specification of a serial communication bus that has been implemented in military and aerospace avionic platforms for over 40 years. MIL-STD-1553 was designed for a high level of fault tolerance while less attention was paid to cyber security issues. Thus, as indicated in recent studies, it is exposed to various threats. In this article, we suggest enhancing the security of MIL-STD-1553 communication buses by integrating a machine learning-based intrusion detection system (IDS); such anIDS will be capable of detecting cyber attacks in real time. The IDS consists of two modules: 1) a remote terminal (RT) authentication module that detects illegitimately connected components and data transfers and 2) a sequence-based anomaly detection module that detects anomalies in the operation of the system. The IDS showed high detection rates for both normal and abnormal behavior when evaluated in a testbed using real 1553 hardware, as well as a very fast and accurate training process using logs from a real system. The RT authentication module managed to authenticate RTs with +0.99 precision and +0.98 recall; and detect illegitimate component (or a legitimate component that impersonates other components) with +0.98 precision and +0.99 recall. The sequence-based anomaly detection module managed to perfectly detect both normal and abnormal behavior. Moreover, the sequencebased anomaly detection module managed to accurately (i.e., zero false positives) model the normal behavior of a real system in a short period of time ( 22 s).

Microservice architectures adoption is growing expeditiously in market size and adoption, including in business-critical systems. This is due to agility in development and deployment further increased by containers and their characteristics. Ensuring security is still a major concern due to challenges faced such as resource separation and isolation, as improper access to one service might compromise complete systems. This doctoral work intends to advance the security of microservice systems through research and improvement of methodologies for detection, tolerance and mitigation of security intrusions, while overcoming challenges related to multi-tenancy, heterogeneity, dynamicity of systems and environments. Our preliminary research shows that host-based IDSes are applicable in container environments. This will be extended to dynamic scenarios, serving as a steppingstone to research intrusion tolerance techniques suited to these environments. These methodologies will be demonstrated in realistic microservice systems: complex, dynamic, scalable and elastic.

Internet of Things (IoT) has become the buzzword for the development of Smart City and its applications. In this context, development of supporting software forms the core part of the IoT infrastructure. A Middleware sits in between the IoT devices and interacts between them to exchange data among the components of the automated architecture. The Middleware services include hand shaking, data transfer and security among its core set of functionalities. It also includes cross-cutting functional services such as authentication, logging and caching. A software that can run these Middleware services requires a careful choice of a good software modelling technique. Aspect-Oriented Programming (AOP) is a software development methodology that can be used to independently encapsulate the core and cross-cutting functionalities of the Middleware services of the IoT infrastructure. In this paper, an attempt has been made using a simulation environment to independently model the two orthogonal functionalities of the Middleware with the focus to improve its modularity. Further, a quantitative measurement of the core design property of cohesion has been done to infer on the improvement in the reusability of the modules encapsulated in the Middleware of IoT. Based on the measurement, it was found that the modularity and reusability of functionalities in the Middleware software has improved in the AspectJ version compared to its equivalent Java version.

Machine to Machine (M2M) a sub area of Internet of Things (IoT) will link billions of devices or things distributed around the world using the Internet. These devices when connected exchange information obtained from the environment such as temperature or humidity from industrial or residential control process. Information Security (IS) and Trust are one of the fundamental points for users and the industry to accept the use of these devices with Confidentiality, Integrity, Availability and Authenticity. The key reason is that most of these devices use wireless media especially in residential and smart city environments. The overall goal of this work is to implement a Middleware Security to improve Safety and Security between the control network devices used in IoT/M2M and the Internet for residential or industrial environments. This implementation has been tested with different protocols as CoAP and MQTT, a microcomputer with free Real-Time Operating System (RTOS) implemented in a Raspberry Pi Gateway Access Point (RGAP), Network Address Translator (NAT), IPTable firewall and encryption is part of this implementation for secure data transmission

Due to the constrained resource and computational limitation of many Internet of Things (IoT) devices, conventional security protections, which require high computational overhead are not suitable to be deployed. Thus, vulnerable IoT devices could be easily exploited by attackers to break into networks. In this paper, we employ cyber deception and moving target defense (MTD) techniques to proactively change the network topology with both real and decoy nodes with the support of software-defined networking (SDN) technology and investigate the impact of single-target and multi-target attacks on the effectiveness of the integrated mechanism via a hierarchical graphical security model with security metrics. We also implement a web-based visualization interface to show topology changes with highlighted attack paths. Finally, the qualitative security analysis is performed for a small-scale and SDN-supported IoT network with different combinations of decoy types and levels of attack intelligence. Simulation results show the integrated defense mechanism can introduce longer mean-time-to-security-failure and larger attack impact under the multi-target attack, compared with the single-target attack model. In addition, adaptive shuffling has better performance than fixed interval shuffling in terms of a higher proportion of decoy paths, longer mean-time-to-security-failure and largely reduced defense cost.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

Massive images are being shared via a variety of ways, such as social networking. The rich content of images raise a serious concern for privacy. A great number of efforts have been devoted to designing mechanisms for privacy protection based on the assumption that the privacy is well defined. However, in practice, given a collection of images it is usually nontrivial to decide which parts of images should be protected, since the sensitivity of objects is context-dependent and user-dependent. To meet personalized privacy requirements of different users, we propose a system IEye to automatically detect private parts of images based on both common knowledge and personal knowledge. Specifically, for each user's images, multi-layered semantic graphs are constructed as feature representations of his/her images and a rule set is learned from those graphs, which describes his/her personalized privacy. In addition, an optimization algorithm is proposed to protect the user's privacy as well as minimize the loss of utility. We conduct experiments on two datasets, the results verify the effectiveness of our design to detect and protect personalized image privacy.

The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.

The upcoming 5th generation (5G) mobile networks need to support ultra-reliable communication for business and life-critical applications. To do that 5G must offer higher degree of reliability than the current Internet, where networks are often subjected to Internet attacks, such as denial of service (DoS) and unwanted traffic. Besides improving the mitigation of Internet attacks, we propose that ultra-reliable mobile networks should only carry the expected user traffic to achieve a predictable level of reliability under malicious activity. To accomplish this, we introduce device-oriented communication security policies. Mobile networks have classically introduced a policy architecture that includes Policy and Charging Control (PCC) functions in LTE. However, in state of the art, this policy architecture is limited to QoS policies for end devices only. In this paper, we present experimental implementation of a Security Policy Management (SPM) system that accounts communication security interests of end devices. The paper also briefly presents the overall security architecture, where the policies set for devices or services in a network slice providing ultra-reliability, are enforced by a network edge node (via SPM) to only admit the expected traffic, by default treating the rest as unwanted traffic.