College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Extension of SPIKE for Encrypted Protocol Fuzzing

Abstract: A fuzzer is a program that attempts to find security vulnerabilities in an application by sending random or semi-random input. Fuzzers have been widely used to find vulnerabilities in protocol implementations. The implementations may conform to the design of the protocol, but most of the times some glitches might remain. As a result vulnerabilities might remain unnoticed. Consequently, different implementations of the same protocol may be vulnerable to different kind of attacks. Fuzzers help us discover such implementation flaws. Among the currently available and popular ones, SPIKE is one recognized open-source fuzzing framework. However, SPIKE has a limitation of fuzzing only non-encrypted protocols. This paper presents the extension of SPIKE, called ESPIKE, for fuzzing of encrypted protocols. ESPIKE will facilitate testing implementations of SSL encrypted protocols. As a proof of concept for efficiency of ESPIKE we demonstrate its usage on sftp and https protocol.

Modified fuzzy hyperline-segment neural network for classification with mixed attribues

Abstract: The fuzzy hyperline segment neural network (FHLSNN) utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is a n-dimensional hyperline segment defined by two end points with a corresponding membership function. In FHLSNN, membership function calculates membership value of the input pattern based on its distance from both the end points of the hyperline segment. But sometimes input pattern is nearer to the hyperline segment but far from its endpoints. To solve this problem, this paper proposes modified fuzzy hyperline segment neural network (MFHLSNN). In MHLSNN membership function is based on minimum of the distance of the input pattern from the midpoint of the hyperline segment and its distance from both the end points. The proposed model is applied to eight different benchmark datasets taken from the UCI machine learning repository. The experimental results of the MFHLSNN are compared with earlier methods like fuzzy min-max neural network, generalized fuzzy min-max neural network and fuzzy hyperline segment neural network. These results show that the MFHLSNN gives improved performance as compared to its earlier methods.

Fragile video watermarking for tampering detection and localization

Abstract: Authentication is required to decide the originality of video signal. In this paper we proposed effective fragile video watermarking technique to embed and extract watermark in DCT domain with high capacity and transparency. Two watermarks are embedded into each frame. The first watermark is bits of the digital signature of hash value of the frame in frequency domain and second watermark is bits of micro-block numbers and frame numbers. The watermarks are embedded into video frames one by one in highest non-zero coefficient of quantized DCT coefficient. The first watermark is used to detect tampering and second watermark is used to localize the area being tampered. This technique causes significantly smaller video distortion as bits are embedded into the highest frequency coefficients. The embedded watermark is extracted and verified using public key. The block numbers and frame numbers are inserted in order to detect intra-frame and inter-frame tampering such as addition or removal of content within frames, frame reordering, dropping or addition of extra frames. If the video is being tampered we may extract one watermark correctly but other may get destroyed. As a result tamper detection of watermarked digital video will proved to be more authentic.

Prevention of Spoofing Attacks in Wireless Networks

Abstract: Prevention of spoofing attacks is a hard problem. We propose a method of preventing the MAC Address spoofing attack. Here we will use an intermediate or dummy node which lies between the server and the users. This dummy node serves 2 purposes-1). Ignore the data requests by attackers 2). Reduce the traffic on the server. The detection of attackers is done by considering physical spatial information in the form of RSS (Received Signal Strength). Dividing the different users/nodes into clusters by using K-Means algorithm. Further the number of attackers are provided by GADE (Generalized Attack Detection Model) and attacker is detected and localized by IDOL (Integrated Detection a Localization System).