Biblio

To build the fifth generation (5G) mobile network, the sharing structure in the 5G network adopted in industries has gained great research interesting. However, in this structure data are shared among diversity networks, which introduces the threaten of network security, such as covert timing channels. To eliminate the covert timing channel, we propose to inject noise into the covert timing channel. By analyzing the modulation method of covert timing channels, we design the jamming strategy on the covert channel. According to the strategy, the interference algorithm of the covert timing channel is designed. Since the interference algorithm depends heavily on the memory, we construct a distributing jammer. Experiments results show that these covert time channel can be blocked under the distributing jammer.

Language-based information flow control (IFC) aims to provide guarantees about information propagation in computer systems having multiple security levels. Existing IFC systems extend the lattice model of Denning's, enforcing transitive security policies by tracking information flows along with a partially ordered set of security levels. They yield a transitive noninterference property of either confidentiality or integrity. In this paper, we explore IFC for security policies that are not necessarily transitive. Such nontransitive security policies avoid unwanted or unexpected information flows implied by transitive policies and naturally accommodate high-level coarse-grained security requirements in modern component-based software. We present a novel security type system for enforcing nontransitive security policies. Unlike traditional security type systems that verify information propagation by subtyping security levels of a transitive policy, our type system relaxes strong transitivity by inferring information flow history through security levels and ensuring that they respect the nontransitive policy in effect. Such a type system yields a new nontransitive noninterference property that offers more flexible information flow relations induced by security policies that do not have to be transitive, therefore generalizing the conventional transitive noninterference. This enables us to directly reason about the extent of information flows in the program and restrict interactions between security-sensitive and untrusted components.

Moving Target Defense (MTD) is a research hotspot in the field of network security. Moving Target Network Defense (MTND) is the implementation of MTD at network level. Numerous related works have been proposed in the field of MTND. In this paper, we focus on the scope and area of MTND, systematically present the recent representative progress from four aspects, including IP address and port mutation, route mutation, fingerprint mutation and multiple mutation, and put forward the future development directions. Several new perspectives and elucidations on MTND are rendered.

This paper considers a pilot spoofing attack scenario in a massive MIMO system. A malicious user tries to disturb the channel estimation process by sending interference symbols to the base-station (BS) via the uplink. Another legitimate user counters by sending random symbols. The BS does not possess any partial channel state information (CSI) and distribution of symbols sent by malicious user a priori. For such scenario, this paper aims to separate the channel directions from the legitimate and malicious users to the BS, respectively. A blind channel separation algorithm based on estimating the characteristic function of the distribution of the signal space vector is proposed. Simulation results show that the proposed algorithm provides good channel separation performance in a typical massive MIMO system.

The extensive use of information and communication technologies in power grid systems make them vulnerable to cyber-attacks. One class of cyber-attack is advanced persistent threats where highly skilled attackers can steal user authentication information's and then move laterally in the network, from host to host in a hidden manner, until they reach an attractive target. Once the presence of the attacker has been detected in the network, appropriate actions should be taken quickly to prevent the attacker going deeper. This paper presents a game theoretic approach to optimize the defense against an invader attempting to use a set of known vulnerabilities to reach critical nodes in the network. First, the network is modeled as a vulnerability multi-graph where the nodes represent physical hosts and edges the vulnerabilities that the attacker can exploit to move laterally from one host to another. Secondly, a two-player zero-sum Markov game is built where the states of the game represent the nodes of the vulnerability multi-graph graph and transitions correspond to the edge vulnerabilities that the attacker can exploit. The solution of the game gives the optimal strategy to disconnect vulnerable services and thus slow down the attack.

In order to ensure the security of electric power supervisory control and data acquisition (SCADA) system, this paper proposes a dynamic awareness security protection model based on security policy, the design idea of which regards safety construction protection as a dynamic analysis process and the security policy should adapt to the network dynamics. According to the current situation of the power SCADA system, the related security technology and the investigation results of system security threat, the paper analyzes the security requirements and puts forward the construction ideas of security protection based on policy protection detection response (P2DR) policy model. The dynamic awareness security protection model proposed in this paper is an effective and useful tool for protecting the security of power-SCADA system.

Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.