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Reverse shell has now emerged as an effective tool used to penetrate systems and networks with speed and compromise them. The efficiency and robustness of reverse shell lie in its use of the victim’s machine to generate the connection request to the attacker. Unfortunately, this connection request goes unchecked by the host’s security systems as it originates from the host. To address this challenge, we present a typical reverse shell, its idea, its implementation including the model as well as how it works, how it infects the systems, and lastly, techniques with which we can prevent the reverse shell from infecting our systems and networks. It is coded in Python and implemented over Windows operating system, which gives this Shell an advantage because Python has various libraries which can control a significant number of aspects of any operating system. Exploiting this feature of the language, we have succeeded in building a reverse shell which gives the attacker complete control of the host the Shell has infected as well as the added functionality of uploading, downloading, and persistent backdoor creation. Furthermore, we present a technique which increases the robustness and functionality of our Shell and distinguishes it from other such similar programs.

Video Surveillance systems are used to monitor, observe and intercept the changes in activities, features and behavior of objects, people or places. A multimodal surveillance system incorporates a network of video cameras, acoustic sensors, pressure sensors, IR sensors and thermal sensors to capture the features of the entity under surveillance, and send the recorded data to a base station for further processing. Multimodal surveillance systems are utilized to capture the required features and use them for pattern recognition, object identification, traffic management, object tracking, and so on. The proposal is to develop an efficient camera placement algorithm for deciding placement of multiple video cameras at junctions and intersections in a multimodal surveillance system which will be capable of providing maximum coverage of the area under surveillance, which will leads to complete elimination or reduction of blind zones in a surveillance area, maximizing the view of subjects, and minimizing occlusions in high vehicular traffic areas. Furthermore, the proposal is to develop a video summarization algorithm which can be used to create summaries of the videos captured in a multi-view surveillance system. Such a video summarization algorithm can be used further for object detection, motion tracking, traffic segmentation, etc. in a multi-view surveillance system.

Collaborative filtering (CF) is typically used for recommending those items to a user which other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and item-based collaborative filtering (IbCF) are two types of CF with a common objective of estimating target user’s rating for the target item. This paper explores different ways of combining predictions from UbCF and IbCF with an aim of minimizing overall prediction error. In this paper, we propose an approach for combining predictions from UbCF and IbCF through multiple linear regression (MLR) and support vector regression (SVR). Results of the proposed approach are compared with the results of other fusion approaches. The comparison demonstrates the superiority of the proposed approach. All the tests are performed on a large publically available dataset.

Automatic speaker verification (ASV) is the process of automatically verifying the identity of the claimant speaker using his voice biometric. In this paper, we have made an attempt to investigate the effect of conventional short-term features on the performance of speaker verification system in the presence of Mimicry attacks. We have prepared a small dataset for some popular cartoon characters. Initial results show that the best false acceptance rate (FAR) achieved is 22.90, with the use of stand-alone linear predictive coding (LPC) features.

Due to miniaturization of hardware and availability of low-cost, low-power sensors, Wireless Sensor Network and Multimedia Sensor Network applications are increasing day by day. Each application has a specific quality of service and experience requirements. The design of routing and MAC protocol which can fulfill the requirements of the application is challenging given the constrained nature of these devices. Considerable efforts are directed towards the design of energy efficient QoS-aware routing protocols. In this article, we present state of the art review of routing protocols for Wireless Multimedia Sensor Networks while addressing the challenges and providing insight into research issues.

Collaborative Filtering (CF) is one of the most effective approaches to engineer recommendation systems. It recommends those items to user which other users with related preferences and tastes liked in the past. User-based and Item-based Collaborative Filtering (IbCF) are two flavours of collaborative filtering. Both of these methods are used to estimate target user’s rating for the target item. In this paper, these methods are implemented and their performance is evaluated on the large dataset. The major attention of this paper is on exploring different ways in which predictions from UbCF and IbCF can be combined to minimize overall prediction error. Predictions from UbCF and IbCF are combined through simple and weighted averaging and performance of these fusion approaches is compared with the performance of UbCf & IbCF when implemented individually. Results are encouraging and demonstrate usefulness of fusion approaches.

Internet is now being used as content distribution network also. Internet users are interested in specific contents rather than host machines where the content is located. Named Data Networking (NDN) is a step towards future Internet architecture that would be based on named data rather than numerically identified hosts. Many projects are in progress to architect the structure of the future Internet. It is envisaged that NDN would provide functional efficiency with named content applied as the core concept. NDN offers lot of research opportunities to contribute for design of future Internet architecture. In this paper we analyze the modern Internet Protocol (IP) based host- centric architecture and explore the newer scalable and more efficient architecture, based on content-centric approach. Open research issues in various area of NDN are also addressed in the paper.

Video surveillance systems is a key component of any security system. Making an intelligent system that can detect and track multiple moving objects from video and also deals with dynamic backgrounds, illumination problem and environment conditions is a challenging task. The proposed system is designed for real-time vehicle detection and classification. The traffic is increasing day by day due to increase in number of vehicles. Vehicle detection, classification, and counting is a very important application by which highway monitoring, traffic planning, analysis of the traffic flow, etc. can be easily done. In this paper, vehicle detection is done by background subtraction and from each detected vehicles Scale-Invariant Feature Transform (SIFT) features is extracted. Vehicles are classified using the neural network and Support Vector Machine (SVM). SVM showed better generalization than Artificial Neural Networks.

Wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. The efficient utilization of energy source in a sensor node is very important criteria to prolong the life time of wireless sensor network. Wireless sensor networks have explored to many new protocols specifically designed for sensor networks where energy consideration is very crucial. Most of importance, given to hierarchical routing protocols based on clustering has better scalability. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. There are several energy efficient hierarchical routing protocols among this LEACH is famous protocol, we have simulated LEACH in NS2 and analyzed performance of LEACH in terms of energy, throughput and lifetime. Keywords : LEACH, Hierarchical Routing Algorithms, clustering, wireless sensor networks.

Wireless sensor network (WSN) is a network of small, low power, resource constrained devices distributed in a large area. The principle focus of such network is to monitor some well-defined phenomenon in the sensor field. Availability of low power CMOS camera has offered wings to WSN applications. Broadening the range of applications from monitoring to multimedia surveillance. Multimedia transmission brings newer challenges primarily provisioning of the quality of service. Realtime video surveillance is one class of application which expects timely delivery of the packets. QoS aware routing protocols are required to cater to this demand. Geographic routing protocols are most suited WSN due to its scalability. GPSR, SPEED, and MMSPEED are the protocols which explore 1-hop neighborhood information in packet forwarding. Most of these protocols stations their routing decision on the location of sink and ignoring energy reserves of a node, which is crucial in each WSN application. A geographic routing protocol is proposed in this paper, which is energy aware. Also rather than sticking to maintaining 1-hop neighborhood information, it maintains n-hop neighborhood which helps early detection of void.

Use of camera sensors has increased the scope of applications visualized by Wireless Sensor Networks (WSN). Video sensors increases the clarity of capture and reduces false alarm rate. Continuous capture and transmission of video streams can be highly energy consuming task given the resource constrained nature of sensor nodes. Also transmission of video stream demands high bandwidth. In applications like border patrolling, in event of intrusion, the system is expected to report the event. High deployment density of nodes results in larger overlap across the area covered by various nodes. Transmission of video streams by all video sensors detecting the intrusion can result in redundant streams, thus increasing energy consumption. Video sensors can vary their capture quality as a function of their backup nodes. Given the directional coverage of video sensor nodes, it becomes a challenging task to identify the set of backup nodes covering the same area. In this paper we investigate existing techniques for backup set computation and suggest a dynamic sized backup set computation method which is efficient and computes minimum sized backup set, minimizing the number of active nodes in the network.

The wireless sensor networks applications are supposed to solve the conflict of energy efficient communication and should achieve the desired QoS like communication delay. To overcome this challenge, we propose Power Adaptive SPEED (PASPEED) routing protocol which is capable of achieving application specific delays at low energy cost and can dynamically adapt routing decisions. PASPEED is location aware, stateless, localized routing protocol with minimal control overhead. PASPEED supports three features: (1) adaptive power control to minimize energy consumption (2) providing end-to- end delay guarantee (3) hole bypassing. It meets soft real-time deadline by maintaining a desired delivery speed across the sensor network. It chooses the most energy efficient node from possible next hop nodes which can provide the required delivery speed. Simulation results shows that PASPEED achieves better deadline miss ratio and video quality as compared with other existing protocols.

Due to large size of images involved in WMSN, its transmission is very demanding and energy consuming. One of the possible way to reduce size of the image to be transmitted is to aggregate the image before transmission. Image aggregation in WMSN transmits the overlapped region of the images only once by eliminating the redundant portion of image. Image aggregation scheme consumes only 46 percentage of the energy consumption compared to non aggregation schemes. The objective of this paper is to design efficient communication protocol for WMSNs by leveraging the correlation of visual information of camera sensors. To describe the degree of correlation of visual information observed by cameras with overlapped field of views a spatial correlation coefficient is derived.

The IEEE802.11p is a draft standard intended to support wireless access in the vehicular environment (WAVE). IEEE802.11p offers the connectivity to the highly mobile vehicular network. The IEEE802.11p standard has introduced the multichannel concept but it does not specify the mechanism for channel access and handoff process for V2I (Vehicle-to-Infrastructure) communication. We present handoff process in vehicular communications by adopting IEEE 802.11p based communication systems. This research mainly focuses on reducing the total time taken by handoff and support seamless handoff process for application demanding the real-time services. The proposed scheme reduces the control messages latency and uses the multi-channel operation of IEEE802.11p that has optimized handoff process. The Simulation result shows that the time taken by handoff and delay regarding control message is reduced. Simulation results show best performance of the proposed scheme compared to existing scheme.

Data mining has been an active area of research for the past couple of decades. Classification is an important data mining technique that consists of assigning a data instance to one of the several predefined categories. Various successful methods have been suggested and tested to solve the problem in the binary classification case. However, the multiclass classification has been attempted by only few researchers. The objective of this paper is to investigate various techniques for solving the multiclass classification problem. Three non-evolutionary and one evolutionary algorithm are compared on four datasets. Further, using this analysis, the paper presents the benefits of producing a hybrid classifier by combining evolutionary and non-evolutionary algorithms; specifically, by merging Genetic Programming and Decision Tree.