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P N, Renjith, K, Ramesh.  2020.  Trust based Security framework for IoT data. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). :1–5.
With an incredible growth in MEMS and Internet, IoT has developed to an inevitable invention and resource for human needs. IoT reframes the communication and created a new way of machine to machine communication. IoT utilizes smart sensor to monitor and track environmental changes in any area of interest. The high volume of sensed information is processed, formulated and presented to the user for decision making. In this paper a model is designed to perform trust evaluation and data aggregation with confidential transmission of secured information in to the network and enables higher secure and reliable data transmission for effective analysis and decision making. The Sensors in IoT devices, senses the same information and forwards redundant data in to the network. This results in higher network congestion and causes transmission overhead. This could be control by introducing data aggregation. A gateway sensor node can act as aggregator and a forward unique information to the base station. However, when the network is adulterated with malicious node, these malicious nodes tend to injects false data in to the network. In this paper, a trust based malicious node detection technique has been introduced to isolate the malicious node from forwarding false information into the network. Simulation results proves the proposed protocol can be used to reduce malicious attack with increased throughput and performance.
P, Charitha Reddy, K, SaiTulasi, J, Anuja T, R, Rajarajeswari, Mohan, Navya.  2021.  Automatic Test Pattern Generation of Multiple stuck-at faults using Test Patterns of Single stuck-at faults. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). :71–75.
The fabricated circuitries are getting massive and denser with every passing year due to which a normal automatic test pattern generation technique to detect only the single stuck-at faults will overlook the multiple stuck-at faults. But generating test patterns that can detect all possible multiple stuck-at fault is practically not possible. Hence, this paper proposes a method, where multiple faults can be detected by using test vectors for detecting single stuck-at faults. Here, the patterns for detecting single faults are generated and their ability to detect multiple stuck-at faults is also analyzed. From the experimental results it was observed that, the generated vectors for single faults cover maximum number of the multiple faults and then new test vectors are generated for the undetermined faults. The generated vectors are optimized for the compact test patterns in order to reduce the test power.
P, Dayananda, Subramanian, Siddharth, Suresh, Vijayalakshmi, Shivalli, Rishab, Sinha, Shrinkhla.  2022.  Video Compression using Deep Neural Networks. 2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP). :1–5.

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.

P, Rahoof P., Nair, L. R., P, Thafasal Ijyas V..  2017.  Trust Structure in Public Key Infrastructures. 2017 2nd International Conference on Anti-Cyber Crimes (ICACC). :223–227.

Recently perceived vulnerabilities in public key infrastructures (PKI) demand that a semantic or cognitive definition of trust is essential for augmenting the security through trust formulations. In this paper, we examine the meaning of trust in PKIs. Properly categorized trust can help in developing intelligent algorithms that can adapt to the security and privacy requirements of the clients. We delineate the different types of trust in a generic PKI model.

P. Dahake, S. Nimbhorkar.  2015.  "Hybrid cryptosystem for maintaining image integrity using biometric fingerprint". 2015 International Conference on Pervasive Computing (ICPC). :1-5.

Integrity of image data plays an important role in data communication. Image data contain confidential information so it is very important to protect data from intruder. When data is transmitted through the network, there may be possibility that data may be get lost or damaged. Existing system does not provide all functionality for securing image during transmission. i.e image compression, encryption and user authentication. In this paper hybrid cryptosystem is proposed in which biometric fingerprint is used for key generation which is further useful for encryption purpose. Secret fragment visible mosaic image method is used for secure transmission of image. For reducing the size of image lossless compression technique is used which leads to the fast transmission of image data through transmission channel. The biometric fingerprint is useful for authentication purpose. Biometric method is more secure method of authentication because it requires physical presence of human being and it is untraceable.

P. Das, S. C. Kushwaha, M. Chakraborty.  2015.  "Multiple embedding secret key image steganography using LSB substitution and Arnold Transform". 2015 2nd International Conference on Electronics and Communication Systems (ICECS). :845-849.

Cryptography and steganography are the two major fields available for data security. While cryptography is a technique in which the information is scrambled in an unintelligent gibberish fashion during transmission, steganography focuses on concealing the existence of the information. Combining both domains gives a higher level of security in which even if the use of covert channel is revealed, the true information will not be exposed. This paper focuses on concealing multiple secret images in a single 24-bit cover image using LSB substitution based image steganography. Each secret image is encrypted before hiding in the cover image using Arnold Transform. Results reveal that the proposed method successfully secures the high capacity data keeping the visual quality of transmitted image satisfactory.

P. Hu, H. Li, H. Fu, D. Cansever, P. Mohapatra.  2015.  "Dynamic defense strategy against advanced persistent threat with insiders". 2015 IEEE Conference on Computer Communications (INFOCOM). :747-755.

The landscape of cyber security has been reformed dramatically by the recently emerging Advanced Persistent Threat (APT). It is uniquely featured by the stealthy, continuous, sophisticated and well-funded attack process for long-term malicious gain, which render the current defense mechanisms inapplicable. A novel design of defense strategy, continuously combating APT in a long time-span with imperfect/incomplete information on attacker's actions, is urgently needed. The challenge is even more escalated when APT is coupled with the insider threat (a major threat in cyber-security), where insiders could trade valuable information to APT attacker for monetary gains. The interplay among the defender, APT attacker and insiders should be judiciously studied to shed insights on a more secure defense system. In this paper, we consider the joint threats from APT attacker and the insiders, and characterize the fore-mentioned interplay as a two-layer game model, i.e., a defense/attack game between defender and APT attacker and an information-trading game among insiders. Through rigorous analysis, we identify the best response strategies for each player and prove the existence of Nash Equilibrium for both games. Extensive numerical study further verifies our analytic results and examines the impact of different system configurations on the achievable security level.

P. Jain, S. Nandanwar.  2015.  "Securing the Clustered Database Using Data Modification Technique". 2015 International Conference on Computational Intelligence and Communication Networks (CICN). :1163-1166.

The new era of information communication and technology (ICT), everyone wants to store/share their Data or information in online media, like in cloud database, mobile database, grid database, drives etc. When the data is stored in online media the main problem is arises related to data is privacy because different types of hacker, attacker or crackers wants to disclose their private information as publically. Security is a continuous process of protecting the data or information from attacks. For securing that information from those kinds of unauthorized people we proposed and implement of one the technique based on the data modification concept with taking the iris database on weka tool. And this paper provides the high privacy in distributed clustered database environments.

P.G., Swathi, Rajesh, Sreeja.  2018.  Double Encryption Using TEA and DNA. 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). :1-5.
Information security has become a major challenge in data transmission. Data transmitted through the network is vulnerable to many passive and active attacks. Cryptographic algorithms provide security against the data intruders and provide secure network communication. In this method, two algorithms TEA and DNA are combined to form a new algorithm called DETD (Double Encryption using TEA and DNA). The algorithm mainly deals with encryption and decryption time of a given input text. Here, both the encryption and decryption time are compared with the other two algorithms and the results are recorded. This algorithm also aims to provide data security by increasing the levels of encryption.
Pachaghare, S., Patil, P..  2020.  Improving Authentication and Data Sharing Capabilities of Cloud using a Fusion of Kerberos and TTL-based Group Sharing. 2020 5th International Conference on Communication and Electronics Systems (ICCES). :1401—1405.
Cloud security has been of utmost concern for researchers and cloud deployers since the inception of cloud computing. Methods like PKI, hashing, encryption, etc. have proven themselves useful throughout cloud technology development, but they are not considered as a complete security solution for all kinds of cloud authentications. Moreover, data sharing in the cloud has also become a question of research due to the abundant use of data storage available on the cloud. To solve these issues, a Kerberos-based time-to-live (TTL) inspired data sharing and authentication mechanism is proposed on the cloud. The algorithm combines the two algorithms and provides a better cloud deployment infrastructure. It uses state-of-the-art elliptic curve cryptography along with a secure hashing algorithm (SHA 256) for authentication, and group-based time-to-live data sharing to evaluate the file-sharing status for the users. The result evaluates the system under different authentication attacks, and it is observed that the system is efficient under any kind of attack and any kind of file sharing process.
Pacheco, J., Zhu, X., Badr, Y., Hariri, S..  2017.  Enabling Risk Management for Smart Infrastructures with an Anomaly Behavior Analysis Intrusion Detection System. 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). :324–328.

The Internet of Things (IoT) connects not only computers and mobile devices, but it also interconnects smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, IoT applications introduce grand security challenges due to the increase in the attack surface. Current security approaches do not handle cybersecurity from a holistic point of view; hence a systematic cybersecurity mechanism needs to be adopted when designing IoTbased applications. In this work, we present a risk management framework to deploy secure IoT-based applications for Smart Infrastructures at the design time and the runtime. At the design time, we propose a risk management method that is appropriate for smart infrastructures. At the design time, our framework relies on the Anomaly Behavior Analysis (ABA) methodology enabled by the Autonomic Computing paradigm and an intrusion detection system to detect any threat that can compromise IoT infrastructures by. Our preliminary experimental results show that our framework can be used to detect threats and protect IoT premises and services.

Pacífico, Racyus D. G., Castanho, Matheus S., Vieira, Luiz F. M., Vieira, Marcos A. M., Duarte, Lucas F. S., Nacif, José A. M..  2021.  Application Layer Packet Classifier in Hardware. 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM). :515–522.
Traffic classification is fundamental to network operators to manage the network better. L7 classification and Deep Packet Inspection (DPI) using regular expressions are vital components to provide application-aware traffic classification. Nevertheless, there are open challenges yet, such as programmability and performance combined with security. In this paper, we introduce eBPFlow, a fast application layer packet classifier in hardware. eBPFlow allows packet classification with DPI on packet headers and payloads in runtime. It enables programming of regular expressions (RegEx) and security protocols using eBPF (extended Berkeley Packet Filter). We built eBPFlow on NetFPGA SUME 40 Gbps and created several application classifiers. The tests were performed in a physical testbed. Our results show that eBPFlow supports packet classification on the application layer with line rate. It only consumes 22 W.
Padala, S. K., D'Souza, J..  2020.  Performance of Spatially Coupled LDPC Codes over Underwater Acoustic Communication Channel. 2020 National Conference on Communications (NCC). :1–5.
Underwater acoustic (UWA) channel is complex because of its multipath environment, Doppler shift and rapidly changing characteristics. Many of the UWA communication- based applications demand high data rates and reliable communication. The orthogonal frequency division multiplexing (OFDM) system is very effective in UWA channels and provides high data rate with low equalization complexity. It is a challenging task to achieve reliability over these channels. The low-density parity-check (LDPC) codes give a better error performance than turbo codes, for UWA channels. The spatially-coupled low-density parity-check (SC-LDPC) codes have been shown to have the capacity-achieving performance over terrestrial communication. In this paper, we have studied by simulation, the performance of protograph based SC-LDPC codes over shallow water acoustic environment with a communication range of 1000 m and channel bandwidth of 10 KHz. Our results show that SC-LDPC codes give 1 dB performance improvement over LDPC codes at a Bit Error Rate (BER) of 10-3 for the same latency constraints.
Padekar, Hitesh, Park, Younghee, Hu, Hongxin, Chang, Sang-Yoon.  2016.  Enabling Dynamic Access Control for Controller Applications in Software-Defined Networks. Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies. :51–61.

Recent findings have shown that network and system attacks in Software-Defined Networks (SDNs) have been caused by malicious network applications that misuse APIs in an SDN controller. Such attacks can both crash the controller and change the internal data structure in the controller, causing serious damage to the infrastructure of SDN-based networks. To address this critical security issue, we introduce a security framework called AEGIS to prevent controller APIs from being misused by malicious network applications. Through the run-time verification of API calls, AEGIS performs a fine-grained access control for important controller APIs that can be misused by malicious applications. The usage of API calls is verified in real time by sophisticated security access rules that are defined based on the relationships between applications and data in the SDN controller. We also present a prototypical implementation of AEGIS and demonstrate its effectiveness and efficiency by performing six different controller attacks including new attacks we have recently discovered.

Padma, Bh, Chandravathi, D, Pratibha, Lanka.  2021.  Defense Against Frequency Analysis In Elliptic Curve Cryptography Using K-Means Clustering. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). :64–69.
Elliptic Curve Cryptography (ECC) is a revolution in asymmetric key cryptography which is based on the hardness of discrete logarithms. ECC offers lightweight encryption as it presents equal security for smaller keys, and reduces processing overhead. But asymmetric schemes are vulnerable to several cryptographic attacks such as plaintext attacks, known cipher text attacks etc. Frequency analysis is a type of cipher text attack which is a passive traffic analysis scenario, where an opponent studies the frequency or occurrence of single letter or groups of letters in a cipher text to predict the plain text part. Block cipher modes are not used in asymmetric key encryption because encrypting many blocks with an asymmetric scheme is literally slow and CBC propagates transmission errors. Therefore, in this research we present a new approach to defence against frequency analysis in ECC using K-Means clustering to defence against Frequency Analysis. In this proposed methodology, security of ECC against frequency analysis is achieved by clustering the points of the curve and selecting different cluster for encoding a text each time it is encrypted. This technique destroys the regularities in the cipher text and thereby guards against cipher text attacks.
Padmanaban, R., Thirumaran, M., Sanjana, Victoria, Moshika, A..  2019.  Security Analytics For Heterogeneous Web. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). :1–6.

In recent days, Enterprises are expanding their business efficiently through web applications which has paved the way for building good consumer relationship with its customers. The major threat faced by these enterprises is their inability to provide secure environments as the web applications are prone to severe vulnerabilities. As a result of this, many security standards and tools have been evolving to handle the vulnerabilities. Though there are many vulnerability detection tools available in the present, they do not provide sufficient information on the attack. For the long-term functioning of an organization, data along with efficient analytics on the vulnerabilities is required to enhance its reliability. The proposed model thus aims to make use of Machine Learning with Analytics to solve the problem in hand. Hence, the sequence of the attack is detected through the pattern using PAA and further the detected vulnerabilities are classified using Machine Learning technique such as SVM. Probabilistic results are provided in order to obtain numerical data sets which could be used for obtaining a report on user and application behavior. Dynamic and Reconfigurable PAA with SVM Classifier is a challenging task to analyze the vulnerabilities and impact of these vulnerabilities in heterogeneous web environment. This will enhance the former processing by analysis of the origin and the pattern of the attack in a more effective manner. Hence, the proposed system is designed to perform detection of attacks. The system works on the mitigation and prevention as part of the attack prediction.

Padmapriya, S., Valli, R., Jayekumar, M..  2020.  Monitoring Algorithm in Malicious Vehicular Adhoc Networks. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—6.

Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

Padmashree, M G, Khanum, Shahela, Arunalatha, J S, Venugopal, K R.  2019.  SIRLC: Secure Information Retrieval using Lightweight Cryptography in HIoT. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :269–273.

Advances in new Communication and Information innovations has led to a new paradigm known as Internet of Things (IoT). Healthcare environment uses IoT technologies for Patients care which can be used in various medical applications. Patient information is encrypted consistently to maintain the access of therapeutic records by authoritative entities. Healthcare Internet of Things (HIoT) facilitate the access of Patient files immediately in emergency situations. In the proposed system, the Patient directly provides the Key to the Doctor in normal care access. In Emergency care, a Patient shares an Attribute based Key with a set of Emergency Supporting Representatives (ESRs) and access permission to the Doctor for utilizing Emergency key from ESR. The Doctor decrypts the medical records by using Attribute based key and Emergency key to save the Patient's life. The proposed model Secure Information Retrieval using Lightweight Cryptography (SIRLC) reduces the secret key generation time and cipher text size. The performance evaluation indicates that SIRLC is a better option to utilize in Healthcare IoT than Lightweight Break-glass Access Control(LiBAC) with enhanced security and reduced computational complexity.

Padmashree, M G, Arunalatha, J S, Venugopal, K R.  2019.  HSSM: High Speed Split Multiplier for Elliptic Curve Cryptography in IoT. 2019 Fifteenth International Conference on Information Processing (ICINPRO). :1—5.

Security of data in the Internet of Things (IoT) deals with Encryption to provide a stable secure system. The IoT device possess a constrained Main Memory and Secondary Memory that mandates the use of Elliptic Curve Cryptographic (ECC) scheme. The Scalar Multiplication has a great impact on the ECC implementations in reducing the Computation and Space Complexity, thereby enhancing the performance of an IoT System providing high Security and Privacy. The proposed High Speed Split Multiplier (HSSM) for ECC in IoT is a lightweight Multiplication technique that uses Split Multiplication with Pseudo-Mersenne Prime Number and Montgomery Curve to withstand the Power Analysis Attack. The proposed algorithm reduces the Computation Time and the Space Complexity of the Cryptographic operations in terms of Clock cycles and RAM when compared with Liu et al.,’s multiplication algorithms [1].

Padmavathi, G., Shanmugapriya, D., Asha, S..  2022.  A Framework to Detect the Malicious Insider Threat in Cloud Environment using Supervised Learning Methods. 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). :354—358.
A malicious insider threat is more vulnerable to an organization. It is necessary to detect the malicious insider because of its huge impact to an organization. The occurrence of a malicious insider threat is less but quite destructive. So, the major focus of this paper is to detect the malicious insider threat in an organization. The traditional insider threat detection algorithm is not suitable for real time insider threat detection. A supervised learning-based anomaly detection technique is used to classify, predict and detect the malicious and non-malicious activity based on highest level of anomaly score. In this paper, a framework is proposed to detect the malicious insider threat using supervised learning-based anomaly detection. It is used to detect the malicious insider threat activity using One-Class Support Vector Machine (OCSVM). The experimental results shows that the proposed framework using OCSVM performs well and detects the malicious insider who obtain huge anomaly score than a normal user.
Padon, Oded.  2018.  Deductive Verification of Distributed Protocols in First-Order Logic. 2018 Formal Methods in Computer Aided Design (FMCAD). :1-1.

Formal verification of infinite-state systems, and distributed systems in particular, is a long standing research goal. In the deductive verification approach, the programmer provides inductive invariants and pre/post specifications of procedures, reducing the verification problem to checking validity of logical verification conditions. This check is often performed by automated theorem provers and SMT solvers, substantially increasing productivity in the verification of complex systems. However, the unpredictability of automated provers presents a major hurdle to usability of these tools. This problem is particularly acute in case of provers that handle undecidable logics, for example, first-order logic with quantifiers and theories such as arithmetic. The resulting extreme sensitivity to minor changes has a strong negative impact on the convergence of the overall proof effort.

Padon, Oded, Immerman, Neil, Shoham, Sharon, Karbyshev, Aleksandr, Sagiv, Mooly.  2016.  Decidability of Inferring Inductive Invariants. Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. :217–231.

Induction is a successful approach for verification of hardware and software systems. A common practice is to model a system using logical formulas, and then use a decision procedure to verify that some logical formula is an inductive safety invariant for the system. A key ingredient in this approach is coming up with the inductive invariant, which is known as invariant inference. This is a major difficulty, and it is often left for humans or addressed by sound but incomplete abstract interpretation. This paper is motivated by the problem of inductive invariants in shape analysis and in distributed protocols. This paper approaches the general problem of inferring first-order inductive invariants by restricting the language L of candidate invariants. Notice that the problem of invariant inference in a restricted language L differs from the safety problem, since a system may be safe and still not have any inductive invariant in L that proves safety. Clearly, if L is finite (and if testing an inductive invariant is decidable), then inferring invariants in L is decidable. This paper presents some interesting cases when inferring inductive invariants in L is decidable even when L is an infinite language of universal formulas. Decidability is obtained by restricting L and defining a suitable well-quasi-order on the state space. We also present some undecidability results that show that our restrictions are necessary. We further present a framework for systematically constructing infinite languages while keeping the invariant inference problem decidable. We illustrate our approach by showing the decidability of inferring invariants for programs manipulating linked-lists, and for distributed protocols.

Pagán, Alexander, Elleithy, Khaled.  2021.  A Multi-Layered Defense Approach to Safeguard Against Ransomware. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). :0942–0947.
There has been a significant rise in ransomware attacks over the last few years. Cyber attackers have made use of tried and true ransomware viruses to target the government, health care, and educational institutions. Ransomware variants can be purchased on the dark web by amateurs giving them the same attack tools used by professional cyber attackers without experience or skill. Traditional antivirus and antimalware products have improved, but they alone fall short when it comes to catching and stopping ransomware attacks. Employee training has become one of the most important aspects of being prepared for attempted cyberattacks. However, training alone only goes so far; human error is still the main entry point for malware and ransomware infections. In this paper, we propose a multi-layered defense approach to safeguard against ransomware. We have come to the startling realization that it is not a matter of “if” your organization will be hit with ransomware, but “when” your organization will be hit with ransomware. If an organization is not adequately prepared for an attack or how to respond to an attack, the effects can be costly and devastating. Our approach proposes having innovative antimalware software on the local machines, properly configured firewalls, active DNS/Web filtering, email security, backups, and staff training. With the implementation of this layered defense, the attempt can be caught and stopped at multiple points in the event of an attempted ransomware attack. If the attack were successful, the layered defense provides the option for recovery of affected data without paying a ransom.
Page, Adam, Attaran, Nasrin, Shea, Colin, Homayoun, Houman, Mohsenin, Tinoosh.  2016.  Low-Power Manycore Accelerator for Personalized Biomedical Applications. Proceedings of the 26th Edition on Great Lakes Symposium on VLSI. :63–68.

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.

Paharia, B., Bhushan, K..  2018.  Fog Computing as a Defensive Approach Against Distributed Denial of Service (DDoS): A Proposed Architecture. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
Cloud computing is a long-established technique which deals with storage and processing of information. In cloud computing, any user is liable to pay and demand according to its particular needs. Due to various limitations of cloud computing like higher latency and more bandwidth consumptions for transmitted information, the origination of Fog computing was essential. Fog computing inherits all the advantages of cloud computing, and also brings computing at the network's edge. In addition, security is a very serious concern for cloud computing. In this paper, fog computing is used as a defensive approach from the day-to-day increasing security threats particularly DDoS attacks in cloud computing. Here an architecture has been proposed to obstruct the malicious traffic generated by the DDoS attack from user to the cloud by utilizing the benefits of fog computing. Fog functions as a filtering layer for the traffic generated and is placed between user and cloud. This paper primarily works to improve the overall performance of the network and enhances reduction in the traffic forwarded to the cloud.