Biblio

The Internet Protocol Version 6 (IPV6) proposed to replace IPV4 to solve scalability challenges and improve quality of service and security. Current implementation of IPv6 uses static value that is determined from the Media Access Control (MAC) address as the Interface Identifier (IID). This results in a deterministic IID for each user that is the same regardless of any network changes. This provides an eavesdropper with the ability to easily track the physical location of the communicating nodes using simple tools, such as ping and traceroute. Moreover, this address generation method provides a means to correlate network traffic with a specific user which can be achieved by filtering the IID and traffic analysis. These serious privacy breaches need to be addressed before widespread deployment of IPv6. In this paper we propose a privacy-enhanced method for generating IID which combines different network parameters. The proposed method generates non-deterministic IIDs that is resistance against correlation attack. We validate our approach using Wireshark, ping and traceroute tools and show that our proposed approach achieves better privacy compared to the existing IID generation methods.

While the Internet of Things (IoT) applications and devices expanded rapidly, security and privacy of the IoT devices emerged as a major problem. Current studies reveal that there are significant weaknesses detected in several types of IoT devices moreover in several situations there are no security mechanisms to protect these devices. The IoT devices' users utilize the internet for the purpose of control and connect their machines. IoT application utilization has risen exponentially over time and our sensitive data is captured by IoT devices continuously, unknowingly or knowingly. The motivation behind this paper was the vulnerabilities that exist at the IP cameras. In this study, we undertake a more extensive investigation of IP cameras' vulnerabilities and demonstrate their effect on users' security and privacy through the use of the Kali Linux penetration testing platform and its tools. For this purpose, the paper performs a hands-on test on an IP camera with the name (“Intelligent Onvif YY HD”) to analyzes the security elements of this device. The results of this paper show that IP cameras have several security lacks and weaknesses which these flaws have multiple security impacts on users.

The necessity for peer-to-peer (p2p) communications is obvious; current centralized solutions are capturing and storing too much information from the individual people communicating with each other. Privacy concerns with a centralized solution in possession of all the users data are a difficult matter. HELIOS platform introduces a new social-media platform that is not in control of any central operator, but brings the power of possession of the data back to the users. It does not have centralized servers that store and handle receiving/sending of the messages. Instead, it relies on the current open-source solutions available in the p2p communities to propagate the messages to the wanted recipients of the data and/or messages. The p2p communications also introduce new problems in terms of privacy and tracking of the user, as the nodes part of a p2p network can see what data the other nodes provide and ask for. How the sharing of data in a p2p network can be achieved securely, taking into account the user's privacy is a question that has not been fully answered so far. We do not claim we answer this question fully in this paper either, but we propose a set of protocols to help answer one specific problem. Especially, this paper proposes how to privately share data (end-point address or other) of the user between other users, provided that they have previously connected with each other securely, either offline or online.

Path-tracking is essential to provide complete information regarding network breach incidents. It records the direction of the attack and its source of origin thus giving the network manager proper information for the next responses. Nevertheless, the existing path-tracking implementations expose the network topology and routing configurations. In this paper, we propose a privacy-aware path-tracking which mystifies network configurations using in-packet bloom filter. We apply our method by using P4 switch to supports a fine-grain (per-packet) path-tracking with dynamic adaptability via in-switch bloom filter computation. We use a hybrid scheme which consists of a destination-based logging and a path finger print-based marking to minimize the redundant path inferring caused by the bloom filter's false positive. For evaluation, we emulate the network using Mininet and BMv2 software switch. We deploy a source routing mechanism to run the evaluations using a limited testbed machine implementing Rocketfuel topology. By using the hybrid marking and logging technique, we can reduce the redundant path to zero percent, ensuring no-collision in the path-inferring. Based on the experiments, it has a lower space efficiency (56 bit) compared with the bloom filter-only solution (128 bit). Our proposed method guarantees that the recorded path remains secret unless the secret keys of every switch are known.

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.

Named Data Networking (NDN) is a content-centric networking, where the publisher of the packet signs and encapsulates the data packet with a name-content-signature encryption to verify the authenticity and integrity of itself. This scheme can solve many of the security issues inherently compared to IP networking. NDN also support mobility since it hides the point-to-point connection details. However, an extreme attack takes place when an NDN consumer newly connects to a network. A Man-in-the-middle (MITM) malicious node can block the consumer and keep intercepting the interest packets sent out so as to fake the corresponding data packets signed with its own private key. Without knowledge and trust to the network, the NDN consumer can by no means perceive the attack and thus exposed to severe security and privacy hazard. In this paper, the Name-Signature Lookup System (NSLS) and corresponding Name-Signature Lookup Protocol (NSLP) is introduced to verify packets with their registered genuine publisher even in an untrusted network with the help of embedded keys inside Network Interface Controller (NIC), by which attacks like MITM is eliminated. A theoretical analysis of comparing NSLS with existing security model is provided. Digest algorithm SHA-256 and signature algorithm RSA are used in the NSLP model without specific preference.