Learning agent-based security schema mitigating man-in-the-middle attacks in fog computing

The fast emerging of internet of things (IoTs) has introduced fog computing as an intermediate layer between end-users and the cloud datacenters. Fog computing layer characterized by its closeness to end users for service provisioning than the cloud. However, security challenges are still a big concern in fog and cloud computing paradigms as well. In fog computing, one of the most destructive attacks is man-in-the-middle (MitM). Moreover, MitM attacks are hard to be detected since they performed passively on the network level. This paper proposes a MitM mitigation scheme in fog computing architecture. The proposal mapped the fog layer on software-defined network (SDN) architecture. The proposal integrated multi-path transmission control protocol (MPTCP), moving target defense (MTD) technique, and reinforcement learning agent (RL) in one framework that contributed significantly to improving the fog layer resources utilization and security. The proposed schema hardens the network reconnaissance and discovery, thus improved the network security against MitM attack. The evaluation framework was tested using a simulation environment on mininet, with the utilization of MPTCP kernel and Ryu SDN controller. The experimental results shows that the proposed schema maintained the network resiliency, improves resource utilization without adding significant overheads compared to the traditional transmission control protocol (TCP)

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

Securing the software-defined networking control plane by using control and data dependency techniques

Software-defined networking (SDN) fundamentally changes how network and security practitioners design, implement, and manage their networks. SDN decouples the decision-making about traffic forwarding (i.e., the control plane) from the traffic being forwarded (i.e., the data plane). SDN also allows for network applications, or apps, to programmatically control network forwarding behavior and policy through a logically centralized control plane orchestrated by a set of SDN controllers. As a result of logical centralization, SDN controllers act as network operating systems in the coordination of shared data plane resources and comprehensive security policy implementation. SDN can support network security through the provision of security services and the assurances of policy enforcement. However, SDN’s programmability means that a network’s security considerations are different from those of traditional networks. For instance, an adversary who manipulates the programmable control plane can leverage significant control over the data plane’s behavior. In this dissertation, we demonstrate that the security posture of SDN can be enhanced using control and data dependency techniques that track information flow and enable understanding of application composability, control and data plane decoupling, and control plane insight. We support that statement through investigation of the various ways in which an attacker can use control flow and data flow dependencies to influence the SDN control plane under different threat models. We systematically explore and evaluate the SDN security posture through a combination of runtime, pre-runtime, and post-runtime contributions in both attack development and defense designs. We begin with the development a conceptual accountability framework for SDN. We analyze the extent to which various entities within SDN are accountable to each other, what they are accountable for, mechanisms for assurance about accountability, standards by which accountability is judged, and the consequences of breaching accountability. We discover significant research gaps in SDN’s accountability that impact SDN’s security posture. In particular, the results of applying the accountability framework showed that more control plane attribution is necessary at different layers of abstraction, and that insight motivated the remaining work in this dissertation. Next, we explore the influence of apps in the SDN control plane’s secure operation. We find that existing access control protections that limit what apps can do, such as role-based access controls, prove to be insufficient for preventing malicious apps from damaging control plane operations. The reason is SDN’s reliance on shared network state. We analyze SDN’s shared state model to discover that benign apps can be tricked into acting as “confused deputies”; malicious apps can poison the state used by benign apps, and that leads the benign apps to make decisions that negatively affect the network. That violates an implicit (but unenforced) integrity policy that governs the network’s security. Because of the strong interdependencies among apps that result from SDN’s shared state model, we show that apps can be easily co-opted as “gadgets,” and that allows an attacker who minimally controls one app to make changes to the network state beyond his or her originally granted permissions. We use a data provenance approach to track the lineage of the network state objects by assigning attribution to the set of processes and agents responsible for each control plane object. We design the ProvSDN tool to track API requests from apps as they access the shared network state’s objects, and to check requests against a predefined integrity policy to ensure that low-integrity apps cannot poison high-integrity apps. ProvSDN acts as both a reference monitor and an information flow control enforcement mechanism. Motivated by the strong inter-app dependencies, we investigate whether implicit data plane dependencies affect the control plane’s secure operation too. We find that data plane hosts typically have an outsized effect on the generation of the network state in reactive-based control plane designs. We also find that SDN’s event-based design, and the apps that subscribe to events, can induce dependencies that originate in the data plane and that eventually change forwarding behaviors. That combination gives attackers that are residing on data plane hosts significant opportunities to influence control plane decisions without having to compromise the SDN controller or apps. We design the EventScope tool to automatically identify where such vulnerabilities occur. EventScope clusters apps’ event usage to decide in which cases unhandled events should be handled, statically analyzes controller and app code to understand how events affect control plane execution, and identifies valid control flow paths in which a data plane attacker can reach vulnerable code to cause unintended data plane changes. We use EventScope to discover 14 new vulnerabilities, and we develop exploits that show how such vulnerabilities could allow an attacker to bypass an intended network (i.e., data plane) access control policy. This research direction is critical for SDN security evaluation because such vulnerabilities could be induced by host-based malware campaigns. Finally, although there are classes of vulnerabilities that can be removed prior to deployment, it is inevitable that other classes of attacks will occur that cannot be accounted for ahead of time. In those cases, a network or security practitioner would need to have the right amount of after-the-fact insight to diagnose the root causes of such attacks without being inundated with too much informa- tion. Challenges remain in 1) the modeling of apps and objects, which can lead to overestimation or underestimation of causal dependencies; and 2) the omission of a data plane model that causally links control and data plane activities. We design the PicoSDN tool to mitigate causal dependency modeling challenges, to account for a data plane model through the use of the data plane topology to link activities in the provenance graph, and to account for network semantics to appropriately query and summarize the control plane’s history. We show how prior work can hinder investigations and analysis in SDN-based attacks and demonstrate how PicoSDN can track SDN control plane attacks.Ope

Cross-VM network attacks & their countermeasures within cloud computing environments

Cloud computing is a contemporary model in which the computing resources are dynamically scaled-up and scaled-down to customers, hosted within large-scale multi-tenant systems. These resources are delivered as improved, cost-effective and available upon request to customers. As one of the main trends of IT industry in modern ages, cloud computing has extended momentum and started to transform the mode enterprises build and offer IT solutions. The primary motivation in using cloud computing model is cost-effectiveness. These motivations can compel Information and Communication Technologies (ICT) organizations to shift their sensitive data and critical infrastructure on cloud environments. Because of the complex nature of underlying cloud infrastructure, the cloud environments are facing a large number of challenges of misconfigurations, cyber-attacks, root-kits, malware instances etc which manifest themselves as a serious threat to cloud environments. These threats noticeably decline the general trustworthiness, reliability and accessibility of the cloud. Security is the primary concern of a cloud service model. However, a number of significant challenges revealed that cloud environments are not as much secure as one would expect. There is also a limited understanding regarding the offering of secure services in a cloud model that can counter such challenges. This indicates the significance of the fact that what establishes the threat in cloud model. One of the main threats in a cloud model is of cost-effectiveness, normally cloud providers reduce cost by sharing infrastructure between multiple un-trusted VMs. This sharing has also led to several problems including co-location attacks. Cloud providers mitigate co-location attacks by introducing the concept of isolation. Due to this, a guest VM cannot interfere with its host machine, and with other guest VMs running on the same system. Such isolation is one of the prime foundations of cloud security for major public providers. However, such logical boundaries are not impenetrable. A myriad of previous studies have demonstrated how co-resident VMs could be vulnerable to attacks through shared file systems, cache side-channels, or through compromising of hypervisor layer using rootkits. Thus, the threat of cross-VM attacks is still possible because an attacker uses one VM to control or access other VMs on the same hypervisor. Hence, multiple methods are devised for strategic VM placement in order to exploit co-residency. Despite the clear potential for co-location attacks for abusing shared memory and disk, fine grained cross-VM network-channel attacks have not yet been demonstrated. Current network based attacks exploit existing vulnerabilities in networking technologies, such as ARP spoofing and DNS poisoning, which are difficult to use for VM-targeted attacks. The most commonly discussed network-based challenges focus on the fact that cloud providers place more layers of isolation between co-resided VMs than in non-virtualized settings because the attacker and victim are often assigned to separate segmentation of virtual networks. However, it has been demonstrated that this is not necessarily sufficient to prevent manipulation of a victim VM’s traffic. This thesis presents a comprehensive method and empirical analysis on the advancement of co-location attacks in which a malicious VM can negatively affect the security and privacy of other co-located VMs as it breaches the security perimeter of the cloud model. In such a scenario, it is imperative for a cloud provider to be able to appropriately secure access to the data such that it reaches to the appropriate destination. The primary contribution of the work presented in this thesis is to introduce two innovative attack models in leading cloud models, impersonation and privilege escalation, that successfully breach the security perimeter of cloud models and also propose countermeasures that block such types of attacks. The attack model revealed in this thesis, is a combination of impersonation and mirroring. This experimental setting can exploit the network channel of cloud model and successfully redirects the network traffic of other co-located VMs. The main contribution of this attack model is to find a gap in the contemporary network cloud architecture that an attacker can exploit. Prior research has also exploited the network channel using ARP poisoning, spoofing but all such attack schemes have been countered as modern cloud providers place more layers of security features than in preceding settings. Impersonation relies on the already existing regular network devices in order to mislead the security perimeter of the cloud model. The other contribution presented of this thesis is ‘privilege escalation’ attack in which a non-root user can escalate a privilege level by using RoP technique on the network channel and control the management domain through which attacker can manage to control the other co-located VMs which they are not authorized to do so. Finally, a countermeasure solution has been proposed by directly modifying the open source code of cloud model that can inhibit all such attacks

Mitigating Stealthy Link Flooding DDoS Attacks Using SDN-Based Moving Target Defense

With the increasing diversity and complication of Distributed Denial-of-Service (DDoS) attacks, it has become extremely challenging to design a fully protected network. For instance, recently, a new type of attack called Stealthy Link Flooding Attack (SLFA) has been shown to cause critical network disconnection problems, where the attacker targets the communication links in the surrounding area of a server. The existing defense mechanisms for this type of attack are based on the detection of some unusual traffic patterns; however, this might be too late as some severe damage might already be done. These mechanisms also do not consider countermeasures during the reconnaissance phase of these attacks. Over the last few years, moving target defense (MTD) has received increasing attention from the research community. The idea is based on frequently changing the network configurations to make it much more difficult for the attackers to attack the network. In this dissertation, we investigate several novel frameworks based on MTD to defend against contemporary DDoS attacks. Specifically, we first introduce MTD against the data phase of SLFA, where the bots are sending data packets to target links. In this framework, we mitigate the traffic if the bandwidth of communication links exceeds the given threshold, and experimentally show that our method significantly alleviates the congestion. As a second work, we propose a framework that considers the reconnaissance phase of SLFA, where the attacker strives to discover critical communication links. We create virtual networks to deceive the attacker and provide forensic features. In our third work, we consider the legitimate network reconnaissance requests while keeping the attacker confused. To this end, we integrate cloud technologies as overlay networks to our system. We demonstrate that the developed mechanism preserves the security of the network information with negligible delays. Finally, we address the problem of identifying and potentially engaging with the attacker. We model the interaction between attackers and defenders into a game and derive a defense mechanism based on the equilibria of the game. We show that game-based mechanisms could provide similar protection against SLFAs like the extensive periodic MTD solution with significantly reduced overhead. The frameworks in this dissertation were verified with extensive experiments as well as with the theoretical analysis. The research in this dissertation has yielded several novel defense mechanisms that provide comprehensive protection against SLFA. Besides, we have shown that they can be integrated conveniently and efficiently to the current network infrastructure

Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio