def midas(df, num_rows, num_buckets):
m = df.src.max()
cur_count = Edgehash(num_rows, num_buckets, m)
total_count = Edgehash(num_rows, num_buckets, m)
anom_score = []
timestamp_keys = df.groupby(["timestamp"]).groups.keys()
for timeframe in tqdm(timestamp_keys):
cur_t = timeframe
curr_df = df.groupby(["timestamp"]).get_group(timeframe)
curr_df.apply(lambda row: getRowInfo(row, anom_score,cur_count,total_count,cur_t), axis=1)
cur_count.clear()
return anom_score
def midasR(src, dst, times, num_rows, num_buckets, factor):
m = np.max(src)
num_entries = src.shape[0]
cur_count = Edgehash(num_rows, num_buckets, m)
total_count = Edgehash(num_rows, num_buckets, m)
src_score = Nodehash(num_rows, num_buckets)
dst_score = Nodehash(num_rows, num_buckets)
src_total = Nodehash(num_rows, num_buckets)
dst_total = Nodehash(num_rows, num_buckets)
anom_score = np.zeros(num_entries)
cur_t = 1
for i in range(num_entries):
if i == 0 or times[i] > cur_t:
cur_count.lower(factor)
src_score.lower(factor)
dst_score.lower(factor)
cur_t = times[i]
cur_src = src[i]
cur_dst = dst[i]
cur_count.insert(cur_src, cur_dst, 1)
total_count.insert(cur_src, cur_dst, 1)
src_score.insert(cur_src, 1)
dst_score.insert(cur_dst, 1)
src_total.insert(cur_src, 1)
dst_total.insert(cur_dst, 1)
cur_score = counts_to_anom(
total_count.get_count(cur_src, cur_dst),
cur_count.get_count(cur_src, cur_dst),
cur_t,
)
cur_score_src = counts_to_anom(
src_total.get_count(cur_src), src_score.get_count(cur_src), cur_t
)
cur_score_dst = counts_to_anom(
dst_total.get_count(cur_dst), dst_score.get_count(cur_dst), cur_t
)
combined_score = max(cur_score_src, cur_score_dst, cur_score)
anom_score[i] = np.log(1 + combined_score)
return anom_score
def midas(src, dst, times, num_rows, num_buckets):
m = np.max(src)
cur_count = Edgehash(num_rows, num_buckets, m)
total_count = Edgehash(num_rows, num_buckets, m)
anom_score = np.zeros(src.shape[0])
cur_t = 1
for i in range(src.shape[0]):
if i == 0 or times[i] > cur_t:
cur_count.clear()
cur_t = times[i]
cur_src = src[i]
cur_dst = dst[i]
cur_count.insert(cur_src, cur_dst, 1)
total_count.insert(cur_src, cur_dst, 1)
cur_mean = total_count.get_count(cur_src, cur_dst) / cur_t
sqerr = np.power(cur_count.get_count(cur_src, cur_dst) - cur_mean, 2)
cur_score = 0 if cur_t == 1 else sqerr / cur_mean + sqerr / (
cur_mean * (cur_t - 1))
cur_score = 0 if math.isnan(cur_score) else cur_score
anom_score[i] = cur_score
return anom_score
def midas(df, num_rows, num_buckets):
m = df.src.max()
cur_count = Edgehash(num_rows, num_buckets, m)
total_count = Edgehash(num_rows, num_buckets, m)
anom_score = []
timestamp_keys = df.groupby(["timestamp"]).groups.keys()
for timeframe in timestamp_keys:
cur_t = timeframe
curr_df = df.groupby(["timestamp"]).get_group(timeframe)
for row in curr_df.itertuples():
cur_src = row.src
cur_dst = row.dst
cur_count.insert(cur_src, cur_dst, 1)
total_count.insert(cur_src, cur_dst, 1)
cur_mean = total_count.get_count(cur_src, cur_dst) / cur_t
sqerr = np.power(
cur_count.get_count(cur_src, cur_dst) - cur_mean, 2)
cur_score = 0 if cur_t == 1 else sqerr / cur_mean + sqerr / (
cur_mean * (cur_t - 1))
cur_score = 0 if math.isnan(cur_score) else cur_score
anom_score.append(cur_score)
cur_count.clear()
return anom_score