Biblio

The Spoofer project has collected data on the deployment and characteristics of IP source address validation on the Internet since 2005. Data from the project comes from participants who install an active probing client that runs in the background. The client automatically runs tests both periodically and when it detects a new network attachment point. We analyze the rich dataset of Spoofer tests in multiple dimensions: across time, networks, autonomous systems, countries, and by Internet protocol version. In our data for the year ending August 2019, at least a quarter of tested ASes did not filter packets with spoofed source addresses leaving their networks. We show that routers performing Network Address Translation do not always filter spoofed packets, as 6.4% of IPv4/24 tested in the year ending August 2019 did not filter. Worse, at least two thirds of tested ASes did not filter packets entering their networks with source addresses claiming to be from within their network that arrived from outside their network. We explore several approaches to encouraging remediation and the challenges of evaluating their impact. While we have been able to remediate 352 IPv4/24, we have found an order of magnitude more IPv4/24 that remains unremediated, despite myriad remediation strategies, with 21% unremediated for more than six months. Our analysis provides the most complete and confident picture of the Internet's susceptibility to date of this long-standing vulnerability. Although there is no simple solution to address the remaining long-tail of unremediated networks, we conclude with a discussion of possible non-technical interventions, and demonstrate how the platform can support evaluation of the impact of such interventions over time.

The emergence of IPv6 protocol extends the address pool, but it also exposes all the Internet-connected devices to danger. Currently, there are some traditional schemes on security management of network addresses, such as prevention, traceability and encryption authentication, but few studies work on IPv6 protocol. In this paper, we propose a hierarchical authentication mechanism for the IPv6 source address with the technology of software defined network (SDN). This mechanism combines the authentication of three parts, namely the access network, the intra-domain and the inter-domain. And it can provide a fine-grained security protection for the devices using IPv6 addresses.

Network attacks have become a growing threat to the current Internet. For the enhancement of network security and accountability, it is urgent to find the origin and identity of the adversary who misbehaves in the network. Some studies focus on embedding users' identities into IPv6 addresses, but such design cannot support the Stateless Address Autoconfiguration (SLAAC) protocol which is widely deployed nowadays. In this paper, we propose SDN-Ti, a general solution to traceback and identification for attackers in IPv6 networks based on Software Defined Network (SDN). In our proposal, the SDN switch performs a translation between the source IPv6 address of the packet and its trusted ID-encoded address generated by the SDN controller. The network administrator can effectively identify the attacker by parsing the malicious packets when the attack incident happens. Our solution not only avoids the heavy storage overhead and time synchronism problems, but also supports multiple IPv6 address assignment scenarios. What's more, SDN-Ti does not require any modification on the end device, hence can be easily deployed. We implement SDN-Ti prototype and evaluate it in a real IPv6 testbed. Experiment results show that our solution only brings very little extra performance cost, and it shows considerable performance in terms of latency, CPU consumption and packet loss compared to the normal forwarding method. The results indicate that SDN-Ti is feasible to be deployed in practice with a large number of users.

The ongoing progressions in wireless innovation have lead to the advancement of another remote framework called Mobile Ad hoc Networks. The Mobile Ad hoc Network is a self arranging system of wireless gadgets associated by wireless connections. The traditional protocol, for example, TCP/IP has restricted use in Mobile impromptu systems in light of the absence of portability and assets. This has lead to the improvement of many steering conventions, for example, proactive, receptive and half breed. One intriguing examination zone in MANET is steering. Steering in the MANETs is a testing assignment and has gotten a colossal measure of consideration from examines. An uncommon consideration is paid on to feature the combination of MANET with the critical highlights of IPv6, for example, coordinated security, start to finish correspondence. This has prompted advancement of various directing conventions for MANETs, and every creator of each developed convention contends that the technique proposed gives an improvement over various distinctive systems considered in the writing for a given system situation. In this way, it is very hard to figure out which conventions may perform best under various diverse system situations, for example, expanding hub thickness and traffic. In this paper, we give the ongoing expository investigation on MANETs for IPV6 systems.

The massive growth of the global routing tables is one of the biggest problems that still face internet nowadays. This problem is mainly caused by the random distribution of IPv4 addresses. With the immigration to IPv6 and the large ranges of addresses provided by this protocol, it is crucial to wisely manage the assignment of IPv6 prefixes. In this paper, we propose a process to generate a logical topology of IPv6 networks. This topology uses perfectly the summarization technique and consists in representing the summary routes in hierarchical manner such that large range of addresses represents several smaller ranges. The proposed aggregation process optimizes and divides up the routing tables which may help resolve the problem of the explosive growth of internet routing tables. Furthermore, the logical topology can be easly customized to fit the features of the routers that are used in the network.

Accountability and privacy are considered valuable but conflicting properties in the Internet, which at present does not provide native support for either. Past efforts to balance accountability and privacy in the Internet have unsatisfactory deployability due to the introduction of new communication identifiers, and because of large-scale modifications to fully deployed infrastructures and protocols. The IPv6 is being deployed around the world and this trend will accelerate. In this paper, we propose a private and accountable proposal based on IPv6 called PAVI that seeks to bootstrap accountability and privacy to the IPv6 Internet without introducing new communication identifiers and large-scale modifications to the deployed base. A dedicated quantitative analysis shows that the proposed PAVI achieves satisfactory levels of accountability and privacy. The results of evaluation of a PAVI prototype show that it incurs little performance overhead, and is widely deployable.

The current global Internet USES the TCP/IP protocol cluster, the current version is IPv4. The IPv4 is with 32-bit addresses, the maximum number of computers connected to the Internet in the world is 232. With the development of Internet of things, big data and cloud storage and other technologies, the limited address space defined by IPv4 has been exhausted. To expand the address space, the IETF designed the next generation IPv6 to replace IPv4. IPv6 using a 128-bit address length that provides almost unlimited addresses. However, with the development and application of the Internet of things, big data and cloud storage, IPv6 has some shortcomings in its addressing structure design; security and network compatibility, These technologies are gradually applied in recent years, the continuous development of new technologies application show that the IPv6 address structure design ideas have some fatal defects. This paper proposed a route to upgrade the original IPv4 by studying on the structure of IPv6 "spliced address", and point out the defects in the design of IPv6 interface ID and the potential problems such as security holes.

Aiming at the incomplete and incomplete security mechanism of wireless access system in emergency communication network, this paper proposes a security mechanism requirement construction method for wireless access system based on security evaluation standard. This paper discusses the requirements of security mechanism construction in wireless access system from three aspects: the definition of security issues, the construction of security functional components and security assurance components. This method can comprehensively analyze the security threats and security requirements of wireless access system in emergency communication network, and can provide correct and reasonable guidance and reference for the establishment of security mechanism.

Future that IoT has to enhance the productivity on healthcare applications.

In AI Matters Volume 4, Issue 2, and Issue 4, we raised the notion of the possibility of an AI Cosmology in part in response to the "AI Hype Cycle" that we are currently experiencing. We posited that our current machine learning and big data era represents but one peak among several previous peaks in AI research in which each peak had accompanying "Hype Cycles". We associated each peak with an epoch in a possible AI Cosmology. We briefly explored the logic machines, cybernetics, and expert system epochs. One of the objectives of identifying these epochs was to help establish that we have been here before. In particular we've been in the territory where some application of AI research finds substantial commercial success which is then closely followed by AI fever and hype. The public's expectations are heightened only to end in disillusionment when the applications fall short. Whereas it is sometimes somewhat of a challenge even for AI researchers, educators, and practitioners to know where the reality ends and hype begins, the layperson is often in an impossible position and at the mercy of pop culture, marketing and advertising campaigns. We suggested that an AI Cosmology might help us identify a single standard model for AI that could be the foundation for a common shared understanding of what AI is and what it is not. A tool to help the layperson understand where AI has been, where it's going, and where it can't go. Something that could provide a basic road map to help the general public navigate the pitfalls of AI Hype.

For modern Automatic Test Equipment (ATE) one of the most daunting tasks is now Information Assurance (IA). What was once at most a secondary item consisting mainly of installing an Anti-Virus suite is now becoming one of the most important aspects of ATE. Given the current climate of IA it has become important to ensure ATE is kept safe from any breaches of security or loss of information. Even though most ATE are not on the Internet (or even on a local network for many) they are still vulnerable to some of the same attack vectors plaguing common computers and other electronic devices. This paper will discuss one method which can be used to ensure that modern ATE can continue to be used to test and detect faults in the systems they are designed to test. Most modern ATE include one or more Ethernet switches to allow communication to the many Instruments or devices contained within them. If the switches purchased are managed and support layer 2 or layer 3 of the Open Systems Interconnection (OSI) model they can also be used to help in the IA footprint of the station. Simple configurations such as limiting broadcast or multicast packets to the appropriate devices is the first step of limiting access to devices to what is needed. If the switch also includes some layer 3 like capabilities Virtual Local Area Networks can be created to further limit the communication pathways to only what is required to perform the required tasks. These and other simple switch configurations while not required can help limit the access of a virus or worm. This paper will discuss these and other configuration tools which can help prevent an ATE system from being compromised.

Insider threats refer to threats posed by individuals who intentionally or unintentionally destroy, exfiltrate, or leak sensitive information, or expose their organization to outside attacks. Surveys of organizations in government and industry consistently show that threats posed by insiders rival those posed by hackers, and that insider attacks are even more costly. Emerging U.S. government guidelines and policies for establishing insider threat programs tend to specify only minimum standards for insider threat monitoring, analysis, and mitigation programs. Arguably, one of the most serious challenges is to identify and integrate behavioral (sociotechnical) indicators of insider threat r isk in addition to cyber/technical indicators. That is, in focusing on data that are most readily obtained, insider threat programs most often miss the human side of the problem. This talk briefly describes research aiming to catalog human as well as technical factors associated with insider threat risk and summarizes several recent studies that seek to inform the development of more comprehensive, proactive approaches to insider threat assessment.

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artificial intelligence. These environments are successful when they effectively couple human decision making with advanced, guided spatial analytics in human-computer collaborative discourse and decision making (HCCD). Our HCCD approach builds upon visual analytics, natural scale templates, traceable information, human-guided analytics, and explainable and interactive machine learning, focusing on empowering the decisionmaker through interactive visual spatial analytic environments where non-digital human expertise and experience can be combined with state-of-the-art and transparent analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. I'll describe how we have applied these techniques to challenges in sustainability, security, resiliency, public safety, and disaster management.

Generally, methods of authentication and identification utilized in asserting users' credentials directly affect security of offered services. In a federated environment, service owners must trust external credentials and make access control decisions based on Assurance Information received from remote Identity Providers (IdPs). Communities (e.g. NIST, IETF and etc.) have tried to provide a coherent and justifiable architecture in order to evaluate Assurance Information and define Assurance Levels (AL). Expensive deployment, limited service owners' authority to define their own requirements and lack of compatibility between heterogeneous existing standards can be considered as some of the unsolved concerns that hinder developers to openly accept published works. By assessing the advantages and disadvantages of well-known models, a comprehensive, flexible and compatible solution is proposed to value and deploy assurance levels through a central entity called Proxy.

This paper presents for the first time a study on the security of information processed by video projectors. Examples of video recovery from the electromagnetic radiation of these equipment will be illustrated both in laboratory and real-field environment. It presents the results of the time parameters evaluation for the analyzed video signal that confirm the video standards specifications. There will also be illustrated the results of a vulnerability analysis based on the colors used to display the images but also the remote video recovery capabilities.

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

Network attacks continue to pose threats to missions in cyber space. To prevent critical missions from getting impacted or minimize the possibility of mission impact, active cyber defense is very important. Mission impact graph is a graphical model that enables mission impact assessment and shows how missions can be possibly impacted by cyber attacks. Although the mission impact graph provides valuable information, it is still very difficult for human analysts to comprehend due to its size and complexity. Especially when given limited resources, human analysts cannot easily decide which security measures to take first with respect to mission assurance. Therefore, this paper proposes to apply a ranking algorithm towards the mission impact graph so that the huge amount of information can be prioritized. The actionable conditions that can be managed by security admins are ranked with numeric values. The rank enables efficient utilization of limited resources and provides guidance for taking security countermeasures.

Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.

The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization's network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.

The task of the insider threat detection is one of the most sophisticated problems of the information security. The analysis of the logs of the access control system may reveal on how employees move and interact providing thus better understanding on how personnel observe security policies and established business processes. The paper presents an approach to the detection of the location-centric employees' interaction patterns. The authors propose the formal definition of the interaction patterns and present the visualization-driven technique to the extraction of the patterns from the data when any prior information about existing interaction routine and procedures is not available. The proposed approach is demonstrated on the data set provided within VAST MiniChallenge-2 2016 contest.

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.

This paper considers the use of novel technologies for mitigating attacks that aim at compromising intrusion detection systems (IDSs). Solutions based on collaborative intrusion detection networks (CIDNs) could increase the resilience against such attacks as they allow IDS nodes to gain knowledge from each other by sharing information. However, despite the vast research in this area, trust management issues still pose significant challenges and recent works investigate whether these could be addressed by relying on blockchain and related distributed ledger technologies. Towards that direction, the paper proposes the use of a trust-based blockchain in CIDNs, referred to as trust-chain, to protect the integrity of the information shared among the CIDN peers, enhance their accountability, and secure their collaboration by thwarting insider attacks. A consensus protocol is proposed for CIDNs, which is a combination of a proof-of-stake and proof-of-work protocols, to enable collaborative IDS nodes to maintain a reliable and tampered-resistant trust-chain.

In today's interconnected world, universities recognize the importance of protecting their information assets from internal and external threats. Being the possible insider threats to Information Security, employees are often coined as the weakest link. Both employees and organizations should be aware of this raising challenge. Understanding staff perception of compliance behaviour is critical for universities wanting to leverage their staff capabilities to mitigate Information Security risks. Therefore, this research seeks to get insights into staff perception based on factors adopted from several theories by using proposed constructs i.e. "perceived" practices/policies and "perceived" intention to comply. Drawing from the General Deterrence Theory, Protection Motivation Theory, Theory of Planned Behaviour and Information Reinforcement, within the context of Palestine universities, this paper integrates staff awareness of Information Security Policies (ISP) countermeasures as antecedents to ``perceived'' influencing factors (perceived sanctions, perceived rewards, perceived coping appraisal, and perceived information reinforcement). The empirical study is designed to follow a quantitative research approaches, use survey as a data collection method and questionnaires as the research instruments. Partial least squares structural equation modelling is used to inspect the reliability and validity of the measurement model and hypotheses testing for the structural model. The research covers ISP awareness among staff and seeks to assert that information security is the responsibility of all academic and administrative staff from all departments. Overall, our pilot study findings seem promising, and we found strong support for our theoretical model.

The constant changing of technologies have brought to critical infrastructure organisations numerous information security threats such as insider threat. Critical infrastructure organisations have difficulties to early detect and capture the possible vital signs of insider threats due sometimes to lack of effective methodologies or frameworks. It is from this viewpoint that, this paper proposes a symptomatic insider threat risk assessments framework known as Insider Threat Framework for Namibia Critical Infrastructure Organization (ITFNACIO), aimed to predict the probable signs of insider threat based on Symptomatic Analysis (SA), and develop a prototype as a proof of concept. A case study was successfully used to validate and implement the proposed framework; hence, qualitative methodology was employed throughout the whole research process where two (2) insider threats were captured. The proposed insider threat framework can be further developed in multiple cases and a more automated system able to trigger an early warning system of possible insider threat events.

Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.