Honeyfarm is a model to deploy honeypots for global network attack monitoring, correlation and forensic analysis. Data control is a fundamental problem in the honeyfarm to protect the Internet from being attacked by compromised honeypots in the honeyfarm, while providing a controlled environment for worm behaviour study. However, this problem is not well addressed in a limited number of existing implementations. This paper presents a honeyfarm system and focuses on the design of a data control mechanism based on Intrusion detection and Data redirection (DOID). Comprehensive experiments including attack event tracing, worm behaviour study and forensic analysis display that DOID is a good tool for attack monitoring and forensic analysis.

Focus on the difficulty of large-scale network traffic monitoring and analysis, this paper proposed the concepts of Group Behavior Flow model to aggregate traffic packets and perform abnormal behavior detection. Based on the flow model the pivotal traffic metrics can be extracted while the number of flow records are reduced significantly. Secondly, we employ the graph model to capture the traffic feature distribution between different group users. And optical flow analysis methods are proposed to extract the dynamic behavior changing features between different groups and achieve the goal of abnormal behavior detection. The experimental results based on actual traffic traces show that the methods proposed in this paper can capture the traffic features effectually in the current 10 Gbps network environment, and achieve the goal of abnormal behavior detection and abnormal source location, which is very important for traffic management.

Several classical source number estimation methods have been proposed in the past based on information theoretic criteria such as minimum description length (MDL). However, in most known real applications there is a scenario in which the number of sensors goes to infinity at the same speed as the number of snapshots (general asymptotic case) which yields to a blind performance for the classical MDL and results in an inaccurate source number estimation. Accordingly, in this work, the Galaxy Based Search Algorithm (GBSA) is modified and applied with the MDL criteria in order to optimize and correct the detection of source number under such sample-starving case. Simulation results show that the proposed GBSA-MDL based method gives reliable results compared to several used source number estimation methods.

In future Internet of things (IoT) network, some of the prime objectives or demands that need to be addressed are massive data, increased devices, decreased delay and reduced energy cost. To meet these demands, drastic improvements need to be made. This paper integrates 5G network with IoT scenario and presents a massive IoT device access scheme. In our small cell-IoT network, IoT devices share the resource block (RB) with small cell devices in an overlay way. Under this context, we formulate the access problem with the objectives of minimizing network overall energy cost and maximizing the number of accessed IoT devices. By utilizing data mining tool, the massive data generated by the small cell network and IoT devices is highly utilized and IoT devices can access to the available RBs with higher intelligence. In addition, under the support of K-means algorithm, IoT devices are classified into different clusters. We further propose a cluster access method, with which, each cluster is allocated appropriate RB. All the devices within the same cluster share the same RB in a sequence while considering RB’s vacant time. Simulation results show that our solution leads to a satisfactory outcome.

In the routing protocol of wireless sensor networks, the traditional LEACH algorithm is too random, and the cluster head selection of it is not ideal. To solve this problem, it proposes a WSN routing algorithm based on energy approximation strategy. It chooses a series of nodes with high energy and high density to form cluster candidate clusters, and then selects the farthest node as the cluster head from the candidates by using the energy approximation strategy. The algorithm is simple and easy to implement, and the cluster head selection of it is ideal. Using Matlab software for simulation, the results show that it is less energy consuming than the LEACH algorithm, and the lifetime of the whole network is prolonged.

The features of actively detection of intrusion detection systems (IDSs) are crucial in cyberspace security evaluation. Most of existing evaluation models are insufficient for selecting proper IDS in varying situations since these methods only base on detection rate and false alarm ratio. The paper proposes an environment-related information security evaluation model for IDSs, and applies the model in a practical IDS evaluation process. Compared to existing ones, the proposed model considers two more factors: background traffic and workload, and thus can achieve a more objective and comprehensive evaluation result for IDSs.

Autonomous mobile robots become increasingly more popular in many fields to execute tasks instead of human beings. When multiple autonomous mobile robots coexist in an area and move autonomously, how to avoid collision among them is a critical issue. In this paper, a distributed negotiation-based collision avoidance scheme is proposed. With this scheme, mobile robots negotiate with each other when they are about to collide. Based on the negotiation, the robots make the most appropriate decision to avoid collision, and move forward to their own destinations with the least cost. The effectiveness and the efficiency of the proposed scheme are proved by extensive simulations.

Due to its high spectrum efficiency, strong ability to resist multipath fading, OFDM networks is widely applied in various wireless communication systems. However, physical layer security is also an important problem in OFDM networks. By using relay nodes, the secrecy outage probability can be reduced since relay nodes could increase freedoms of optimization for the cooperative OFDM networks. By optimizing multiple variables, which includes adaptive hybrid relaying scheme, relay selection, and resource allocation, we propose the secure algorithm which aims at minimizing the secrecy outage probability of multiuser in cooperative OFDM networks. To achieve this goal, we establish an optimization problem including multiple optimization variables. Then, we convert the optimization problem into a graph theory problem. Simulation results show that the secure algorithm can significantly reduce the secrecy outage probability of multiuser in cooperative OFDM networks.

The development and maturation of cloud computing provides a new idea for deploying RFID systems. A Cloud-based RFID system becomes a new promising architecture. It can be offered as a service of cloud computing to individuals and organizations. However, the cloud-based RFID systems are confronted with more special security and privacy threats, especially the untrustworthy cloud provider and insecure backward communications. Unfortunately, most current RFID authentication schemes fail to meet the special security and privacy requirements of cloud-based RFID, i.e. to provide anonymity and confidentiality against the cloud and build secure backend channels. In this paper, we propose a secure distance bounding protocol for a RFID system, which is cloud-based RFID mutual authentication protocol compatible with the mature EPC-C1 G2 standards. It can effectively resist various threats in cloud environment comparing with other cloud-based RFID authentication protocol and reduce the success probability of a Mafia attack and make it lower than the optimal situation in academic circles.

With the increase of design scale and complexity, security vulnerabilities residing in hardware designs become hard to detect. Existing functional testing and verification methods cannot guarantee test and verification coverage in design phase. Fortunately, gate level information flow tracking (GLIFT) has been proposed to enforce bit-tight information flow security from the gate level to detect security vulnerabilities and prevent information leakage effectively. However, there is a significant limitation that the inherent high complexity of GLIFT logic causes significant overheads in static verification and physical implementation. In order to address the limitation, we propose a simplified GLIFT method that incorporates more detailed optimization logic routes to reduce its complexity and allow don’t care to simplify original GLIFT logic. Experimental results have demonstrated that the simplified GLIFT method can reduce the design overhand in several gates by sacrificing a fraction of GLIFT precision.