Name: Chen Kai

Professor in School of Computer Science and Technology

Phone: 86-27-87541689


Academic Areas:

Research Interest

Network attack detection network application


Doctor--Systems Engineering--Huazhong University of Science & Technology,Wuhan, Hubei, P.R. China--2008

Master--Systems Engineering--Huazhong University of Science & Technology,Wuhan, Hubei, P.R. China--2004

Bachelor--Computer Application--Wuhan University of Hydraulic and Electric Engineering,Wuhan, Hubei, P.R. China--1995

Oversea Study and Visit

Academic Memberships

Honours and Awards

Honours and Awards

[1] Yindong Shen; Kunkun Peng; Kai Chen; Jingpeng Li,Evolutionary crew scheduling with adaptive chromosomes ,Transportation Research Part B: Methodological,2013,This paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future

[2] Kai Chen,A Real-time Detection Method of LDoS Based on Shewhart Control Chart Detection Theory ,the 2012 International Conference on Computer Application and System Modeling,2012,The low-rate denial of service (LDoS) attack is a new threat to Internet security. Due to its low rate and high concealment characteristics, LDoS attack is difficult to be detected through the analysis of attack flow directly. Most present methods primarily analysis network traffic or feature of LDoS flows to determine LDoS, but they cannot get the satisfactory outcome. From the phenomenon that TCP flow exhibits special different characteristics under LDoS attack and with the superiority of Shewhart Control Chart in outlier detection, this paper proposes a real-time LDoS attack detection method based on Shewhart Control Chart theory, and devises detection criterions based on abundant experiments. This detection method can detect LDoS attack accurately and effectively.

[3] Kai Chen,EBDT:A Method for Detecting LDoS Attack ,the IEEE International Conference on Information and Automation Shenyang,2012,The Low-rate Denial of Service (LDoS), as a new type of DoS, is more difficult to be detected due to its concealment and variety. However, whenever a kind of LDoS attack occurs, the TCP traffic becomes unusual: its distribution and decreased degree are significantly different than those without any LDoS attacks. Based on these characteristics, a method for detecting LDoS attacks called EBDT is proposed, which detects LDoS attacks by analyzing the variation of TCP traffic. Simulations show that EBDT can detect LDoS attacks effectively and the testing results with the real network traffic show that EBDT has a low false-positive rate.


[5] Kai Chen,Detecting LDoS Attacks based on Abnormal Network Traffic ,KSII Transactions on Internet and Information Systems,2012,By sending periodically short bursts of traffic to reduce legit transmission control protocol (TCP) traffic, the low-rate denial of service (LDoS) attacks are hard to be detected and may endanger covertly a network for a long period. Traditionally, LDoS detecting methods mainly concentrate on the attack stream with feature matching, and only a limited number of attack patterns can be detected off-line with high cost. Recent researches divert focus from the attack stream to the traffic anomalies induced by LDoS attacks, which can detect more kinds of attacks with higher efficiency. However, the limited number of abnormal characteristics and the inadequacy of judgment rules may cause wrong decision in some particular situations. In this paper, we address the problem of detecting LDoS attacks and present a scheme based on the fluctuant features of legit TCP and acknowledgment (ACK) traffic. In the scheme, we define judgment criteria which used to identify LDoS attacks in real time at an optimal detection cost. We evaluate the performance of our strategy in real-world network topologies. Simulations results clearly demonstrate the superiority of the method proposed in detecting LDoS attacks.

[6] Huiyu Liu, Kai Chen, Xiaosu Chen,Research on SynFlood Attack Target Locating Method ,the 2012 International Conference on Computer Application and System Modeling,2012,This paper proposes a new SynFlood attack target location method based on the Abnormal TCP Connection Graph (ATCG). The method build an Abnormal TCP Connection Graph based on the status of TCP connections. Then the method calculates the Abnormal Source Number (ASN) and Attack Intensity (AI). If such two values exceed the threshold defined in advance, the node which the IP address indicated can be determined as attack target. The simulation results indicate that the method has favorable accuracy and higher data packet processing capability. It can be deployed at the backbone router in a large or medium-sized network.