Biblio

The Internet Transport Protocol (ITP) is introduced to support reliable end-to-end transport services in the IP Internet without the need for end-to-end connections, changes to the Internet routing infrastructure, or modifications to name-resolution services. Results from simulation experiments show that ITP outperforms the Transmission Control Protocol (TCP) and the Named Data Networking (NDN) architecture, which requires replacing the Internet Protocol (IP). In addition, ITP allows transparent content caching while enforcing privacy.

With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis.

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and -0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.

As a new paradigm for the monitoring and troubleshooting of backbone networks, the multilayer in-band network telemetry (ML-INT) with deep learning (DL) based data analytics (DA) has recently been proven to be effective on realtime visualization and fine-grained monitoring. However, the existing studies on ML-INT&DA systems have overlooked the privacy and security issues, i.e., a malicious party can apply tapping in the data reporting channels between the data and control planes to illegally obtain plaintext ML-INT data in them. In this paper, we discuss a privacy-preserving DL-based ML-INT&DA system for realizing AI-assisted network automation in backbone networks in the form of IP-over-Optical. We first show a lightweight encryption scheme based on integer vector homomorphic encryption (IVHE), which is used to encrypt plaintext ML-INT data. Then, we architect a DL model for anomaly detection, which can directly analyze the ciphertext ML-INT data. Finally, we present the implementation and experimental demonstrations of the proposed system. The privacy-preserving DL-based ML-INT&DA system is realized in a real IP over elastic optical network (IP-over-EON) testbed, and the experimental results verify the feasibility and effectiveness of our proposal.

Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.

With the emergence of smart automotive devices, the data communication between these devices gains increasing importance. SOME/IP is a light-weight protocol to facilitate inter- process/device communication, which supports both procedural calls and event notifications. Because of its simplicity and capability, SOME/IP is getting adopted by more and more automotive devices. Subsequently, the security of SOME/IP applications becomes crucial. However, previous security testing techniques cannot fit the scenario of vulnerability detection SOME/IP applications due to miscellaneous challenges such as the difficulty of server-side testing programs in parallel, etc. By addressing these challenges, we propose Ori - a greybox fuzzer for SOME/IP applications, which features two key innovations: the attach fuzzing mode and structural mutation. The attach fuzzing mode enables Ori to test server programs efficiently, and the structural mutation allows Ori to generate valid SOME/IP packets to reach deep paths of the target program effectively. Our evaluation shows that Ori can detect vulnerabilities in SOME/IP applications effectively and efficiently.

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.

Existing logic-locking attacks are known to successfully decrypt functionally correct key of a locked combinational circuit. It is possible to extend these attacks to real-world Silicon-based Intellectual Properties (IPs, which are sequential circuits) through scan-chains by selectively initializing the combinational logic and analyzing the responses. In this paper, we propose SeqL, which achieves functional isolation and locks selective flip-flop functional-input/scan-output pairs, thus rendering the decrypted key functionally incorrect. We conduct a formal study of the scan-locking problem and demonstrate automating our proposed defense on any given IP. We show that SeqL hides functionally correct keys from the attacker, thereby increasing the likelihood of the decrypted key being functionally incorrect. When tested on pipelined combinational benchmarks (ISCAS, MCNC), sequential benchmarks (ITC) and a fully-fledged RISC-V CPU, SeqL gave 100% resilience to a broad range of state-of-the-art attacks including SAT [1], Double-DIP [2], HackTest [3], SMT [4], FALL [5], Shift-and-Leak [6] and Multi-cycle attacks [7].

Logic encryption is a popular Design-for-Security(DfS) solution that offers protection against the potential adversaries in the third-party fab labs and end-users. However, over the years, logic encryption has been a target of several attacks, especially Boolean satisfiability attacks. This paper exploits SAT attack's inability of deobfuscating sequential circuits as a defense against it. We propose several strategies capable of preventing the SAT attack by obfuscating the scan-based Design-for-Testability (DfT) infrastructure. Unlike the existing SAT-resilient schemes, the proposed techniques do not suffer from poor output corruption for wrong keys. This paper also offers various probable solutions for inserting the key-gates into the circuit that ensures protection against numerous other attacks, which exploit weak key-gate locations. Along with several gate-level obfuscation strategies, this paper also presents a Cellular Automata (CA) guided FSM obfuscation strategy to offer protection at a higher abstraction level, that is, RTL-level. For all the proposed schemes, rigorous security analysis against various attacks evaluates their strengths and limitations. Testability analysis also ensures that none of the proposed techniques hamper the basic testing properties of the ICs. We also present a CA-based FSM watermarking strategy that helps to detect potential theft of the designer's IP by any adversary.

Distributed denial of service (DDoS) attacks can bring tremendous damage to online services and ISPs. Existing adopted mitigation methods either require the victim to have a sufficient number of resources for traffic filtering or to pay a third party cloud service to filter the traffic. In our previous work we proposed CoFence, a collaborative network that allows member domains to help each other in terms of DDoS traffic handling. In that network, victim servers facing a DDoS attack can redirect excessive connection requests to other helping servers in different domains for filtering. Only filtered traffic will continue to interact with the victim server. However, sending traffic to third party servers brings up the issue of privacy: specifically leaked client source IP addresses. In this work we propose a privacy protection mechanism for defense so that the helping servers will not be able to see the IP address of the client traffic while it has minimum impact to the data filtering function. We implemented the design through a test bed to demonstrated the feasibility of the proposed design.

GDSII layouts of IP-confidential products are heavily controlled and access is only granted to certain privileged personnel. Failure analysts are generally excluded. Without guidance from GDSII, failure analysis, specifically physical inspection based on fault isolation findings cannot proceed. To overcome this challenge, we develop an automated approach that enables image snapshots relevant to failure analysts to be furnished without compromising the confidentiality of the GDSII content in this paper. Modules built are executed to trace the suspected nets and extract them into multiple images of different pre-defined frame specifications to facilitate failure analysis.

IP geolocation technology is widely adopted in network security, privacy protection, online advertising, etc. However, existing IP geolocation methods are vulnerable to delay inflation, which reduces their reliability and applicability, especially in weakly connected networks. To solve this problem, a ranking nodes based IP geolocation method (RNBG) is proposed. RNBG leverages the scale-free nature of complex networks to find a few important and stable nodes in networks. And then these nodes are used in the geolocation of IPs in different regions. Experimental results in China and the US show that RNBG can achieve high accuracy even in weakly connected network. Compared with typical methods, the geolocation accuracy is increased by 2.60%-14.27%, up to 97.55%.

Directional Overcurrent Relays (DOCRs) play an essential role in the power system protection to guarantee the reliability, speed of relay operation and avoiding mal-trip in the primary and backup relays when unintentional fault conditions occur in the system. Moreover, the dual setting protection scheme is more efficient protection schemes for offering fast response protection and providing flexibility in the coordination of relay. In this paper, the Adaptive Modified Firefly Algorithm (AMFA) is used to determine the optimal coordination of dual setting DOCRs in the ring distribution system. The AMFA is completed by choosing the minimum value of pickup current (\textbackslashtextbackslashpmbI\textbackslashtextbackslashpmbP) and time dial setting (TDS). On the other hand, dual setting DOCRs protection scheme also proposed for operating in both forward and reverse directions that consisted of individual time current characteristics (TCC) curve for each direction. The previous method is applied to the ring distribution system network of PT. Pupuk Sriwidjaja by considering the fault on each bus. The result illustration that the AMFA within dual setting protection scheme is significantly reaching the optimized coordination and the relay coordination is certain for all simulation scenarios with the minimum operation. The AMFA has been successfully implemented in MATLAB software programming.

In this paper Intelligent Electronic Devices (IED) that use ethernet for communicating with substation devices on the grid where modelled in OPNET. There is a need to test the communication protocol performance over the network. A model for the substation communication network was implemented in OPNET. This was done for ESKOM, which is the electrical power generation and distribution authority in South Africa. The substation communication model consists of 10 ethernet nodes which simulate protection Intelligent Electronic Devices (IEDs), 13 ethernet switches, a server which simulates the substation Remote Terminal Unit (RTU) and the DNP3 Protocol over TCP/IP simulated on the model. DNP3 is a protocol that can be used in a power utility computer network to provide communication service for the grid components. It was selected as the communication protocol because it is widely used in the energy sector in South Africa. The network load and packet delay parameters were sampled when 10%, 50%, 90% and 100% of devices are online. Analysis of the results showed that with an increase in number of nodes there was an increase in packet delay as well as the network load. The load on the network should be taken into consideration when designing a substation communication network that requires a quick response such as a smart gird.

The globalization of IC manufacturing has increased the likelihood for IP providers to suffer financial and reputational loss from IP piracy. Logic locking prevents IP piracy by corrupting the functionality of an IP unless a correct secret key is inserted. However, existing logic-locking techniques can impose significant area overhead and performance impact (delay and power) on designs. In this work, we propose BISTLock, a logic-locking technique that utilizes built-in self-test (BIST) to isolate functional inputs when the circuit is locked. We also propose a set of security metrics and use the proposed metrics to quantify BISTLock's security strength for an open-source AES core. Our experimental results demonstrate that BISTLock is easy to implement and introduces an average of 0.74% area and no power or delay overhead across the set of benchmarks used for evaluation.

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%.

In modern internet-scale computing, interaction between a large number of parties that are not known a-priori is predominant, with each party functioning both as a provider and consumer of services and information. In such an environment, traditional access control mechanisms face considerable limitations, since granting appropriate authorizations to each distinct party is infeasible both due to the high number of grantees and the dynamic nature of interactions. Trust management has emerged as a solution to this issue, offering aids towards the automated verification of actions against security policies. In this paper, we present a trust- and risk-based approach to security, which considers status, behavior and associated risk aspects in the trust computation process, while additionally it captures user-to-user trust relationships which are propagated to the device level, through user-to-device ownership links.

The complexity of building, deploying, and managing cross-organizational enterprise computing services with self-service, security, and quality assurances has been increasing exponentially in the era of hybrid multiclouds. AI-accelerated material discovery capabilities, for example, are desirable for enterprise application users to consume through business API services with assurance of satisfactory nonfunctional properties, e.g., enterprise-compliant self-service management of sharable sensitive data and machine learning capabilities at Internet scale. This paper presents a composable microservices based approach to creating and continuously improving enterprise computing services. Moreover, it elaborates on several key architecture design decisions for Navarch, a composable enterprise microservices fabric that facilitates consuming, managing, and composing enterprise API services. Under service management model of individual administration, every Navarch microservice is a managed composable API service that can be provided by an internal organization, an enterprise partner, or a public service provider. This paper also illustrates a Navarch-enabled systematic and efficient approach to transforming an AI-accelerated material discovery tool into secure, scalable, and composable enterprise microservices. Performance of the microservices can be continuously improved by exploiting advanced heterogeneous microservice hosting infrastructures. Factual comparative performance analyses are provided before the paper concludes with future work.

Distributed large-scale cyber attacks targeting the availability of computing and network resources still remain a serious threat. To limit the effects caused by those attacks and to provide a proactive defense, mitigation should move to the networks of Internet Service Providers (ISPs). In this context, this thesis focuses on a development of a collaborative, automated approach to mitigate the effects of Distributed Denial of Service (DDoS) attacks at Internet Scale. This thesis has the following contributions: i) a systematic and multifaceted study on mitigation of large-scale cyber attacks at ISPs. ii) A detailed guidance selecting an exchange format and protocol suitable to use to disseminate threat information. iii) To overcome the shortcomings of missing flow-based interoperability of current exchange formats, a development of the exchange format Flow-based Event Exchange Format (FLEX). iv) A communication process to facilitate the automated defense in response to ongoing network-based attacks, v) a model to select and perform a semi-automatic deployment of suitable response actions. vi) An investigation of the effectiveness of the defense techniques moving-target using Software Defined Networking (SDN) and their applicability in context of large-scale cyber attacks and the networks of ISPs. Finally, a trust model that determines a trust and a knowledge level of a security event to deploy semi-automated remediations and facilitate the dissemination of security event information using the exchange format FLEX in context of ISP networks.

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.

The objective of the proposed research is to develop an IOT based security system with a two-point authentication. Human face recognition and fingerprint is a known method for access authentication. A combination of both technologies and integration of the system with IoT make will make the security system more efficient and reliable. Use of online platform google firebase is made for saving database and retrieving it in real-time. In this system access to the fingerprint (touch sensor) from mobile is proposed using an android app developed in android studio and authentication for the same is also proposed. On identification of both face and fingerprint together, access to door or locker is provided.

IoT data marketplaces are being developed to help cities and communities create large scale IoT applications. Such data marketplaces let the IoT device owners sell their data to the application developers. Following this application development model, the application developers need not deploy their own IoT devices when developing IoT applications; instead, they can buy data from a data marketplace. In a marketplace-based IoT application, the application developers are making critical business and operation decisions using the data produced by seller's IoT devices. Under these circumstances, it is crucial to verify and validate the security of IoT devices.In this paper, we assess the security of IoT data marketplaces. In particular, we discuss what kind of vulnerabilities exist in IoT data marketplaces using the well-known STRIDE model, and present a security assessment and certification framework for IoT data marketplaces to help the device owners to examine the security vulnerabilities of their devices. Most importantly, our solution certifies the IoT devices when they connect to the data marketplace, which helps the application developers to make an informed decision when buying and consuming data from a data marketplace. To demonstrate the effectiveness of the proposed approach, we have developed a proof-of-concept using I3 (Intelligent IoT Integrator), which is an open-source IoT data marketplace developed at the University of Southern California, and IoTcube, which is a vulnerability detection toolkit developed by researchers at Korea University. Through this work, we show that it is possible to increase the reliability of a IoT data marketplace while not damaging the convenience of the users.

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. The content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.

Internet of Things (IoT) has been growing exponentially in the commercial market in recent years. It is also a fact that people hold one or more computing devices at home. Many of them have been developed to operate through internet connectivity with cloud computing technologies that result in the demand for fast, robust, and secure services. In most cases, the lack of these services makes difficult the transfer of data to fulfill the devices' purposes. Under these conditions, an intermediate layer or middleware is needed to process, filter, and send data through a more efficient alternative. This paper presents the adaptive solution of a middleware architecture as an intermediate layer between smart devices and cloud computing to enhance the management of the heterogeneity of data provining from IoT devices. The proposed middleware provides easy configuration, adaptability, and bearability for different environments. Finally, this solution has been implemented in the healthcare domain, in which IoT solutions are deployed into Ambient Assisted Living (AAL) environments.

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.