Biblio

Internet of Things (IoT) have offered great opportunities for the growth of smart objects in the last decade. Smart devices are deployed in many fields such as smart cities, healthcare and agriculture. One of the applications of IoT is Wireless Sensor Networks (WSN) that require inexpensive and space-economic design for remote sensing and communication capabilities. This, unfortunately, lead to their inherent security vulnerabilities. Lightweight cryptography schemes are designed to counter many attacks in low-powered devices such as the IoT and WSN. These schemes can provide support for data encryption and key management while maintaining some level of efficiency. Most of these block ciphers provide good security. However, due to the complex cryptographic scheme's efficiency and optimisation is an issue. In this work, we focus on a new lightweight encryption scheme called the Klein block cipher. The algorithms of Klein block cipher are analysed for performance and security optimisations. A new algorithm which consists of 3-layer substitute box is proposed to reduce the need for resource consumption but maintain the security.

IoT devices are open to traditional control flow integrity (CFI) attacks resulting from buffer overflow and return-oriented programming like techniques. They often have limited computational capacity ruling out many of the traditional heavy-duty software countermeasures. In this work, we deploy hardware/software solutions to detect CFI attacks. Some of the medium capability IoT devices, for example based on Raspberry Pi, contain ARM Cortex A-53 (Pi 3) or Cortex A-73 (Pi 4) processors. These processors include hardware counters to count microarchitecture level events affecting performance. Lighter weight IoT devices, say based on ARM Cortex M4 or M7, include DWT (Debug, Watch & Trace) module. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. The proposed anomaly detection mechanism is evaluated on ArduPilot [1] - a popular autopilot software on a Raspberry Pi 3 with PMU and DWT. A self-navigation program is evaluated on an iCreate Roomba platform with an ARM Cortex M4 processor with DWT only. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per control flow anomaly check.

Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.

The Extended Triple Diffie-Hellman (X3DH) protocol has been used for years as the basis of secure communication establishment among parties (i.e, humans and devices) over the Internet. However, such a protocol has several limits. It is typically based on a single trust third-party server that represents a single point of failure (SPoF) being consequently exposed to well- known Distributed Denial of Service (DDOS) attacks. In order to address such a limit, several solutions have been proposed so far that are often cost expensive and difficult to be maintained. The objective of this paper is to propose a BlockChain-Based X3DH (BCB-X3DH) protocol that allows eliminating such a SPoF, also simplifying its maintenance. Specifically, it combines the well- known X3DH security mechanisms with the intrinsic features of data non-repudiation and immutability that are typical of Smart Contracts. Furthermore, different implementation approaches are discussed to suits both human-to-human and device-to-device scenarios. Experiments compared the performance of both X3DH and BCB-X3DH.

IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.

Lightweight cryptography is a new branch of cryptography focused on providing security to resource-constraint devices such as wireless sensor networks (WSN), Radio-Frequency Identification (RFIDs) and other embedded systems. The factors considered in lightweight cryptography are mainly circuit area, memory requirement, processing time, latency, power, and energy consumption. This paper presents a discussion on common lightweight block ciphers in terms of different performance parameters, strength, design trends, limitations, and applications including the National Institute of Science and Technology (NIST) round 1 and 2 candidates. Analysis of these lightweight algorithms has offered an insight into this newly emerging field of cryptography.

The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.

As the core infrastructure of cloud data operation, exchange and storage, data centerneeds to ensure its security and reliability, which are the important prerequisites for the development of cloud computing. Due to various illegal accesses, attacks, viruses and other security threats, it is necessary to protect the boundary of cloud data center through security gateway. Since the traffic growing up to gigabyte level, the secure gateway must ensure high transmission efficiency and different network services to support the cloud services. In addition, data center is gradually evolving from IPv4 to IPv6 due to excessive consumption of IP addresses. Packet classification algorithm, which can divide packets into different specific streams, is very important for QoS, real-time data stream application and firewall. Therefore, it is necessary to design a high performance IPv6 packet classification algorithm suitable for security gateway.AsIPv6 has a128-bitIP address and a different packet structure compared with IPv4, the traditional IPv4 packet classification algorithm is not suitable properly for IPv6 situations. This paper proposes a fast packet classification algorithm for IPv6 - PCHA (packet classification based on hash andAdelson-Velsky-Landis Tree). It adopts the three flow classification fields of source IPaddress(SA), destination IPaddress(DA) and flow label(FL) in the IPv6 packet defined by RFC3697 to implement fast three-tuple matching of IPv6 packet. It is through hash matching of variable length IPv6 address and tree matching of shorter flow label. Analysis and testing show that the algorithm has a time complexity close to O(1) in the acceptable range of space complexity, which meets the requirements of fast classification of IPv6 packetsand can adapt well to the changes in the size of rule sets, supporting fast preprocessing of rule sets. Our algorithm supports the storage of 500,000 3-tuple rules on the gateway device and can maintain 75% of the performance of throughput for small packets of 78 bytes.

Industrial Control Systems (ICS) have evolved in the last decade, shifting from proprietary software/hardware to contemporary embedded architectures paired with open-source operating systems. In contrast to the IT world, where continuous updates and patches are expected, decommissioning always-on ICS for security assessment can incur prohibitive costs to their owner. Thus, a solution for routinely assessing the cybersecurity posture of diverse ICS without affecting their operation is essential. Therefore, in this paper we introduce IFFSET, a platform that leverages full system emulation of Linux-based ICS firmware and utilizes fuzzing for security evaluation. Our platform extracts the file system and kernel information from a live ICS device, building an image which is emulated on a desktop system through QEMU. We employ fuzzing as a security assessment tool to analyze ICS specific libraries and find potential security threatening conditions. We test our platform with commercial PLCs, showcasing potential threats with no interruption to the control process.

In this paper, our objective is to focus on the recent trend of military fields where they brought Internet of Things (IoT) to have better impact on the battlefield by improving the effectiveness and this is called Internet of Battlefield Things(IoBT). Due to the requirements of high computing capability and minimum response time with minimum fault tolerance this paper proposed a decentralized IoBT architecture. The proposed method can increase the reliability in the battlefield environment by searching the reliable nodes among all the edge nodes in the environment, and by adding the fault tolerance in the edge nodes will increase the effectiveness of overall battlefield scenario. This suggested fault tolerance approach is worth for decentralized mode to handle the issue of latency requirements and maintaining the task reliability of the battlefield. Our experimental results ensure the effectiveness of the proposed approach as well as enjoy the requirements of latency-aware military field while ensuring the overall reliability of the network.

Internet of Things (IoT) has been growing exponentially in the commercial market in recent years. It is also a fact that people hold one or more computing devices at home. Many of them have been developed to operate through internet connectivity with cloud computing technologies that result in the demand for fast, robust, and secure services. In most cases, the lack of these services makes difficult the transfer of data to fulfill the devices' purposes. Under these conditions, an intermediate layer or middleware is needed to process, filter, and send data through a more efficient alternative. This paper presents the adaptive solution of a middleware architecture as an intermediate layer between smart devices and cloud computing to enhance the management of the heterogeneity of data provining from IoT devices. The proposed middleware provides easy configuration, adaptability, and bearability for different environments. Finally, this solution has been implemented in the healthcare domain, in which IoT solutions are deployed into Ambient Assisted Living (AAL) environments.

Attribute-based encryption received widespread attention as soon as it was proposed. However, due to its specific characteristics, some restrictions on attribute set are not flexible enough in actual operation. In addition, since access authorities are determined according to users' attributes, users sharing the same attributes are difficult to be distinguished. Once a malicious user makes illicit gains by their decryption authorities, it is difficult to track down specific user. This paper follows practical demands to propose a more flexible key-policy attribute-based encryption scheme with black-box traceability. The scheme has a constant size of public parameters which can be utilized to construct attribute-related parameters flexibly, and the method of traitor tracing in broadcast encryption is introduced to achieve effective malicious user tracing. In addition, the security and feasibility can be proved by the security proofs and performance evaluation in this paper.

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS.

One of the main security issues in telecare medecine information systems is the remote user authentication and key agreement between healthcare professionals and patient's medical sensors. Many of the proposed approaches are based on multiple factors (password, token and possibly biometrics). Two-factor authentication protocols do not resist to many possible attacks. As for three-factor authentication schemes, they usually come with high resource consumption. Since medical sensors have limited storage and computational capabilities, ensuring a minimal resources consumption becomes a major concern in this context. In this paper, we propose a secure and lightweight three-factor authentication and key generation scheme for securing communications between healtcare professional and patient's medical sensors. Thanks to formal verification, we prove that this scheme is robust enough against known possible attacks. A comparison with the most relevant related work's schemes shows that our protocol ensures an optimised resource consumption level.

Fog Computing is one of the edge computing paradigms that envisages being the proximate processing and storage infrastructure for a multitude of IoT appliances. With its dynamic deployability as a medium level cloud service, fog nodes are enabling heterogeneous service provisioning infrastructure that features scalability, interoperability, and adaptability. Out of the various 5G based services possible with the fog computing platforms, security services are imperative but minimally investigated direct live. Thus, in this research, we are focused on launching security services in a fog node with an architecture capable of provisioning on-demand service requests. As the fog nodes are constrained on resources, our intention is to integrate light-weight virtualization technology such as Docker for forming the service provisioning infrastructure. We managed to launch multiple security instances configured to be Intrusion Detection and Prevention Systems (IDPSs) on the fog infrastructure emulated via a Raspberry Pi-4 device. This environment was tested with multiple network flows to validate its feasibility. In our proposed architecture, orchestration strategies performed by the security orchestrator were stated as guidelines for achieving pragmatic, dynamic orchestration with fog in IoT deployments. The results of this research guarantee the possibility of developing an ambient security service model that facilitates IoT devices with enhanced security.

The most recent and important developments in the field of computer networks are cloud and fog computing. In this study, modern cloud computer networks comprising computers, internet of things (IoT), fog servers, and cloud servers for data transmission, is investigated. A cloud computer networks can be modeled as a network with nodes and arcs, in which each arc represents a transmission line, and each node represents an IoT device, a fog server, or a cloud server. Each transmission line has several possible capacities and is regarded as a multistate. The network is termed a multi-state cloud computer network (MCCN). this study firstly constructs the mathematic model to elucidate the flow relationship among the IoT devices, edge servers, and cloud servers and subsequently develop an algorithm to evaluate the performance of the MCCN by calculating network reliability which is defined as the probability of the data being successfully processed by the MCCN.

Federated learning (FL) is the rapidly developing machine learning technique that is used to perform collaborative model training over decentralized datasets. FL enables privacy-preserving model development whereby the datasets are scattered over a large set of data producers (i.e., devices and/or systems). These data producers train the learning models, encapsulate the model updates with differential privacy techniques, and share them to centralized systems for global aggregation. However, these centralized models are always prone to adversarial attacks (such as data-poisoning and model poisoning attacks) due to a large number of data producers. Hence, FL methods need to ensure fairness and high-quality model availability across all the participants in the underlying AI systems. In this paper, we propose a novel FL framework, called FairFed, to meet fairness and high-quality data requirements. The FairFed provides a fairness mechanism to detect adversaries across the devices and datasets in the FL network and reject their model updates. We use a Python-simulated FL framework to enable large-scale training over MNIST dataset. We simulate a cross-device model training settings to detect adversaries in the training network. We used TensorFlow Federated and Python to implement the fairness protocol, the deep neural network, and the outlier detection algorithm. We thoroughly test the proposed FairFed framework with random and uniform data distributions across the training network and compare our initial results with the baseline fairness scheme. Our proposed work shows promising results in terms of model accuracy and loss.

Large scale deployment of IoT devices has made Bluetooth Protocol (IEEE 802.15.1) the wireless protocol of choice for close-range communications. Devices such as keyboards, smartwatches, headphones, computer mouse, and various wearable connecting devices use Bluetooth network for communication. Moreover, Bluetooth networks are widely used in medical devices like heart monitors, blood glucose monitors, asthma inhalers, and pulse oximeters. Also, Bluetooth has replaced cables for wire-free equipment in a surgical environment. In hospitals, devices communicate with one another, sharing sensitive and critical information over Bluetooth scatter-networks. Thus, it is imperative to secure the Bluetooth networks against attacks like Man in the Middle attack (MITM), eavesdropping attacks, and Denial of Service (DoS) attacks. This paper presents a Multi-Level Bluetooth Intrusion Detection System (ML-BIDS) to detect malicious attacks against Bluetooth devices. In the ML-IDS framework, we perform continuous device identification and authorization in Bluetooth networks following the zero-trust principle [ref]. The ML-BIDS framework includes an anomaly-based intrusion detection system (ABIDS) to detect attacks on the Bluetooth protocol. The ABIDS tracks the normal behavior of the Bluetooth protocol by comparing it with the Bluetooth protocol state machine. Bluetooth frame flows consisting of Bluetooth frames received over 10 seconds are split into n-grams to track the current state of the protocol in the state machine. We evaluated the performance of several machine learning algorithms like C4.5, Adaboost, SVM, Naive Bayes, Jrip, and Bagging to classify normal Bluetooth protocol flows from abnormal Bluetooth protocol flows. The ABIDS detects attacks on Bluetooth protocols with a precision of up to 99.6% and recall up to 99.6%. The ML-BIDS framework also performs whitelisting of the devices on the Bluetooth network to prevent unauthorized devices from connecting to the network. ML-BIDS uses a combination of the Bluetooth Address, mac address, and IP address to uniquely identify a Bluetooth device connecting to the network, and hence ensuring only authorized devices can connect to the Bluetooth network.

Due to increased number of devices with limited resources in Internet of Things (IoT) has to serve time sensitive applications including health monitoring, emergency response, industrial applications and smart city etc. This has incurred the problem of solving the provisioning of limited computational resources of the devices to fulfill the requirement with reduced latency. With rapid increase of devices and heterogeneity characteristic the resource provisioning is crucial and leads to conflict of trusting among the devices requests. Trust is essential component in any context for communicating or sharing the resources in the network. The proposed work comprises of trusting and provisioning based on deadline. Trust quantity is measured with concept of game theory and optimal strategy decision among provider and customer and provision resources within deadline to execute the tasks is done by finding Nash equilibrium. Nash equilibrium (NE) is estimated by constructing the payoff matrix with choice of two player strategies. NE is obtained in the proposed work for the Trust- Respond (TR) strategy. The latency aware approach for avoiding resource contention due to limited resources of the edge devices, fog computing leverages the cloud services in a distributed way at the edge of the devices. The communication is established between edge devices-fog-cloud and provision of resources is performed based on scalar chain and Gang Plank theory of management to reduce latency and increase trust quantity. To test the performance of proposed work performance parameter considered are latency and computational time.

Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for E-Health system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP.

Due to the wide adoption of IoT/CPS systems, embedded devices (IoT frontends) become increasingly connected and mission-critical, which in turn has attracted advanced attacks (e.g., control-flow hijacks and data-only attacks). Unfortunately, IoT backends (e.g., remote controllers or in-cloud services) are unable to detect if such attacks have happened while receiving data, service requests, or operation status from IoT devices (remotely deployed embedded devices). As a result, currently, IoT backends are forced to blindly trust the IoT devices that they interact with.To fill this void, we first formulate a new security property for embedded devices, called "Operation Execution Integrity" or OEI. We then design and build a system, OAT, that enables remote OEI attestation for ARM-based bare-metal embedded devices. Our formulation of OEI captures the integrity of both control flow and critical data involved in an operation execution. Therefore, satisfying OEI entails that an operation execution is free of unexpected control and data manipulations, which existing attestation methods cannot check. Our design of OAT strikes a balance between prover's constraints (embedded devices' limited computing power and storage) and verifier's requirements (complete verifiability and forensic assistance). OAT uses a new control-flow measurement scheme, which enables lightweight and space-efficient collection of measurements (97% space reduction from the trace-based approach). OAT performs the remote control-flow verification through abstract execution, which is fast and deterministic. OAT also features lightweight integrity checking for critical data (74% less instrumentation needed than previous work). Our security analysis shows that OAT allows remote verifiers or IoT backends to detect both controlflow hijacks and data-only attacks that affect the execution of operations on IoT devices. In our evaluation using real embedded programs, OAT incurs a runtime overhead of 2.7%.

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data. This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities. Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems. Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state. Previous researchers have demonstrated independent methods for attack detection and safeguard. However, the majority of them lack in providing onboard system recovery and secure communication techniques. To bridge this gap, this manuscript proposes SRACARE - a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks. The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array (FPGA) board. This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery. The framework achieves these with only 8% performance overhead and a very small increase in hardware-software footprint.

Unlike traditional processors, Internet of Things (IoT) devices are short of resources to incorporate mature protections (e.g. MMU, TrustZone) against modern control-flow attacks. Remote (control-flow) attestation is fast becoming a key instrument in securing such devices as it has proven the effectiveness on not only detecting runtime malware infestation of a remote device, but also saving the computing resources by moving the costly verification process away. However, few control-flow attestation schemes have been able to draw on any systematic research into the software specificity of bare-metal systems, which are widely deployed on resource-constrained IoT devices. To our knowledge, the unique design patterns of the system limit implementations of such expositions. In this paper, we present the design and proof-of-concept implementation of LAPE, a lightweight attestation of program execution scheme that enables detecting control-flow attacks for bare-metal systems without requiring hardware modification. With rudimentary memory protection support found in modern IoT-class microcontrollers, LAPE leverages software instrumentation to compartmentalize the firmware functions into several ”attestation compartments”. It then continuously tracks the control-flow events of each compartment and periodically reports them to the verifier. The PoC of the scheme is incorporated into an LLVM-based compiler to generate the LAPE-enabled firmware. By taking experiments with several real-world IoT firmware, the results show both the efficiency and practicality of LAPE.

In recent years, we have seen an advent in software attestation defenses targeting embedded systems which aim to detect tampering with a device's running program. With a persistent threat of an increasingly powerful attacker with physical access to the device, attestation approaches have become more rooted into the device's hardware with some approaches even changing the underlying microarchitecture. These drastic changes to the hardware make the proposed defenses hard to apply to new systems. In this paper, we present and evaluate LAHEL as the means to study the implementation and pitfalls of a hardware-based attestation mechanism. We limit LAHEL to utilize existing technologies without demanding any hardware changes. We implement LAHEL as a hardware IP core which interfaces with the CoreSight Debug Architecture available in modern ARM cores. We show how LAHEL can be integrated to system on chip designs allowing for microcontroller vendors to easily add our defense into their products. We present and test our prototype on a Zynq-7000 SoC, evaluating the security of LAHEL against powerful time-of-check-time-of-use (TOCTOU) attacks, while demonstrating improved performance over existing attestation schemes.

In recent times cloud services are used widely and due to which there are so many attacks on the cloud devices. One of the major attacks is DDos (distributed denial-of-service) -attack which mainly targeted the Memcached which is a caching system developed for speeding the websites and the networks through Memcached's database. The DDoS attack tries to destroy the database by creating a flood of internet traffic at the targeted server end. Attackers send the spoofing applications to the vulnerable UDP Memcached server which even manipulate the legitimate identity of the sender. In this work, we have proposed a vector quantization approach based on a supervised deep learning approach to detect the Memcached attack performed by the use of malicious firmware on different types of Cloud attached devices. This vector quantization approach detects the DDoas attack performed by malicious firmware on the different types of cloud devices and this also classifies the applications which are vulnerable to attack based on cloud-The Hackbeased services. The result computed during the testing shows the 98.2 % as legally positive and 0.034% as falsely negative.