Biblio

In cloud computing environment, there are malignant nodes which create a huge problem to transfer data in communication. As there are so many models to prevent the data over the network, here we try to prevent or make secure to the network by avoiding mallicious nodes in between the communication. So the probabiliostic approach what we use here is a coherent tool to supervise the security challenges in the cloud environment. The matter of security for cloud computing is a superficial quality of service from cloud service providers. Even, cloud computing dealing everyday with new challenges, which is in process to well investigate. This research work draws the light on aspect regarding with the cloud data transmission and security by identifying the malignanat nodes in between the communication. Cloud computing network shared the common pool of resources like hardware, framework, platforms and security mechanisms. therefore Cloud Computing cache the information and deliver the secure transaction of data, so privacy and security has become the bone of contention which hampers the process to execute safely. To ensure the security of data in cloud environment, we proposed a method by implementing white box cryptography on RSA algorithm and then we work on the network, and find the malignant nodes which hampering the communication by hitting each other in the network. Several existing security models already have been deployed with security attacks. A probabilistic authentication and authorization approach is introduced to overcome this attack easily. It observes corrupted nodes before hitting with maximum probability. here we use a command table to conquer the malignant nodes. then we do the comparative study and it shows the probabilistic authentication and authorization protocol gives the performance much better than the old ones.

We are witnessing a huge growth of cyber-physical systems, which are autonomous, mobile, endowed with sensing, controlled by software, and often wirelessly connected and Internet-enabled. They include factory automation systems, robotic assistants, self-driving cars, and wearable and implantable devices. Since they are increasingly often used in safety- or business-critical contexts, to mention invasive treatment or biometric authentication, there is an urgent need for modelling and verification technologies to support the design process, and hence improve the reliability and reduce production costs. This paper gives an overview of quantitative verification and synthesis techniques developed for cyber-physical systems, summarising recent achievements and future challenges in this important field.

Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.

Many standard optimization methods for segmentation and reconstruction compute ML model estimates for appearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu-lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to continuous or discrete energy formulations in segmenta- tion, stereo, and other reconstruction problems.

The rate at which cyber-attacks are increasing globally portrays a terrifying picture upfront. The main dynamics of such attacks could be studied in terms of the actions of attackers and defenders in a cyber-security game. However currently little research has taken place to study such interactions. In this paper we use behavioral game theory and try to investigate the role of certain actions taken by attackers and defenders in a simulated cyber-attack scenario of defacing a website. We choose a Reinforcement Learning (RL) model to represent a simulated attacker and a defender in a 2×4 cyber-security game where each of the 2 players could take up to 4 actions. A pair of model participants were computationally simulated across 1000 simulations where each pair played at most 30 rounds in the game. The goal of the attacker was to deface the website and the goal of the defender was to prevent the attacker from doing so. Our results show that the actions taken by both the attackers and defenders are a function of attention paid by these roles to their recently obtained outcomes. It was observed that if attacker pays more attention to recent outcomes then he is more likely to perform attack actions. We discuss the implication of our results on the evolution of dynamics between attackers and defenders in cyber-security games.

The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included.

Routers in the Content-Centric Networking (CCN) architecture maintain state for all pending content requests, so as to be able to later return the corresponding content. By employing stateful forwarding, CCN supports native multicast, enhances security and enables adaptive forwarding, at the cost of excessive forwarding state that raises scalability concerns. We propose a semi-stateless forwarding scheme in which, instead of tracking each request at every on-path router, requests are tracked at every d hops. At intermediate hops, requests gather reverse path information, which is later used to deliver responses between routers using Bloom filter-based stateless forwarding. Our approach effectively reduces forwarding state, while preserving the advantages of CCN forwarding. Evaluation results over realistic ISP topologies show that our approach reduces forwarding state by 54%-70% in unicast delivery, without any bandwidth penalties, while in multicast delivery it reduces forwarding state by 34%-55% at the expense of 6%-13% in bandwidth overhead.
 

IP spoofing based DDoS attack that relies on multiple compromised hosts in the network to attack the victim. In IP spoofing, IP addresses can be forged easily, thus, makes it difficult to filter illegitimate packets from legitimate one out of aggregated traffic. A number of mitigation techniques have been proposed in the literature by various researchers. The conventional Hop Count Filtering or probabilistic Hop Count Filtering based research work indicates the problems related to higher computational time and low detection rate of illegitimate packets. In this paper, DPHCF-RTT technique has been implemented and analysed for variable number of hops. Goal is to improve the limitations of Conventional HCF or Probabilistic HCF techniques by maximizing the detection rate of illegitimate packets and reducing the computation time. It is based on distributed probabilistic HCF using RTT. It has been used in an intermediate system. It has the advantage for resolving the problems of network bandwidth jam and host resources exhaustion. MATLAB 7 has been used for simulations. Mitigation of DDoS attacks have been done through DPHCF-RTT technique. It has been shown a maximum detection rate up to 99% of malicious packets.

Dynamic firewalls with stateful inspection have added a lot of security features over the stateless traditional static filters. Dynamic firewalls need to be adaptive. In this paper, we have designed a framework for dynamic firewalls based on probabilistic ontology using Multi Entity Bayesian Networks (MEBN) logic. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated substructures and can express a probability distribution over models of any consistent first order theory. The motivation of our proposed work is about preventing novel attacks (i.e. those attacks for which no signatures have been generated yet). The proposed framework is in two important parts: first part is the data flow architecture which extracts important connection based features with the prime goal of an explicit rule inclusion into the rule base of the firewall; second part is the knowledge flow architecture which uses semantic threat graph as well as reasoning under uncertainty to fulfill the required objective of providing futuristic threat prevention technique in dynamic firewalls.

To allow fine-grained access control of sensitive data, researchers have proposed various types of functional encryption schemes, such as identity-based encryption, searchable encryption and attribute-based encryption. We observe that it is difficult to define some complex access policies in certain application scenarios by using these schemes individually. In this paper, we attempt to address this problem by proposing a functional encryption approach named Key-Policy Attribute-Based Encryption with Attribute Extension (KP-ABE-AE). In this approach, we utilize extended attributes to integrate various encryption schemes that support different access policies under a common top-level KP-ABE scheme, thus expanding the scope of access policies that can be defined. Theoretical analysis and experimental studies are conducted to demonstrate the applicability of the proposed KP-ABE-AE. We also present an optimization for a special application of KP-ABE-AE where IPE schemes are integrated with a KP-ABE scheme. The optimization results in an integrated scheme with better efficiency when compared to the existing encryption schemes that support the same scope of access policies.

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.

In Wireless Mesh Networks (WMNs), Network-Wide Broadcasts (NWBs) are a fundamental operation, required by routing and other mechanisms that distribute information to all nodes in the network. However, due to the characteristics of wireless communication, NWBs are generally problematic. Optimizing them thus is a prime target when improving the overall performance and dependability of WMNs. Most existing optimizations neglect the real nature of WMNs and are based on simple graph models, which provide optimistic assumptions of NWB dissemination. On the other hand, models that fully consider the complex propagation characteristics of NWBs quickly become unsolvable due to their complexity. In this paper, we present the Monte Carlo method Probabilistic Breadth-First Search (PBFS) to approximate the reachability of NWB protocols. PBFS simulates individual NWBs on graphs with probabilistic edge weights, which reflect link qualities of individual wireless links in the WMN, and estimates reachability over a configurable number of simulated runs. This approach is not only more efficient than existing ones, but further provides additional information, such as the distribution of path lengths. Furthermore, it is easily extensible to NWB schemes other than flooding. The applicability of PBFS is validated both theoretically and empirically, in the latter by comparing reachability as calculated by PBFS and measured in a real-world WMN. Validation shows that PBFS quickly converges to the theoretically correct value and approximates the behavior of real-life testbeds very well. The feasibility of PBFS to support research on NWB optimizations or higher level protocols that employ NWBs is demonstrated in two use cases.

Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.