Ramasamy Saravanakumar

25 papers B 3Journal 14Unranked 8
YearRankTypeTitle / Venue / Authors
2022 J jnl
Neural Process. Lett.
Ramasamy Saravanakumar, M. Syed Ali
2022 J jnl
IEEE Control. Syst. Lett.
Ramasamy Saravanakumar
2022 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Ramasamy Saravanakumar, Amir Amini, Rupak Datta, Yang Cao
2021 conf
ISCMI
Rupak Datta, Ramasamy Saravanakumar
2021 conf
ISCMI
Ramasamy Saravanakumar
2021 J jnl
Inf. Sci.
Rupak Datta, Ramasamy Saravanakumar, Rajeeb Dey, Baby Bhattacharya, Choon Ki Ahn
2021 J jnl
Inf. Sci.
Ali Kazemy, Ramasamy Saravanakumar, James Lam
2020 B conf
SMC
Zhouning Du, Hiroaki Mukaidani, Ramasamy Saravanakumar
2020 B conf
SMC
Ramasamy Saravanakumar, Hiroaki Mukaidani
2020 J jnl
Neurocomputing
Ramasamy Saravanakumar, Hiroaki Mukaidani, Muthukumar Palanisamy
2020 J jnl
J. Intell. Fuzzy Syst.
Rupak Datta, Rajeeb Dey, Ramasamy Saravanakumar, Baby Bhattacharya, Tsung-Chih Lin
2020 conf
SICE
Hiroya Kikuchi, Hiroaki Mukaidani, Ramasamy Saravanakumar
2020 B conf
SMC
Hiroya Kikuchi, Hiroaki Mukaidani, Ramasamy Saravanakumar, Weihua Zhuang
2020 J jnl
Inf. Sci.
Rupak Datta, Rajeeb Dey, Baby Bhattacharya, Ramasamy Saravanakumar, Oh-Min Kwon
2020 J jnl
IEEE Control. Syst. Lett.
Hiroaki Mukaidani, Ramasamy Saravanakumar, Hua Xu, Weihua Zhuang
2019 J jnl
Int. J. Syst. Sci.
Nallappan Gunasekaran, Ramasamy Saravanakumar, M. Syed Ali, Quanxin Zhu
2019 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Ramasamy Saravanakumar, Sreten B. Stojanovic, Damnjan D. Radosavljevic, Choon Ki Ahn, Hamid Reza Karimi
2019 J jnl
Fuzzy Sets Syst.
Nallappan Gunasekaran, Ramasamy Saravanakumar, Young Hoon Joo, Han Sol Kim
2019 conf
SICE
Muneomi Sagara, Hiroaki Mukaidani, Ramasamy Saravanakumar, Hua Xu
2019 conf
ASCC
Hiroaki Mukaidani, Ramasamy Saravanakumar, Hua Xu
2019 conf
SICE
Ramasamy Saravanakumar, Ali Kazemy, Hiroaki Mukaidani
2019 conf
CDC
Hiroaki Mukaidani, Ramasamy Saravanakumar, Hua Xu, Weihua Zhuang
2019 conf
SICE
Hiroaki Mukaidani, Ramasamy Saravanakumar, Hua Xu, Muneomi Sagara
2019 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Ramasamy Saravanakumar, Hyung Soo Kang, Choon Ki Ahn, Xiaojie Su, Hamid Reza Karimi
2017 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Ramasamy Saravanakumar, Muhammed Syed Ali, Choon Ki Ahn, Hamid Reza Karimi, Peng Shi
redb/extractors/decompiler/bninja/analysis/scores.py
← Index redb/extractors/decompiler/bninja/analysis/scores.py python
from collections import deque
from binaryninja import highlevelil
from binaryninja.enums import HighLevelILOperation


class ObfuscationScores:
    def __init__(self, hlil_function):
        self.function = hlil_function
        self._basic_blocks = list(hlil_function.basic_blocks) if hlil_function and hlil_function.basic_blocks else []
        self._block_count = len(self._basic_blocks)

    def flattened_score(self):
        """
        A heuristic for detecting control flow flattening from Tim Blazytko.
        Source: https://www.synthesis.to/2021/03/03/flattening_detection.html
        """
        if self._block_count == 0:
            return 0.0

        max_flattening_ratio = 0.0

        for basic_block in self._basic_blocks:
            dominated = get_dominated_by(basic_block)
            if not any(edge.source in dominated for edge in basic_block.incoming_edges):
                continue
            ratio = len(dominated) / self._block_count
            if ratio > max_flattening_ratio:
                max_flattening_ratio = ratio

        return max_flattening_ratio

    def MBA_score(self):
        """
        Score for MBA is obtained by the number of instructions that have at least one arithmetic operation and
        one logic operation DIVIDED by the number of instructions.
        """
        total = 0
        mba_count = 0

        for ins in self.function.instructions:
            total += 1
            if uses_mba(ins):
                mba_count += 1

        if total == 0:
            return 0.0

        return mba_count / total

def get_dominated_by(dominator):
    """
    Get the dominators that are dominated by the given dominator.
    (To recall the theory, a basic block B is called dominator for A if every path from START
    to A must include B)
    """
    result = set()
    worklist = deque([dominator])

    while worklist:
        block = worklist.popleft()
        if block in result:
            continue
        result.add(block)
        worklist.extend(block.dominator_tree_children)

    return result

_ARITHMETIC_OPS = frozenset({
    HighLevelILOperation.HLIL_ADD,
    HighLevelILOperation.HLIL_NEG,
    HighLevelILOperation.HLIL_SUB,
    HighLevelILOperation.HLIL_MUL,
    HighLevelILOperation.HLIL_DIVS,
    HighLevelILOperation.HLIL_MODS,
})

_LOGIC_OPS = frozenset({
    HighLevelILOperation.HLIL_NOT,
    HighLevelILOperation.HLIL_AND,
    HighLevelILOperation.HLIL_OR,
    HighLevelILOperation.HLIL_XOR,
    HighLevelILOperation.HLIL_LSR,
    HighLevelILOperation.HLIL_LSL,
})

_MBA_OPS = _ARITHMETIC_OPS | _LOGIC_OPS

def uses_mba(hlil_instruction):
    uses_logic = False
    uses_arithmetic = False
    stack = [hlil_instruction]

    while stack:
        instruction = stack.pop()

        if not isinstance(instruction, highlevelil.HighLevelILInstruction):
            continue

        op = instruction.operation

        if op not in _MBA_OPS:
            for operand in instruction.operands:
                if isinstance(operand, highlevelil.HighLevelILInstruction):
                    stack.append(operand)
            continue

        if op in _ARITHMETIC_OPS:
            uses_arithmetic = True
        else:
            uses_logic = True

        if uses_logic and uses_arithmetic:
            return True

        for operand in instruction.operands:
            if isinstance(operand, highlevelil.HighLevelILInstruction):
                stack.append(operand)

    return False